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diff --git a/config/IR&onnx_for_416_Petr_1/yoeo.bin b/config/IR&onnx_for_416_Petr_1/yoeo.bin
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diff --git a/config/IR&onnx_for_416_Petr_1/yoeo.mapping b/config/IR&onnx_for_416_Petr_1/yoeo.mapping
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diff --git a/config/IR&onnx_for_416_Petr_1/yoeo.onnx b/config/IR&onnx_for_416_Petr_1/yoeo.onnx
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diff --git a/config/IR&onnx_for_416_Petr_1/yoeo.xml b/config/IR&onnx_for_416_Petr_1/yoeo.xml
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diff --git a/config/IR&onnx_for_416_mine_759/yoeo_mine_759.bin b/config/IR&onnx_for_416_mine_759/yoeo_mine_759.bin
new file mode 100644
index 0000000..2a8a446
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diff --git a/config/IR&onnx_for_416_mine_759/yoeo_mine_759.mapping b/config/IR&onnx_for_416_mine_759/yoeo_mine_759.mapping
new file mode 100644
index 0000000..6519145
--- /dev/null
+++ b/config/IR&onnx_for_416_mine_759/yoeo_mine_759.mapping
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diff --git a/config/IR&onnx_for_416_mine_759/yoeo_mine_759.onnx b/config/IR&onnx_for_416_mine_759/yoeo_mine_759.onnx
new file mode 100644
index 0000000..bcfb8c5
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diff --git a/config/IR&onnx_for_416_mine_759/yoeo_mine_759.xml b/config/IR&onnx_for_416_mine_759/yoeo_mine_759.xml
new file mode 100644
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index 3cdc35b..0000000
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diff --git a/data/samples/sequences_jasper_kicking_euro_ball_00358.png b/data/samples/sequences_jasper_kicking_euro_ball_00358.png
deleted file mode 100644
index c3efeff..0000000
Binary files a/data/samples/sequences_jasper_kicking_euro_ball_00358.png and /dev/null differ
diff --git a/poetry.lock b/poetry.lock
index 3ca81cd..cd41d51 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -31,7 +31,7 @@ optional = false
python-versions = ">=3.6.0"
[package.extras]
-unicode_backport = ["unicodedata2"]
+unicode-backport = ["unicodedata2"]
[[package]]
name = "colorama"
@@ -138,7 +138,7 @@ six = ">=1.9.0"
[package.extras]
aiohttp = ["aiohttp (>=3.6.2,<4.0.0dev)", "requests (>=2.20.0,<3.0.0dev)"]
-enterprise_cert = ["cryptography (==36.0.2)", "pyopenssl (==22.0.0)"]
+enterprise-cert = ["cryptography (==36.0.2)", "pyopenssl (==22.0.0)"]
pyopenssl = ["pyopenssl (>=20.0.0)"]
reauth = ["pyu2f (>=0.1.5)"]
@@ -578,7 +578,7 @@ urllib3 = ">=1.21.1,<1.27"
[package.extras]
socks = ["PySocks (>=1.5.6,!=1.5.7)"]
-use_chardet_on_py3 = ["chardet (>=3.0.2,<6)"]
+use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
[[package]]
name = "requests-oauthlib"
@@ -1394,6 +1394,8 @@ packaging = [
{file = "packaging-21.3.tar.gz", hash = "sha256:dd47c42927d89ab911e606518907cc2d3a1f38bbd026385970643f9c5b8ecfeb"},
]
Pillow = [
+ {file = "Pillow-9.3.0-1-cp37-cp37m-win32.whl", hash = "sha256:e6ea6b856a74d560d9326c0f5895ef8050126acfdc7ca08ad703eb0081e82b74"},
+ {file = "Pillow-9.3.0-1-cp37-cp37m-win_amd64.whl", hash = "sha256:32a44128c4bdca7f31de5be641187367fe2a450ad83b833ef78910397db491aa"},
{file = "Pillow-9.3.0-cp310-cp310-macosx_10_10_x86_64.whl", hash = "sha256:0b7257127d646ff8676ec8a15520013a698d1fdc48bc2a79ba4e53df792526f2"},
{file = "Pillow-9.3.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:b90f7616ea170e92820775ed47e136208e04c967271c9ef615b6fbd08d9af0e3"},
{file = "Pillow-9.3.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:68943d632f1f9e3dce98908e873b3a090f6cba1cbb1b892a9e8d97c938871fbe"},
diff --git a/pth2pt_YOEO.py b/pth2pt_YOEO.py
new file mode 100644
index 0000000..bd96517
--- /dev/null
+++ b/pth2pt_YOEO.py
@@ -0,0 +1,23 @@
+import torch
+from yoeo import models
+import torchvision
+from detectron2.modeling import build_model
+
+def inference_func(model, image):
+ inputs = [{"image": image}]
+ return model.inference(inputs, do_postprocess=False)[0]
+
+cfg = "/home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/config/yoeo.cfg"
+# weights = "/home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/weights/yoeo_mine_759.pth"
+weights = "/home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/weights/yoeo.pth"
+# print("cfg.MODEL.WEIGHTS: ",cfg.MODEL.WEIGHTS) ## RETURNS : cfg.MODEL.WEIGHTS: drive/Detectron2/model_final.pth
+
+model = models.load_model(cfg, weights, "cpu")
+print(model)
+
+example = torch.rand(1, 3, 416, 416)
+# wrapper = TracingAdapter(module, example, inference_func)
+# model.eval()
+traced_script_module = torch.jit.trace(model, example)
+# traced_script_module = torch.jit.trace(wrapper, (example,))
+traced_script_module.save("/home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/weights/model-final.pt")
diff --git a/pyproject.toml b/pyproject.toml
index 82a9b78..90e06f2 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -34,3 +34,6 @@ build-backend = "poetry.core.masonry.api"
yoeo-detect = "yoeo.detect:run"
yoeo-train = "yoeo.train:run"
yoeo-test = "yoeo.test:run"
+yoeo-detectcam = "yoeo.detectwebcam:run"
+yoeo-detectcam_IR = "yoeo.detectwebcam_IR:run"
+yoeo-detectcam_pt = "yoeo.detectwebcam_pt:run"
diff --git a/runs/segment/predict2/0.mp4 b/runs/segment/predict2/0.mp4
new file mode 100644
index 0000000..8f082d2
Binary files /dev/null and b/runs/segment/predict2/0.mp4 differ
diff --git a/runs/segment/predict3/0.mp4 b/runs/segment/predict3/0.mp4
new file mode 100644
index 0000000..60b8efc
Binary files /dev/null and b/runs/segment/predict3/0.mp4 differ
diff --git a/runs/segment/predict4/0.mp4 b/runs/segment/predict4/0.mp4
new file mode 100644
index 0000000..ad0a217
Binary files /dev/null and b/runs/segment/predict4/0.mp4 differ
diff --git a/scripts/convertONNXModelToOpenVinoIR.py b/scripts/convertONNXModelToOpenVinoIR.py
new file mode 100644
index 0000000..23ad859
--- /dev/null
+++ b/scripts/convertONNXModelToOpenVinoIR.py
@@ -0,0 +1,66 @@
+#! /usr/bin/env python3
+import argparse
+import os
+
+
+def convert_model(onnx_path: str, output_path: str) -> None:
+ command = assemble_command(onnx_path, output_path)
+ run_command(command)
+
+
+def assemble_command(onnx_path: str, output_path: str) -> str:
+ # https://docs.openvino.ai/latest/notebooks/102-pytorch-onnx-to-openvino-with-output.html (April 7, 2022)
+ mo_command = f"""mo
+ --input_model "{onnx_path}"
+ --output_dir "{output_path}"
+ --input InputLayer
+ --output Detections,Segmentations
+ --framework onnx
+ --static_shape
+ --batch 1
+ """
+ mo_command = " ".join(mo_command.split())
+
+ return mo_command
+
+
+def run_command(command: str) -> None:
+ # https://docs.openvino.ai/latest/notebooks/102-pytorch-onnx-to-openvino-with-output.html (April 7, 2022)
+ print("Exporting ONNX model to IR...")
+
+ mo_result = os.system(command)
+
+ print("=" * 30)
+ if mo_result == 0:
+ print("Model conversion was successful")
+ else:
+ print("Model conversion failed")
+
+
+def get_output_path(model_onnx: str) -> str:
+ return get_parent_dir(model_onnx)
+
+
+def get_parent_dir(path: str) -> str:
+ absolute_path = os.path.abspath(path)
+ parent_dir, filename = os.path.split(absolute_path)
+
+ return parent_dir
+
+
+def run():
+ parser = argparse.ArgumentParser(description='Convert ONNX Model to OpenVino IR')
+ parser.add_argument(
+ "model_onnx",
+ type=str,
+ help="full path to model file (.onnx)"
+ )
+
+ args = parser.parse_args()
+
+ output_path = get_output_path(args.model_onnx)
+ convert_model(args.model_onnx, output_path)
+
+
+if __name__ == "__main__":
+ run()
\ No newline at end of file
diff --git a/scripts/convertPyTorchModelToONNX.py b/scripts/convertPyTorchModelToONNX.py
new file mode 100644
index 0000000..1ff7ab1
--- /dev/null
+++ b/scripts/convertPyTorchModelToONNX.py
@@ -0,0 +1,100 @@
+#! /usr/bin/env python3
+import argparse
+import os.path
+from typing import Tuple
+
+import onnx
+import torch
+
+import yoeo.models
+
+
+def convert_model(model_cfg: str, weights_pth: str, output_path: str) -> None:
+ pytorch_model = yoeo.models.load_model(model_cfg, weights_pth)
+ convert_to_onnx(model=pytorch_model, output_path=output_path)
+
+
+def convert_to_onnx(model: yoeo.models.Darknet, output_path: str, image_size: int = 416, batch_size: int = 1) -> None:
+ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+ model.to(device)
+ dummy_input = torch.randn(batch_size, 3, image_size, image_size, device=device)
+
+ torch.onnx.export(
+ model,
+ dummy_input,
+ output_path,
+ verbose=False,
+ export_params=True,
+ input_names=["InputLayer"],
+ output_names=["Detections", "Segmentations"],
+ opset_version=11
+ )
+
+
+def check_model(model_path: str) -> None:
+ onnx_model = load_onnx(model_path)
+ check_onnx(onnx_model)
+
+
+def load_onnx(path: str) -> onnx.onnx_ml_pb2.ModelProto:
+ return onnx.load(path)
+
+
+def check_onnx(model: onnx.onnx_ml_pb2.ModelProto) -> None:
+ # https://github.com/onnx/onnx/blob/main/docs/PythonAPIOverview.md (April 7, 2022)
+ print("="*30)
+ try:
+ onnx.checker.check_model(model)
+ except onnx.checker.ValidationError as e:
+ print('The model is invalid: %s' % e)
+ else:
+ print('The model is valid!')
+
+
+def construct_path(model_cfg: str) -> str:
+ parent_dir = get_parent_dir(model_cfg)
+ filename = get_filename_wout_extension(model_cfg)
+
+ onnx_path = os.path.join(parent_dir, f"{filename}.onnx")
+
+ return onnx_path
+
+
+def get_parent_dir(path: str) -> str:
+ absolute_path = os.path.abspath(path)
+ parent_dir, filename = os.path.split(absolute_path)
+
+ return parent_dir
+
+
+def get_filename_wout_extension(path: str) -> str:
+ absolute_path = os.path.abspath(path)
+ parent_dir, filename = os.path.split(absolute_path)
+ filename, ext = os.path.splitext(filename)
+
+ return filename
+
+
+def run():
+ parser = argparse.ArgumentParser(description='Convert PyTorch Model to ONNX')
+ parser.add_argument(
+ "model_cfg",
+ type=str,
+ help="full path to model file (.cfg). ONNX model will be output with the same filename as well."
+ )
+ parser.add_argument(
+ "model_weights",
+ type=str,
+ help="full path to model weights file (.pth or .weights)."
+ )
+
+ args = parser.parse_args()
+
+ onnx_path = construct_path(args.model_cfg)
+ convert_model(args.model_cfg, args.model_weights, onnx_path)
+ check_model(onnx_path)
+
+
+if __name__ == "__main__":
+ run()
\ No newline at end of file
diff --git a/tezises/inputs&outputs b/tezises/inputs&outputs
new file mode 100644
index 0000000..c3619e6
--- /dev/null
+++ b/tezises/inputs&outputs
@@ -0,0 +1,38 @@
+PYTORCH .cfg + .pth
+
+num 1 DETECTIONS SHAPE IStorch.Size([1, 6000, 8]),
+num 1 SEGMENTATIONS SHAPE IStorch.Size([1, 640, 640]),
+
+torchscipt
+
+python .pt
+SHAPE OF EVERY ELEM: torch.Size([1, 3, 20, 20, 8])
+SHAPE OF EVERY ELEM: torch.Size([1, 3, 40, 40, 8])
+SHAPE OF EVERY ELEM: torch.Size([1, 3, 640, 640])
+
+cpp .pt
+Shape of every elem: [1, 3, 20, 20, 8]
+Shape of every elem: [1, 3, 40, 40, 8]
+Shape of every elem: [1, 3, 640, 640]
+
+IR
+
+python .xml
+outputs[
+,
+
+]>
+
+cpp .xml
+[1,2535,8]
+[1,416,416]
+
+
+зрение робана
+
+Пути к весам и обработчику сеток: ~/env/common/vision_filters/ball_detection_wideangle.json
+Чето там еще (трешолды и другие параметры) ~/env/common/vision_filters/colors_wideangle.json
+пайплайн зрения прописан здесь ~/env/common/vision_filters/all.json
+
+
+
diff --git a/yoeo/detect.py b/yoeo/detect.py
index 76163fc..edbf3d1 100755
--- a/yoeo/detect.py
+++ b/yoeo/detect.py
@@ -1,5 +1,4 @@
#! /usr/bin/env python3
-
from __future__ import division
import os
@@ -59,6 +58,7 @@ def detect_directory(model_path, weights_path, img_path, classes, output_path,
output_path,
conf_thres,
nms_thres)
+
_draw_and_save_output_images(
img_detections, segmentations, imgs, img_size, output_path, classes)
@@ -134,11 +134,15 @@ def detect(model, dataloader, output_path, conf_thres, nms_thres):
for (img_paths, input_imgs) in tqdm.tqdm(dataloader, desc="Detecting"):
# Configure input
+ print(f"VOT TAKOE 1 {input_imgs}")
input_imgs = Variable(input_imgs.type(Tensor))
+ print(f"VOT TAKOE 2 {input_imgs}")
+
# Get detections
with torch.no_grad():
detections, segmentations = model(input_imgs)
+ print(f"VOT TAKOE 3 {input_imgs}")
detections = non_max_suppression(detections, conf_thres, nms_thres)
# Store image and detections
@@ -195,7 +199,7 @@ def _draw_and_save_output_image(image_path, detections, seg, img_size, output_pa
# Get segmentation
seg = seg.cpu().detach().numpy().astype(np.uint8)
# Draw all of it
-
+ print(f"ETO EST SEG {seg}")
# The amount of padding that was added
pad_x = max(img.shape[0] - img.shape[1], 0) * (img_size / max(img.shape[:2])) // 2
pad_y = max(img.shape[1] - img.shape[0], 0) * (img_size / max(img.shape[:2])) // 2
@@ -206,13 +210,14 @@ def _draw_and_save_output_image(image_path, detections, seg, img_size, output_pa
int(pad_x) : int(img_size - pad_x),
] * 255
-
+ print(f"MILLIARDNAYA {img}")
ax.imshow(
SegmentationMapsOnImage(
seg[
int(pad_y) : int(img_size - pad_y),
int(pad_x) : int(img_size - pad_x),
], shape=img.shape).draw_on_image(img)[0])
+ print("JJEEPPAAA")
# Rescale boxes to original image
detections = rescale_boxes(detections, img_size, img.shape[:2])
unique_labels = detections[:, -1].cpu().unique()
@@ -275,6 +280,7 @@ def _create_data_loader(img_path, batch_size, img_size, n_cpu):
:return: Returns DataLoader
:rtype: DataLoader
"""
+
dataset = ImageFolder(
img_path,
transform=transforms.Compose([DEFAULT_TRANSFORMS, Resize(img_size)]))
@@ -318,6 +324,5 @@ def run():
conf_thres=args.conf_thres,
nms_thres=args.nms_thres)
-
if __name__ == '__main__':
run()
diff --git a/yoeo/detectwebcam.py b/yoeo/detectwebcam.py
new file mode 100644
index 0000000..66ccd8b
--- /dev/null
+++ b/yoeo/detectwebcam.py
@@ -0,0 +1,359 @@
+#! /usr/bin/env python3
+from __future__ import division
+
+import os
+import argparse
+import tqdm
+import numpy as np
+import cv2
+import time
+
+import torch
+import torchvision.transforms as transforms
+from torch.utils.data import DataLoader
+from torch.autograd import Variable
+
+from imgaug.augmentables.segmaps import SegmentationMapsOnImage
+
+from yoeo.models import load_model
+from yoeo.utils.utils import load_classes, rescale_boxes, non_max_suppression, print_environment_info, rescale_segmentation
+from yoeo.utils.datasets import ImageFolder
+from yoeo.utils.transforms import Resize, DEFAULT_TRANSFORMS
+
+import matplotlib.pyplot as plt
+import matplotlib.patches as patches
+from matplotlib.ticker import NullLocator
+
+
+def detect_directory(model_path, weights_path, classes, output_path,
+ batch_size=8, img_size=416, n_cpu=8, conf_thres=0.5, nms_thres=0.5):
+ """Detects objects on all images in specified directory and saves output images with drawn detections.
+
+ :param model_path: Path to model definition file (.cfg)
+ :type model_path: str
+ :param weights_path: Path to weights or checkpoint file (.weights or .pth)
+ :type weights_path: str
+ :param img_path: Path to directory with images to inference
+ :type img_path: str
+ :param classes: List of class names
+ :type classes: [str]
+ :param output_path: Path to output directory
+ :type output_path: str
+ :param batch_size: Size of each image batch, defaults to 8
+ :type batch_size: int, optional
+ :param img_size: Size of each image dimension for yolo, defaults to 416
+ :type img_size: int, optional
+ :param n_cpu: Number of cpu threads to use during batch generation, defaults to 8
+ :type n_cpu: int, optional
+ :param conf_thres: Object confidence threshold, defaults to 0.5
+ :type conf_thres: float, optional
+ :param nms_thres: IOU threshold for non-maximum suppression, defaults to 0.5
+ :type nms_thres: float, optional
+ """
+ model = load_model(model_path, weights_path)
+ # model = torch.jit.load(weights_path)
+ print("NY PRIVET 7")
+
+ cam = cv2.VideoCapture(0)
+ key = cv2.waitKey(1)
+
+ while key != 27:
+ print("START OF INFERENCE OF IMAGE")
+ t = time.time()
+ _, image = cam.read()
+
+ fps = int(cam.get(cv2.CAP_PROP_FPS))
+ fps_text = "FPS: {:.2f}".format(fps)
+ cv2.putText(image, fps_text, (5, 30), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 1)
+
+ cv2.imshow('raw', image)
+
+ detections, segmentations = detect_image(
+ model,
+ image,
+ img_size,
+ conf_thres,
+ nms_thres)
+ print("NY PRIVET last")
+
+ _draw_and_save_output_image(image, detections, segmentations, img_size, output_path, classes)
+ print(time.time() - t)
+ print("END OF INFERENCE OF IMAGE")
+
+ # print(f"---- Detections were saved to: '{output_path}' ----")
+
+ print(f"SUM UP: {image.shape}")
+
+ if cv2.waitKey(1) == 27:
+ cam.release()
+ cv2.destroyAllWindows()
+ break
+
+
+def detect_image(model, image, img_size=416, conf_thres=0.5, nms_thres=0.5):
+ """Inferences one image with model.
+
+ :param model: Model for inference
+ :type model: models.Darknet
+ :param image: Image to inference
+ :type image: nd.array
+ :param img_size: Size of each image dimension for yolo, defaults to 416
+ :type img_size: int, optional
+ :param conf_thres: Object confidence threshold, defaults to 0.5
+ :type conf_thres: float, optional
+ :param nms_thres: IOU threshold for non-maximum suppression, defaults to 0.5
+ :type nms_thres: float, optional
+ :return: Detections on image with each detection in the format: [x1, y1, x2, y2, confidence, class], Segmentation as 2d numpy array with the coresponding class id in each cell
+ :rtype: nd.array, nd.array
+ """
+ model.eval() # Set model to evaluation mode
+
+ # Configure input
+ input_img = transforms.Compose([
+ DEFAULT_TRANSFORMS,
+ Resize(img_size)])((
+ image,
+ np.empty((1, 5)),
+ np.empty((img_size, img_size), dtype=np.uint8)))[0].unsqueeze(0)
+
+ print(f"raw image shape: {image.shape}")
+ print(f"torch image shape: {input_img.shape}")
+ print(f"torch image shape: {type(input_img)}")
+
+
+ # if torch.cuda.is_available():
+ # input_img = input_img.to("cuda")
+ # print(f"model: {model}")
+ # Get detections
+ with torch.no_grad():
+ detections, segmentations = model(input_img)
+ print(f"num 1 DETECTIONS SHAPE IS{detections.shape}, {type(detections)}")
+ print(f"num 1 SEGMENTATIONS SHAPE IS{segmentations.shape}, {type(segmentations)}")
+ detections = non_max_suppression(detections, conf_thres, nms_thres)
+ print(f"num 2 DETECTIONS SHAPE IS{len(detections)}")
+
+ detections = rescale_boxes(detections[0], img_size, image.shape[0:2])
+ print(f"num 3 DETECTIONS SHAPE IS{detections.shape}")
+
+ segmentations = rescale_segmentation(segmentations, image.shape[0:2])
+ print(f"detections shape: {detections.shape}")
+ print(f"detections shape: {detections.shape}")
+
+ # return detections.numpy(), segmentations.cpu().detach().numpy()
+ return detections, segmentations
+
+def detect(model, output_path, conf_thres, nms_thres):
+ """Inferences images with model.
+
+ :param model: Model for inference
+ :type model: models.Darknet
+ :param dataloader: Dataloader provides the batches of images to inference
+ :type dataloader: DataLoader
+ :param output_path: Path to output directory
+ :type output_path: str
+ :param conf_thres: Object confidence threshold, defaults to 0.5
+ :type conf_thres: float, optional
+ :param nms_thres: IOU threshold for non-maximum suppression, defaults to 0.5
+ :type nms_thres: float, optional
+ :return: List of detections. The coordinates are given for the padded image that is provided by the dataloader.
+ Use `utils.rescale_boxes` to transform them into the desired input image coordinate system before its transformed by the dataloader),
+ List of input image paths
+ :rtype: [Tensor], [str]
+ """
+ # Create output directory, if missing
+ os.makedirs(output_path, exist_ok=True)
+ print("nychetiii 1")
+ model.eval() # Set model to evaluation mode
+ print("nychetiii 2")
+ Tensor = torch.cuda.FloatTensor if torch.cuda.is_available() else torch.FloatTensor
+
+
+
+ cam = cv2.VideoCapture(0)
+ key = cv2.waitKey(1)
+ while key != 27:
+ _, image = cam.read()
+ cv2.imshow('raw', image)
+
+ img = torch.from_numpy(image)
+ img = Variable(img.type(Tensor))
+ print(img)
+ # Get detections
+ with torch.no_grad():
+ print(img)
+ detections, segmentations = model(img)
+ print("NY PRIVET 8")
+ detections = non_max_suppression(detections, conf_thres, nms_thres)
+
+ if detections and segmentations and img:
+ return detections, segmentations, img
+
+ if cv2.waitkey(1) == 27:
+ cam.release()
+ cv2.destroyAllWindows()
+
+
+def _draw_and_save_output_image(image, detections, seg, img_size, output_path, classes):
+ """Draws detections in output image and stores this.
+
+ :param image_path: Path to input image
+ :type image_path: str
+ :param detections: List of detections on image
+ :type detections: [Tensor]
+ :param seg: Segmentation image
+ :type seg: Tensor
+ :param img_size: Size of each image dimension for yolo
+ :type img_size: int
+ :param output_path: Path of output directory
+ :type output_path: str
+ :param classes: List of class names
+ :type classes: [str]
+ """
+ # Create plot
+ img = cv2.cvtColor(image,cv2.COLOR_BGR2RGB)
+ plt.figure()
+ fig, ax = plt.subplots(1)
+ # Get segmentation
+ seg = seg.cpu().detach().numpy().astype(np.uint8)
+ # seg = seg.astype(np.uint8)
+ # Draw all of it
+ seg = seg[0]
+ print(f"ETO EST SEG {seg}")
+ # The amount of padding that was added
+ print("GOVNINA")
+ print(img_size / max(img.shape[:2]))
+ print(max(img.shape[0] - img.shape[1], 0))
+ print(img.shape[0], img.shape[1])
+ print("end of GOVNINA")
+ pad_x = max(img.shape[0] - img.shape[1], 0) * (img_size / max(img.shape[:2])) // 2
+ # pad_x = 21.0
+ pad_y = max(img.shape[1] - img.shape[0], 0) * (img_size / max(img.shape[:2])) // 2
+ print(f"CHEKAI PADI {pad_x, pad_y}")
+
+ seg_map = seg[
+ int(pad_y) : int(img_size - pad_y),
+ int(pad_x) : int(img_size - pad_x),
+ ] * 255
+
+ print(f"MILLIARDNAYA {img, pad_y, pad_x}")
+ ax.imshow(
+ SegmentationMapsOnImage(
+ seg[
+ int(pad_y) : int(img_size - pad_y),
+ int(pad_x) : int(img_size - pad_x),
+ ], shape=img.shape).draw_on_image(img)[0])
+ print("JJEEPPAAA")
+ # Rescale boxes to original image
+
+ print(f"num after 1 DETECTIONS SHAPE IS{detections.shape}")
+ detections = rescale_boxes(detections, img_size, img.shape[:2])
+
+ unique_labels = detections[:, -1].cpu().unique()
+ n_cls_preds = len(unique_labels)
+ # Bounding-box colors
+ cmap = plt.get_cmap("tab20b")
+ colors = [cmap(i) for i in np.linspace(0, 1, len(classes))]
+ print(f"num after 2 DETECTIONS SHAPE IS{detections.shape}")
+ for x1, y1, x2, y2, conf, cls_pred in detections:
+
+ print(f"\t+ Label: {classes[int(cls_pred)]} | Confidence: {conf.item():0.4f}")
+
+ box_w = x2 - x1
+ box_h = y2 - y1
+
+ # Create a Rectangle patch
+ bbox = patches.Rectangle((x1, y1), box_w, box_h, linewidth=2, edgecolor=colors[int(cls_pred)], facecolor="none")
+ # Add the bbox to the plot
+ ax.add_patch(bbox)
+ # Add label
+ """
+ plt.text(
+ x1,
+ y1,
+ s=classes[int(cls_pred)],
+ color="white",
+ verticalalignment="top",
+ bbox={"color": colors[int(cls_pred)], "pad": 0})
+ """
+
+ # Save generated image with detections
+ plt.axis("off")
+ plt.tight_layout(pad=0)
+ plt.gca().xaxis.set_major_locator(NullLocator())
+ plt.gca().yaxis.set_major_locator(NullLocator())
+ # filename = os.path.basename(image_path).split(".")[0]
+ # output_path_1 = os.path.join(output_path, f"{filename}.png")
+ # redraw the canvas
+ fig.canvas.draw()
+ # convert canvas to image
+ img = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8,
+ sep='')
+ print(f"summing up PICTURE 0 : {img.shape}")
+ img = img.reshape(fig.canvas.get_width_height()[::-1] + (3,))
+ # img is rgb, convert to opencv's default bgr
+ img = cv2.cvtColor(img,cv2.COLOR_RGB2BGR)
+ print(f"summing up PICTURE 1 : {img.shape}")
+ # cv2.imwrite(output_path_1, img)
+ cv2.imshow('inference', img)
+ # cv2.waitKey(1)
+
+
+def _create_data_loader(img_path, batch_size, img_size, n_cpu):
+ """Creates a DataLoader for inferencing.
+
+ :param img_path: Path to file containing all paths to validation images.
+ :type img_path: str
+ :param batch_size: Size of each image batch
+ :type batch_size: int
+ :param img_size: Size of each image dimension for yolo
+ :type img_size: int
+ :param n_cpu: Number of cpu threads to use during batch generation
+ :type n_cpu: int
+ :return: Returns DataLoader
+ :rtype: DataLoader
+ """
+
+ dataset = ImageFolder(
+ img_path,
+ transform=transforms.Compose([DEFAULT_TRANSFORMS, Resize(img_size)]))
+ dataloader = DataLoader(
+ dataset,
+ batch_size=batch_size,
+ shuffle=False,
+ num_workers=n_cpu,
+ pin_memory=True)
+ return dataloader
+
+
+def run():
+ print_environment_info()
+ parser = argparse.ArgumentParser(description="Detect objects on images.")
+ parser.add_argument("-m", "--model", type=str, default="config/yoeo.cfg", help="Path to model definition file (.cfg)")
+ parser.add_argument("-w", "--weights", type=str, default="weights/yoeo.pth", help="Path to weights or checkpoint file (.weights or .pth)")
+ parser.add_argument("-i", "--images", type=str, default="data/samples", help="Path to directory with images to inference")
+ parser.add_argument("-c", "--classes", type=str, default="data/yoeo_names.yaml", help="Path to .yaml file containing the classes' names")
+ parser.add_argument("-o", "--output", type=str, default="output", help="Path to output directory")
+ parser.add_argument("-b", "--batch_size", type=int, default=1, help="Size of each image batch")
+ parser.add_argument("--img_size", type=int, default=416, help="Size of each image dimension for yolo")
+ parser.add_argument("--n_cpu", type=int, default=8, help="Number of cpu threads to use during batch generation")
+ parser.add_argument("--conf_thres", type=float, default=0.5, help="Object confidence threshold")
+ parser.add_argument("--nms_thres", type=float, default=0.4, help="IOU threshold for non-maximum suppression")
+ args = parser.parse_args()
+ print(f"Command line arguments: {args}")
+
+ # Extract class names from file
+ classes = load_classes(args.classes)['detection'] # List of class names
+
+ detect_directory(
+ args.model,
+ args.weights,
+ classes,
+ args.output,
+ batch_size=args.batch_size,
+ img_size=args.img_size,
+ n_cpu=args.n_cpu,
+ conf_thres=args.conf_thres,
+ nms_thres=args.nms_thres)
+
+if __name__ == '__main__':
+ run()
diff --git a/yoeo/detectwebcam_IR.py b/yoeo/detectwebcam_IR.py
new file mode 100644
index 0000000..3d8510d
--- /dev/null
+++ b/yoeo/detectwebcam_IR.py
@@ -0,0 +1,236 @@
+#! /usr/bin/env python3
+from __future__ import division
+
+import os
+import argparse
+import tqdm
+import numpy as np
+import cv2
+import time
+
+import sys
+import time
+from pathlib import Path
+from openvino.runtime import Core
+
+
+import torch
+import torchvision.transforms as transforms
+from torch.utils.data import DataLoader
+from torch.autograd import Variable
+
+from imgaug.augmentables.segmaps import SegmentationMapsOnImage
+
+from yoeo.models import load_model
+from yoeo.utils.utils import load_classes, rescale_boxes, non_max_suppression, print_environment_info, rescale_segmentation
+from yoeo.utils.datasets import ImageFolder
+from yoeo.utils.transforms import Resize, DEFAULT_TRANSFORMS
+
+import matplotlib.cm
+import matplotlib.pyplot as plt
+import matplotlib.patches as patches
+from matplotlib.ticker import NullLocator
+
+
+def detect_directory(model_path, weights_path, classes, output_path,
+ batch_size=8, img_size=416, n_cpu=8, conf_thres=0.5, nms_thres=0.5):
+ """Detects objects on all images in specified directory and saves output images with drawn detections.
+
+ :param model_path: Path to model definition file (.cfg)
+ :type model_path: str
+ :param weights_path: Path to weights or checkpoint file (.weights or .pth)
+ :type weights_path: str
+ :param img_path: Path to directory with images to inference
+ :type img_path: str
+ :param classes: List of class names
+ :type classes: [str]
+ :param output_path: Path to output directory
+ :type output_path: str
+ :param batch_size: Size of each image batch, defaults to 8
+ :type batch_size: int, optional
+ :param img_size: Size of each image dimension for yolo, defaults to 416
+ :type img_size: int, optional
+ :param n_cpu: Number of cpu threads to use during batch generation, defaults to 8
+ :type n_cpu: int, optional
+ :param conf_thres: Object confidence threshold, defaults to 0.5
+ :type conf_thres: float, optional
+ :param nms_thres: IOU threshold for non-maximum suppression, defaults to 0.5
+ :type nms_thres: float, optional
+ """
+ device = "CPU"
+ model_xml_path = "/home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/config/IR&onnx_for_416_Petr_1/yoeo.xml"
+ ie = Core()
+ ie.set_property({'CACHE_DIR': '../cache'})
+ model = ie.read_model(model_xml_path)
+ compiled_model = ie.compile_model(model=model, device_name=device)
+ print(compiled_model.input(0).shape, compiled_model.output(1).shape)
+ input_key = compiled_model.input(0)
+ network_input_shape = list(input_key.shape)
+ network_image_height, network_image_width = network_input_shape[2:]
+
+ print("NY PRIVET 7")
+ cam = cv2.VideoCapture(0)
+ key = cv2.waitKey(1)
+
+ while key != 27:
+ print("START OF INFERENCE OF IMAGE")
+ t = time.time()
+ _, image = cam.read()
+
+ fps = int(cam.get(cv2.CAP_PROP_FPS))
+ fps_text = "FPS: {:.2f}".format(fps)
+ cv2.putText(image, fps_text, (5, 30), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 1)
+
+ cv2.imshow('raw', image)
+
+ result_image = detect_image(
+ compiled_model,
+ image,
+ network_image_height,
+ network_image_width,
+ conf_thres,
+ nms_thres)
+
+ cv2.imshow("rawIMG", image)
+ cv2.imshow("resultIMG", result_image)
+
+ if cv2.waitKey(1) == 27:
+ cam.release()
+ cv2.destroyAllWindows()
+ break
+
+def to_rgb(image_data) -> np.ndarray:
+ """
+ Convert image_data from BGR to RGB
+ """
+ return cv2.cvtColor(image_data, cv2.COLOR_BGR2RGB)
+
+def to_bgr(image_data) -> np.ndarray:
+ """
+ Convert image_data from BGR to RGB
+ """
+ return cv2.cvtColor(image_data, cv2.COLOR_RGB2BGR)
+
+def normalize_minmax(data):
+ """Normalizes the values in `data` between 0 and 1"""
+ return (data - data.min()) / (data.max() - data.min())
+
+def convert_result_to_image(result, colormap="viridis"):
+ """
+ Convert network result of floating point numbers to an RGB image with
+ integer values from 0-255 by applying a colormap.
+
+ `result` is expected to be a single network result in 1,H,W shape
+ `colormap` is a matplotlib colormap.
+ See https://matplotlib.org/stable/tutorials/colors/colormaps.html
+ """
+ cmap = matplotlib.cm.get_cmap(colormap)
+ result = result.squeeze(0)
+ result = normalize_minmax(result)
+ result = cmap(result)[:, :, :3] * 255
+ result = result.astype(np.uint8)
+ return result
+
+
+def detect_image(compiled_model, image, network_image_height=416, network_image_width=416, conf_thres=0.5, nms_thres=0.5):
+ """Inferences one image with model.
+
+ :param model: Model for inference
+ :type model: models.Darknet
+ :param image: Image to inference
+ :type image: nd.array
+ :param img_size: Size of each image dimension for yolo, defaults to 416
+ :type img_size: int, optional
+ :param conf_thres: Object confidence threshold, defaults to 0.5
+ :type conf_thres: float, optional
+ :param nms_thres: IOU threshold for non-maximum suppression, defaults to 0.5
+ :type nms_thres: float, optional
+ :return: Detections on image with each detection in the format: [x1, y1, x2, y2, confidence, class], Segmentation as 2d numpy array with the coresponding class id in each cell
+ :rtype: nd.array, nd.array
+ """
+ # Resize to input shape for network.
+
+ # resized_image = to_rgb(cv2.resize(src=image, dsize=(network_image_height, network_image_width)))
+
+ # resized_image = cv2.resize(src=image, dsize=(network_image_height, network_image_width))
+
+ # Reshape the image to network input shape NCHW.
+ # input_image = np.expand_dims(np.transpose(resized_image, (2, 0, 1)), 0)
+
+ input_image = transforms.Compose([
+ DEFAULT_TRANSFORMS,
+ Resize(network_image_height)])((
+ image,
+ np.empty((1, 5)),
+ np.empty((network_image_height, network_image_height), dtype=np.uint8)))[0].unsqueeze(0)
+
+ print(f"raw image shape: {image.shape}")
+ print(f"torch image shape: {input_image.shape}")
+ print(f"torch image shape: {type(input_image)}")
+
+
+ # if torch.cuda.is_available():
+ # input_img = input_img.to("cuda")
+ print(f"model: {compiled_model}")
+ # Get detections
+ output_seg = compiled_model.output(1)
+ result = compiled_model([input_image])[output_seg]
+ print(f"RESULT TYPE and SHAPE: {type(result)}, {result.shape} AND OUTPUTSEG == {type(output_seg)}")
+ unq = []
+ for elem in result:
+ for raw in elem:
+ for l in raw:
+ if l not in unq:
+ unq.append(l)
+ print(l)
+ print(f"UNQ IS {unq}")
+
+ # Convert the network result of disparity map to an image that shows
+ # distance as colors.
+ result_image = convert_result_to_image(result=result)
+ # result_image = to_bgr(result_image)
+ # Resize back to original image shape. The `cv2.resize` function expects shape
+ # in (width, height), [::-1] reverses the (height, width) shape to match this.
+ result_image = cv2.resize(result_image, image.shape[:2][::-1])
+
+ # detections, segmentations = model(input_image)
+ # segmentations = rescale_segmentation(segmentations, image.shape[0:2])
+ # print(f"detections shape: {detections.shape}")
+ # print(f"detections shape: {detections.shape}")
+
+ # return detections.numpy(), segmentations.cpu().detach().numpy()
+ return result_image
+
+
+def run():
+ print_environment_info()
+ parser = argparse.ArgumentParser(description="Detect objects on images.")
+ parser.add_argument("-m", "--model", type=str, default="config/yoeo.cfg", help="Path to model definition file (.cfg)")
+ parser.add_argument("-w", "--weights", type=str, default="weights/yoeo.pth", help="Path to weights or checkpoint file (.weights or .pth)")
+ parser.add_argument("-i", "--images", type=str, default="data/samples", help="Path to directory with images to inference")
+ parser.add_argument("-c", "--classes", type=str, default="data/yoeo_names.yaml", help="Path to .yaml file containing the classes' names")
+ parser.add_argument("-o", "--output", type=str, default="output", help="Path to output directory")
+ parser.add_argument("-b", "--batch_size", type=int, default=1, help="Size of each image batch")
+ parser.add_argument("--img_size", type=int, default=416, help="Size of each image dimension for yolo")
+ parser.add_argument("--n_cpu", type=int, default=8, help="Number of cpu threads to use during batch generation")
+ parser.add_argument("--conf_thres", type=float, default=0.5, help="Object confidence threshold")
+ parser.add_argument("--nms_thres", type=float, default=0.4, help="IOU threshold for non-maximum suppression")
+ args = parser.parse_args()
+ print(f"Command line arguments: {args}")
+
+ # Extract class names from file
+ classes = load_classes(args.classes)['detection'] # List of class names
+
+ detect_directory(
+ args.model,
+ args.weights,
+ classes,
+ args.output,
+ batch_size=args.batch_size,
+ img_size=args.img_size,
+ n_cpu=args.n_cpu,
+ conf_thres=args.conf_thres,
+ nms_thres=args.nms_thres)
+
+if __name__ == '__main__':
+ run()
\ No newline at end of file
diff --git a/yoeo/detectwebcam_IR_cpp/.CMakeCache.txt b/yoeo/detectwebcam_IR_cpp/.CMakeCache.txt
new file mode 100644
index 0000000..9da3516
--- /dev/null
+++ b/yoeo/detectwebcam_IR_cpp/.CMakeCache.txt
@@ -0,0 +1,480 @@
+# This is the CMakeCache file.
+# For build in directory: /home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/yoeo/detectwebcam_IR_cpp
+# It was generated by CMake: /usr/local/bin/cmake
+# You can edit this file to change values found and used by cmake.
+# If you do not want to change any of the values, simply exit the editor.
+# If you do want to change a value, simply edit, save, and exit the editor.
+# The syntax for the file is as follows:
+# KEY:TYPE=VALUE
+# KEY is the name of a variable in the cache.
+# TYPE is a hint to GUIs for the type of VALUE, DO NOT EDIT TYPE!.
+# VALUE is the current value for the KEY.
+
+########################
+# EXTERNAL cache entries
+########################
+
+//Path to a file.
+CL2_HPP_INCLUDE_DIR:PATH=CL2_HPP_INCLUDE_DIR-NOTFOUND
+
+//Path to a program.
+CMAKE_ADDR2LINE:FILEPATH=/usr/bin/addr2line
+
+//Path to a program.
+CMAKE_AR:FILEPATH=/usr/bin/ar
+
+//Choose the type of build, options are: None Debug Release RelWithDebInfo
+// MinSizeRel ...
+CMAKE_BUILD_TYPE:STRING=Release
+
+//Enable/Disable color output during build.
+CMAKE_COLOR_MAKEFILE:BOOL=ON
+
+//CXX compiler
+CMAKE_CXX_COMPILER:STRING=/usr/bin/c++
+
+//A wrapper around 'ar' adding the appropriate '--plugin' option
+// for the GCC compiler
+CMAKE_CXX_COMPILER_AR:FILEPATH=/usr/bin/gcc-ar-9
+
+//A wrapper around 'ranlib' adding the appropriate '--plugin' option
+// for the GCC compiler
+CMAKE_CXX_COMPILER_RANLIB:FILEPATH=/usr/bin/gcc-ranlib-9
+
+//Flags used by the CXX compiler during all build types.
+CMAKE_CXX_FLAGS:STRING=
+
+//Flags used by the CXX compiler during DEBUG builds.
+CMAKE_CXX_FLAGS_DEBUG:STRING=-g
+
+//Flags used by the CXX compiler during MINSIZEREL builds.
+CMAKE_CXX_FLAGS_MINSIZEREL:STRING=-Os -DNDEBUG
+
+//Flags used by the CXX compiler during RELEASE builds.
+CMAKE_CXX_FLAGS_RELEASE:STRING=-O3 -DNDEBUG
+
+//Flags used by the CXX compiler during RELWITHDEBINFO builds.
+CMAKE_CXX_FLAGS_RELWITHDEBINFO:STRING=-O2 -g -DNDEBUG
+
+//C compiler
+CMAKE_C_COMPILER:STRING=/usr/bin/cc
+
+//A wrapper around 'ar' adding the appropriate '--plugin' option
+// for the GCC compiler
+CMAKE_C_COMPILER_AR:FILEPATH=/usr/bin/gcc-ar-9
+
+//A wrapper around 'ranlib' adding the appropriate '--plugin' option
+// for the GCC compiler
+CMAKE_C_COMPILER_RANLIB:FILEPATH=/usr/bin/gcc-ranlib-9
+
+//Flags used by the C compiler during all build types.
+CMAKE_C_FLAGS:STRING=
+
+//Flags used by the C compiler during DEBUG builds.
+CMAKE_C_FLAGS_DEBUG:STRING=-g
+
+//Flags used by the C compiler during MINSIZEREL builds.
+CMAKE_C_FLAGS_MINSIZEREL:STRING=-Os -DNDEBUG
+
+//Flags used by the C compiler during RELEASE builds.
+CMAKE_C_FLAGS_RELEASE:STRING=-O3 -DNDEBUG
+
+//Flags used by the C compiler during RELWITHDEBINFO builds.
+CMAKE_C_FLAGS_RELWITHDEBINFO:STRING=-O2 -g -DNDEBUG
+
+//Path to a program.
+CMAKE_DLLTOOL:FILEPATH=CMAKE_DLLTOOL-NOTFOUND
+
+//Flags used by the linker during all build types.
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+
+//Flags used by the linker during DEBUG builds.
+CMAKE_EXE_LINKER_FLAGS_DEBUG:STRING=
+
+//Flags used by the linker during MINSIZEREL builds.
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+
+//Flags used by the linker during RELEASE builds.
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+
+//Flags used by the linker during RELWITHDEBINFO builds.
+CMAKE_EXE_LINKER_FLAGS_RELWITHDEBINFO:STRING=
+
+//Enable/Disable output of compile commands during generation.
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+
+//Install path prefix, prepended onto install directories.
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+
+//Path to a program.
+CMAKE_LINKER:FILEPATH=/usr/bin/ld
+
+//Path to a program.
+CMAKE_MAKE_PROGRAM:FILEPATH=/usr/bin/make
+
+//Flags used by the linker during the creation of modules during
+// all build types.
+CMAKE_MODULE_LINKER_FLAGS:STRING=
+
+//Flags used by the linker during the creation of modules during
+// DEBUG builds.
+CMAKE_MODULE_LINKER_FLAGS_DEBUG:STRING=
+
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+// MINSIZEREL builds.
+CMAKE_MODULE_LINKER_FLAGS_MINSIZEREL:STRING=
+
+//Flags used by the linker during the creation of modules during
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+CMAKE_MODULE_LINKER_FLAGS_RELEASE:STRING=
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+
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+
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+CMAKE_OBJCOPY:FILEPATH=/usr/bin/objcopy
+
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+CMAKE_OBJDUMP:FILEPATH=/usr/bin/objdump
+
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+
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+// during all build types.
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+
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+// during DEBUG builds.
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+
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+// during RELEASE builds.
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+
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+// during RELWITHDEBINFO builds.
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+
+//If set, runtime paths are not added when installing shared libraries,
+// but are added when building.
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+
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+
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+// during all build types.
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+// during DEBUG builds.
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+
+//Path to a program.
+CMAKE_STRIP:FILEPATH=/usr/bin/strip
+
+//If this value is on, makefiles will be generated without the
+// .SILENT directive, and all commands will be echoed to the console
+// during the make. This is useful for debugging only. With Visual
+// Studio IDE projects all commands are done without /nologo.
+CMAKE_VERBOSE_MAKEFILE:BOOL=FALSE
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+#elif defined(__ultrix) || defined(__ultrix__) || defined(_ULTRIX)
+# define PLATFORM_ID "ULTRIX"
+
+#elif defined(__XENIX__) || defined(_XENIX) || defined(XENIX)
+# define PLATFORM_ID "Xenix"
+
+#elif defined(__WATCOMC__)
+# if defined(__LINUX__)
+# define PLATFORM_ID "Linux"
+
+# elif defined(__DOS__)
+# define PLATFORM_ID "DOS"
+
+# elif defined(__OS2__)
+# define PLATFORM_ID "OS2"
+
+# elif defined(__WINDOWS__)
+# define PLATFORM_ID "Windows3x"
+
+# elif defined(__VXWORKS__)
+# define PLATFORM_ID "VxWorks"
+
+# else /* unknown platform */
+# define PLATFORM_ID
+# endif
+
+#elif defined(__INTEGRITY)
+# if defined(INT_178B)
+# define PLATFORM_ID "Integrity178"
+
+# else /* regular Integrity */
+# define PLATFORM_ID "Integrity"
+# endif
+
+#else /* unknown platform */
+# define PLATFORM_ID
+
+#endif
+
+/* For windows compilers MSVC and Intel we can determine
+ the architecture of the compiler being used. This is because
+ the compilers do not have flags that can change the architecture,
+ but rather depend on which compiler is being used
+*/
+#if defined(_WIN32) && defined(_MSC_VER)
+# if defined(_M_IA64)
+# define ARCHITECTURE_ID "IA64"
+
+# elif defined(_M_ARM64EC)
+# define ARCHITECTURE_ID "ARM64EC"
+
+# elif defined(_M_X64) || defined(_M_AMD64)
+# define ARCHITECTURE_ID "x64"
+
+# elif defined(_M_IX86)
+# define ARCHITECTURE_ID "X86"
+
+# elif defined(_M_ARM64)
+# define ARCHITECTURE_ID "ARM64"
+
+# elif defined(_M_ARM)
+# if _M_ARM == 4
+# define ARCHITECTURE_ID "ARMV4I"
+# elif _M_ARM == 5
+# define ARCHITECTURE_ID "ARMV5I"
+# else
+# define ARCHITECTURE_ID "ARMV" STRINGIFY(_M_ARM)
+# endif
+
+# elif defined(_M_MIPS)
+# define ARCHITECTURE_ID "MIPS"
+
+# elif defined(_M_SH)
+# define ARCHITECTURE_ID "SHx"
+
+# else /* unknown architecture */
+# define ARCHITECTURE_ID ""
+# endif
+
+#elif defined(__WATCOMC__)
+# if defined(_M_I86)
+# define ARCHITECTURE_ID "I86"
+
+# elif defined(_M_IX86)
+# define ARCHITECTURE_ID "X86"
+
+# else /* unknown architecture */
+# define ARCHITECTURE_ID ""
+# endif
+
+#elif defined(__IAR_SYSTEMS_ICC__) || defined(__IAR_SYSTEMS_ICC)
+# if defined(__ICCARM__)
+# define ARCHITECTURE_ID "ARM"
+
+# elif defined(__ICCRX__)
+# define ARCHITECTURE_ID "RX"
+
+# elif defined(__ICCRH850__)
+# define ARCHITECTURE_ID "RH850"
+
+# elif defined(__ICCRL78__)
+# define ARCHITECTURE_ID "RL78"
+
+# elif defined(__ICCRISCV__)
+# define ARCHITECTURE_ID "RISCV"
+
+# elif defined(__ICCAVR__)
+# define ARCHITECTURE_ID "AVR"
+
+# elif defined(__ICC430__)
+# define ARCHITECTURE_ID "MSP430"
+
+# elif defined(__ICCV850__)
+# define ARCHITECTURE_ID "V850"
+
+# elif defined(__ICC8051__)
+# define ARCHITECTURE_ID "8051"
+
+# elif defined(__ICCSTM8__)
+# define ARCHITECTURE_ID "STM8"
+
+# else /* unknown architecture */
+# define ARCHITECTURE_ID ""
+# endif
+
+#elif defined(__ghs__)
+# if defined(__PPC64__)
+# define ARCHITECTURE_ID "PPC64"
+
+# elif defined(__ppc__)
+# define ARCHITECTURE_ID "PPC"
+
+# elif defined(__ARM__)
+# define ARCHITECTURE_ID "ARM"
+
+# elif defined(__x86_64__)
+# define ARCHITECTURE_ID "x64"
+
+# elif defined(__i386__)
+# define ARCHITECTURE_ID "X86"
+
+# else /* unknown architecture */
+# define ARCHITECTURE_ID ""
+# endif
+
+#elif defined(__TI_COMPILER_VERSION__)
+# if defined(__TI_ARM__)
+# define ARCHITECTURE_ID "ARM"
+
+# elif defined(__MSP430__)
+# define ARCHITECTURE_ID "MSP430"
+
+# elif defined(__TMS320C28XX__)
+# define ARCHITECTURE_ID "TMS320C28x"
+
+# elif defined(__TMS320C6X__) || defined(_TMS320C6X)
+# define ARCHITECTURE_ID "TMS320C6x"
+
+# else /* unknown architecture */
+# define ARCHITECTURE_ID ""
+# endif
+
+#else
+# define ARCHITECTURE_ID
+#endif
+
+/* Convert integer to decimal digit literals. */
+#define DEC(n) \
+ ('0' + (((n) / 10000000)%10)), \
+ ('0' + (((n) / 1000000)%10)), \
+ ('0' + (((n) / 100000)%10)), \
+ ('0' + (((n) / 10000)%10)), \
+ ('0' + (((n) / 1000)%10)), \
+ ('0' + (((n) / 100)%10)), \
+ ('0' + (((n) / 10)%10)), \
+ ('0' + ((n) % 10))
+
+/* Convert integer to hex digit literals. */
+#define HEX(n) \
+ ('0' + ((n)>>28 & 0xF)), \
+ ('0' + ((n)>>24 & 0xF)), \
+ ('0' + ((n)>>20 & 0xF)), \
+ ('0' + ((n)>>16 & 0xF)), \
+ ('0' + ((n)>>12 & 0xF)), \
+ ('0' + ((n)>>8 & 0xF)), \
+ ('0' + ((n)>>4 & 0xF)), \
+ ('0' + ((n) & 0xF))
+
+/* Construct a string literal encoding the version number components. */
+#ifdef COMPILER_VERSION_MAJOR
+char const info_version[] = {
+ 'I', 'N', 'F', 'O', ':',
+ 'c','o','m','p','i','l','e','r','_','v','e','r','s','i','o','n','[',
+ COMPILER_VERSION_MAJOR,
+# ifdef COMPILER_VERSION_MINOR
+ '.', COMPILER_VERSION_MINOR,
+# ifdef COMPILER_VERSION_PATCH
+ '.', COMPILER_VERSION_PATCH,
+# ifdef COMPILER_VERSION_TWEAK
+ '.', COMPILER_VERSION_TWEAK,
+# endif
+# endif
+# endif
+ ']','\0'};
+#endif
+
+/* Construct a string literal encoding the internal version number. */
+#ifdef COMPILER_VERSION_INTERNAL
+char const info_version_internal[] = {
+ 'I', 'N', 'F', 'O', ':',
+ 'c','o','m','p','i','l','e','r','_','v','e','r','s','i','o','n','_',
+ 'i','n','t','e','r','n','a','l','[',
+ COMPILER_VERSION_INTERNAL,']','\0'};
+#endif
+
+/* Construct a string literal encoding the version number components. */
+#ifdef SIMULATE_VERSION_MAJOR
+char const info_simulate_version[] = {
+ 'I', 'N', 'F', 'O', ':',
+ 's','i','m','u','l','a','t','e','_','v','e','r','s','i','o','n','[',
+ SIMULATE_VERSION_MAJOR,
+# ifdef SIMULATE_VERSION_MINOR
+ '.', SIMULATE_VERSION_MINOR,
+# ifdef SIMULATE_VERSION_PATCH
+ '.', SIMULATE_VERSION_PATCH,
+# ifdef SIMULATE_VERSION_TWEAK
+ '.', SIMULATE_VERSION_TWEAK,
+# endif
+# endif
+# endif
+ ']','\0'};
+#endif
+
+/* Construct the string literal in pieces to prevent the source from
+ getting matched. Store it in a pointer rather than an array
+ because some compilers will just produce instructions to fill the
+ array rather than assigning a pointer to a static array. */
+char const* info_platform = "INFO" ":" "platform[" PLATFORM_ID "]";
+char const* info_arch = "INFO" ":" "arch[" ARCHITECTURE_ID "]";
+
+
+
+#if !defined(__STDC__)
+# if (defined(_MSC_VER) && !defined(__clang__)) \
+ || (defined(__ibmxl__) || defined(__IBMC__))
+# define C_DIALECT "90"
+# else
+# define C_DIALECT
+# endif
+#elif __STDC_VERSION__ >= 201000L
+# define C_DIALECT "11"
+#elif __STDC_VERSION__ >= 199901L
+# define C_DIALECT "99"
+#else
+# define C_DIALECT "90"
+#endif
+const char* info_language_dialect_default =
+ "INFO" ":" "dialect_default[" C_DIALECT "]";
+
+/*--------------------------------------------------------------------------*/
+
+#ifdef ID_VOID_MAIN
+void main() {}
+#else
+# if defined(__CLASSIC_C__)
+int main(argc, argv) int argc; char *argv[];
+# else
+int main(int argc, char* argv[])
+# endif
+{
+ int require = 0;
+ require += info_compiler[argc];
+ require += info_platform[argc];
+ require += info_arch[argc];
+#ifdef COMPILER_VERSION_MAJOR
+ require += info_version[argc];
+#endif
+#ifdef COMPILER_VERSION_INTERNAL
+ require += info_version_internal[argc];
+#endif
+#ifdef SIMULATE_ID
+ require += info_simulate[argc];
+#endif
+#ifdef SIMULATE_VERSION_MAJOR
+ require += info_simulate_version[argc];
+#endif
+#if defined(__CRAYXT_COMPUTE_LINUX_TARGET)
+ require += info_cray[argc];
+#endif
+ require += info_language_dialect_default[argc];
+ (void)argv;
+ return require;
+}
+#endif
diff --git a/yoeo/detectwebcam_IR_cpp/.CMakeFiles/3.20.0/CompilerIdC/a.out b/yoeo/detectwebcam_IR_cpp/.CMakeFiles/3.20.0/CompilerIdC/a.out
new file mode 100755
index 0000000..e82fab4
Binary files /dev/null and b/yoeo/detectwebcam_IR_cpp/.CMakeFiles/3.20.0/CompilerIdC/a.out differ
diff --git a/yoeo/detectwebcam_IR_cpp/.CMakeFiles/3.20.0/CompilerIdCXX/CMakeCXXCompilerId.cpp b/yoeo/detectwebcam_IR_cpp/.CMakeFiles/3.20.0/CompilerIdCXX/CMakeCXXCompilerId.cpp
new file mode 100644
index 0000000..356dbc6
--- /dev/null
+++ b/yoeo/detectwebcam_IR_cpp/.CMakeFiles/3.20.0/CompilerIdCXX/CMakeCXXCompilerId.cpp
@@ -0,0 +1,734 @@
+/* This source file must have a .cpp extension so that all C++ compilers
+ recognize the extension without flags. Borland does not know .cxx for
+ example. */
+#ifndef __cplusplus
+# error "A C compiler has been selected for C++."
+#endif
+
+
+/* Version number components: V=Version, R=Revision, P=Patch
+ Version date components: YYYY=Year, MM=Month, DD=Day */
+
+#if defined(__COMO__)
+# define COMPILER_ID "Comeau"
+ /* __COMO_VERSION__ = VRR */
+# define COMPILER_VERSION_MAJOR DEC(__COMO_VERSION__ / 100)
+# define COMPILER_VERSION_MINOR DEC(__COMO_VERSION__ % 100)
+
+#elif defined(__INTEL_COMPILER) || defined(__ICC)
+# define COMPILER_ID "Intel"
+# if defined(_MSC_VER)
+# define SIMULATE_ID "MSVC"
+# endif
+# if defined(__GNUC__)
+# define SIMULATE_ID "GNU"
+# endif
+ /* __INTEL_COMPILER = VRP */
+# define COMPILER_VERSION_MAJOR DEC(__INTEL_COMPILER/100)
+# define COMPILER_VERSION_MINOR DEC(__INTEL_COMPILER/10 % 10)
+# if defined(__INTEL_COMPILER_UPDATE)
+# define COMPILER_VERSION_PATCH DEC(__INTEL_COMPILER_UPDATE)
+# else
+# define COMPILER_VERSION_PATCH DEC(__INTEL_COMPILER % 10)
+# endif
+# if defined(__INTEL_COMPILER_BUILD_DATE)
+ /* __INTEL_COMPILER_BUILD_DATE = YYYYMMDD */
+# define COMPILER_VERSION_TWEAK DEC(__INTEL_COMPILER_BUILD_DATE)
+# endif
+# if defined(_MSC_VER)
+ /* _MSC_VER = VVRR */
+# define SIMULATE_VERSION_MAJOR DEC(_MSC_VER / 100)
+# define SIMULATE_VERSION_MINOR DEC(_MSC_VER % 100)
+# endif
+# if defined(__GNUC__)
+# define SIMULATE_VERSION_MAJOR DEC(__GNUC__)
+# elif defined(__GNUG__)
+# define SIMULATE_VERSION_MAJOR DEC(__GNUG__)
+# endif
+# if defined(__GNUC_MINOR__)
+# define SIMULATE_VERSION_MINOR DEC(__GNUC_MINOR__)
+# endif
+# if defined(__GNUC_PATCHLEVEL__)
+# define SIMULATE_VERSION_PATCH DEC(__GNUC_PATCHLEVEL__)
+# endif
+
+#elif (defined(__clang__) && defined(__INTEL_CLANG_COMPILER)) || defined(__INTEL_LLVM_COMPILER)
+# define COMPILER_ID "IntelLLVM"
+#if defined(_MSC_VER)
+# define SIMULATE_ID "MSVC"
+#endif
+#if defined(__GNUC__)
+# define SIMULATE_ID "GNU"
+#endif
+/* __INTEL_LLVM_COMPILER = VVVVRP prior to 2021.2.0, VVVVRRPP for 2021.2.0 and
+ * later. Look for 6 digit vs. 8 digit version number to decide encoding.
+ * VVVV is no smaller than the current year when a versio is released.
+ */
+#if __INTEL_LLVM_COMPILER < 1000000L
+# define COMPILER_VERSION_MAJOR DEC(__INTEL_LLVM_COMPILER/100)
+# define COMPILER_VERSION_MINOR DEC(__INTEL_LLVM_COMPILER/10 % 10)
+# define COMPILER_VERSION_PATCH DEC(__INTEL_LLVM_COMPILER % 10)
+#else
+# define COMPILER_VERSION_MAJOR DEC(__INTEL_LLVM_COMPILER/10000)
+# define COMPILER_VERSION_MINOR DEC(__INTEL_LLVM_COMPILER/100 % 100)
+# define COMPILER_VERSION_PATCH DEC(__INTEL_LLVM_COMPILER % 100)
+#endif
+#if defined(_MSC_VER)
+ /* _MSC_VER = VVRR */
+# define SIMULATE_VERSION_MAJOR DEC(_MSC_VER / 100)
+# define SIMULATE_VERSION_MINOR DEC(_MSC_VER % 100)
+#endif
+#if defined(__GNUC__)
+# define SIMULATE_VERSION_MAJOR DEC(__GNUC__)
+#elif defined(__GNUG__)
+# define SIMULATE_VERSION_MAJOR DEC(__GNUG__)
+#endif
+#if defined(__GNUC_MINOR__)
+# define SIMULATE_VERSION_MINOR DEC(__GNUC_MINOR__)
+#endif
+#if defined(__GNUC_PATCHLEVEL__)
+# define SIMULATE_VERSION_PATCH DEC(__GNUC_PATCHLEVEL__)
+#endif
+
+#elif defined(__PATHCC__)
+# define COMPILER_ID "PathScale"
+# define COMPILER_VERSION_MAJOR DEC(__PATHCC__)
+# define COMPILER_VERSION_MINOR DEC(__PATHCC_MINOR__)
+# if defined(__PATHCC_PATCHLEVEL__)
+# define COMPILER_VERSION_PATCH DEC(__PATHCC_PATCHLEVEL__)
+# endif
+
+#elif defined(__BORLANDC__) && defined(__CODEGEARC_VERSION__)
+# define COMPILER_ID "Embarcadero"
+# define COMPILER_VERSION_MAJOR HEX(__CODEGEARC_VERSION__>>24 & 0x00FF)
+# define COMPILER_VERSION_MINOR HEX(__CODEGEARC_VERSION__>>16 & 0x00FF)
+# define COMPILER_VERSION_PATCH DEC(__CODEGEARC_VERSION__ & 0xFFFF)
+
+#elif defined(__BORLANDC__)
+# define COMPILER_ID "Borland"
+ /* __BORLANDC__ = 0xVRR */
+# define COMPILER_VERSION_MAJOR HEX(__BORLANDC__>>8)
+# define COMPILER_VERSION_MINOR HEX(__BORLANDC__ & 0xFF)
+
+#elif defined(__WATCOMC__) && __WATCOMC__ < 1200
+# define COMPILER_ID "Watcom"
+ /* __WATCOMC__ = VVRR */
+# define COMPILER_VERSION_MAJOR DEC(__WATCOMC__ / 100)
+# define COMPILER_VERSION_MINOR DEC((__WATCOMC__ / 10) % 10)
+# if (__WATCOMC__ % 10) > 0
+# define COMPILER_VERSION_PATCH DEC(__WATCOMC__ % 10)
+# endif
+
+#elif defined(__WATCOMC__)
+# define COMPILER_ID "OpenWatcom"
+ /* __WATCOMC__ = VVRP + 1100 */
+# define COMPILER_VERSION_MAJOR DEC((__WATCOMC__ - 1100) / 100)
+# define COMPILER_VERSION_MINOR DEC((__WATCOMC__ / 10) % 10)
+# if (__WATCOMC__ % 10) > 0
+# define COMPILER_VERSION_PATCH DEC(__WATCOMC__ % 10)
+# endif
+
+#elif defined(__SUNPRO_CC)
+# define COMPILER_ID "SunPro"
+# if __SUNPRO_CC >= 0x5100
+ /* __SUNPRO_CC = 0xVRRP */
+# define COMPILER_VERSION_MAJOR HEX(__SUNPRO_CC>>12)
+# define COMPILER_VERSION_MINOR HEX(__SUNPRO_CC>>4 & 0xFF)
+# define COMPILER_VERSION_PATCH HEX(__SUNPRO_CC & 0xF)
+# else
+ /* __SUNPRO_CC = 0xVRP */
+# define COMPILER_VERSION_MAJOR HEX(__SUNPRO_CC>>8)
+# define COMPILER_VERSION_MINOR HEX(__SUNPRO_CC>>4 & 0xF)
+# define COMPILER_VERSION_PATCH HEX(__SUNPRO_CC & 0xF)
+# endif
+
+#elif defined(__HP_aCC)
+# define COMPILER_ID "HP"
+ /* __HP_aCC = VVRRPP */
+# define COMPILER_VERSION_MAJOR DEC(__HP_aCC/10000)
+# define COMPILER_VERSION_MINOR DEC(__HP_aCC/100 % 100)
+# define COMPILER_VERSION_PATCH DEC(__HP_aCC % 100)
+
+#elif defined(__DECCXX)
+# define COMPILER_ID "Compaq"
+ /* __DECCXX_VER = VVRRTPPPP */
+# define COMPILER_VERSION_MAJOR DEC(__DECCXX_VER/10000000)
+# define COMPILER_VERSION_MINOR DEC(__DECCXX_VER/100000 % 100)
+# define COMPILER_VERSION_PATCH DEC(__DECCXX_VER % 10000)
+
+#elif defined(__IBMCPP__) && defined(__COMPILER_VER__)
+# define COMPILER_ID "zOS"
+ /* __IBMCPP__ = VRP */
+# define COMPILER_VERSION_MAJOR DEC(__IBMCPP__/100)
+# define COMPILER_VERSION_MINOR DEC(__IBMCPP__/10 % 10)
+# define COMPILER_VERSION_PATCH DEC(__IBMCPP__ % 10)
+
+#elif defined(__ibmxl__) && defined(__clang__)
+# define COMPILER_ID "XLClang"
+# define COMPILER_VERSION_MAJOR DEC(__ibmxl_version__)
+# define COMPILER_VERSION_MINOR DEC(__ibmxl_release__)
+# define COMPILER_VERSION_PATCH DEC(__ibmxl_modification__)
+# define COMPILER_VERSION_TWEAK DEC(__ibmxl_ptf_fix_level__)
+
+
+#elif defined(__IBMCPP__) && !defined(__COMPILER_VER__) && __IBMCPP__ >= 800
+# define COMPILER_ID "XL"
+ /* __IBMCPP__ = VRP */
+# define COMPILER_VERSION_MAJOR DEC(__IBMCPP__/100)
+# define COMPILER_VERSION_MINOR DEC(__IBMCPP__/10 % 10)
+# define COMPILER_VERSION_PATCH DEC(__IBMCPP__ % 10)
+
+#elif defined(__IBMCPP__) && !defined(__COMPILER_VER__) && __IBMCPP__ < 800
+# define COMPILER_ID "VisualAge"
+ /* __IBMCPP__ = VRP */
+# define COMPILER_VERSION_MAJOR DEC(__IBMCPP__/100)
+# define COMPILER_VERSION_MINOR DEC(__IBMCPP__/10 % 10)
+# define COMPILER_VERSION_PATCH DEC(__IBMCPP__ % 10)
+
+#elif defined(__NVCOMPILER)
+# define COMPILER_ID "NVHPC"
+# define COMPILER_VERSION_MAJOR DEC(__NVCOMPILER_MAJOR__)
+# define COMPILER_VERSION_MINOR DEC(__NVCOMPILER_MINOR__)
+# if defined(__NVCOMPILER_PATCHLEVEL__)
+# define COMPILER_VERSION_PATCH DEC(__NVCOMPILER_PATCHLEVEL__)
+# endif
+
+#elif defined(__PGI)
+# define COMPILER_ID "PGI"
+# define COMPILER_VERSION_MAJOR DEC(__PGIC__)
+# define COMPILER_VERSION_MINOR DEC(__PGIC_MINOR__)
+# if defined(__PGIC_PATCHLEVEL__)
+# define COMPILER_VERSION_PATCH DEC(__PGIC_PATCHLEVEL__)
+# endif
+
+#elif defined(_CRAYC)
+# define COMPILER_ID "Cray"
+# define COMPILER_VERSION_MAJOR DEC(_RELEASE_MAJOR)
+# define COMPILER_VERSION_MINOR DEC(_RELEASE_MINOR)
+
+#elif defined(__TI_COMPILER_VERSION__)
+# define COMPILER_ID "TI"
+ /* __TI_COMPILER_VERSION__ = VVVRRRPPP */
+# define COMPILER_VERSION_MAJOR DEC(__TI_COMPILER_VERSION__/1000000)
+# define COMPILER_VERSION_MINOR DEC(__TI_COMPILER_VERSION__/1000 % 1000)
+# define COMPILER_VERSION_PATCH DEC(__TI_COMPILER_VERSION__ % 1000)
+
+#elif defined(__FUJITSU) || defined(__FCC_VERSION) || defined(__fcc_version)
+# define COMPILER_ID "Fujitsu"
+
+#elif defined(__ghs__)
+# define COMPILER_ID "GHS"
+/* __GHS_VERSION_NUMBER = VVVVRP */
+# ifdef __GHS_VERSION_NUMBER
+# define COMPILER_VERSION_MAJOR DEC(__GHS_VERSION_NUMBER / 100)
+# define COMPILER_VERSION_MINOR DEC(__GHS_VERSION_NUMBER / 10 % 10)
+# define COMPILER_VERSION_PATCH DEC(__GHS_VERSION_NUMBER % 10)
+# endif
+
+#elif defined(__SCO_VERSION__)
+# define COMPILER_ID "SCO"
+
+#elif defined(__ARMCC_VERSION) && !defined(__clang__)
+# define COMPILER_ID "ARMCC"
+#if __ARMCC_VERSION >= 1000000
+ /* __ARMCC_VERSION = VRRPPPP */
+ # define COMPILER_VERSION_MAJOR DEC(__ARMCC_VERSION/1000000)
+ # define COMPILER_VERSION_MINOR DEC(__ARMCC_VERSION/10000 % 100)
+ # define COMPILER_VERSION_PATCH DEC(__ARMCC_VERSION % 10000)
+#else
+ /* __ARMCC_VERSION = VRPPPP */
+ # define COMPILER_VERSION_MAJOR DEC(__ARMCC_VERSION/100000)
+ # define COMPILER_VERSION_MINOR DEC(__ARMCC_VERSION/10000 % 10)
+ # define COMPILER_VERSION_PATCH DEC(__ARMCC_VERSION % 10000)
+#endif
+
+
+#elif defined(__clang__) && defined(__apple_build_version__)
+# define COMPILER_ID "AppleClang"
+# if defined(_MSC_VER)
+# define SIMULATE_ID "MSVC"
+# endif
+# define COMPILER_VERSION_MAJOR DEC(__clang_major__)
+# define COMPILER_VERSION_MINOR DEC(__clang_minor__)
+# define COMPILER_VERSION_PATCH DEC(__clang_patchlevel__)
+# if defined(_MSC_VER)
+ /* _MSC_VER = VVRR */
+# define SIMULATE_VERSION_MAJOR DEC(_MSC_VER / 100)
+# define SIMULATE_VERSION_MINOR DEC(_MSC_VER % 100)
+# endif
+# define COMPILER_VERSION_TWEAK DEC(__apple_build_version__)
+
+#elif defined(__clang__) && defined(__ARMCOMPILER_VERSION)
+# define COMPILER_ID "ARMClang"
+ # define COMPILER_VERSION_MAJOR DEC(__ARMCOMPILER_VERSION/1000000)
+ # define COMPILER_VERSION_MINOR DEC(__ARMCOMPILER_VERSION/10000 % 100)
+ # define COMPILER_VERSION_PATCH DEC(__ARMCOMPILER_VERSION % 10000)
+# define COMPILER_VERSION_INTERNAL DEC(__ARMCOMPILER_VERSION)
+
+#elif defined(__clang__)
+# define COMPILER_ID "Clang"
+# if defined(_MSC_VER)
+# define SIMULATE_ID "MSVC"
+# endif
+# define COMPILER_VERSION_MAJOR DEC(__clang_major__)
+# define COMPILER_VERSION_MINOR DEC(__clang_minor__)
+# define COMPILER_VERSION_PATCH DEC(__clang_patchlevel__)
+# if defined(_MSC_VER)
+ /* _MSC_VER = VVRR */
+# define SIMULATE_VERSION_MAJOR DEC(_MSC_VER / 100)
+# define SIMULATE_VERSION_MINOR DEC(_MSC_VER % 100)
+# endif
+
+#elif defined(__GNUC__) || defined(__GNUG__)
+# define COMPILER_ID "GNU"
+# if defined(__GNUC__)
+# define COMPILER_VERSION_MAJOR DEC(__GNUC__)
+# else
+# define COMPILER_VERSION_MAJOR DEC(__GNUG__)
+# endif
+# if defined(__GNUC_MINOR__)
+# define COMPILER_VERSION_MINOR DEC(__GNUC_MINOR__)
+# endif
+# if defined(__GNUC_PATCHLEVEL__)
+# define COMPILER_VERSION_PATCH DEC(__GNUC_PATCHLEVEL__)
+# endif
+
+#elif defined(_MSC_VER)
+# define COMPILER_ID "MSVC"
+ /* _MSC_VER = VVRR */
+# define COMPILER_VERSION_MAJOR DEC(_MSC_VER / 100)
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+Building C object CMakeFiles/cmTC_93230.dir/CheckFunctionExists.c.o
+/usr/bin/cc -DCHECK_FUNCTION_EXISTS=pthread_create -fPIE -o CMakeFiles/cmTC_93230.dir/CheckFunctionExists.c.o -c /usr/local/share/cmake-3.20/Modules/CheckFunctionExists.c
+Linking C executable cmTC_93230
+/usr/local/bin/cmake -E cmake_link_script CMakeFiles/cmTC_93230.dir/link.txt --verbose=1
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+/usr/bin/ld: cannot find -lpthreads
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+make[1]: *** [CMakeFiles/cmTC_93230.dir/build.make:99: cmTC_93230] Error 1
+make[1]: Leaving directory '/home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/yoeo/detectwebcam_IR_cpp/CMakeFiles/CMakeTmp'
+make: *** [Makefile:127: cmTC_93230/fast] Error 2
+
+
+
+Determining if the CL_VERSION_2_2 exist failed with the following output:
+Change Dir: /home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/yoeo/detectwebcam_IR_cpp/CMakeFiles/CMakeTmp
+
+Run Build Command(s):/usr/bin/make -f Makefile cmTC_925af/fast && /usr/bin/make -f CMakeFiles/cmTC_925af.dir/build.make CMakeFiles/cmTC_925af.dir/build
+make[1]: Entering directory '/home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/yoeo/detectwebcam_IR_cpp/CMakeFiles/CMakeTmp'
+Building C object CMakeFiles/cmTC_925af.dir/CheckSymbolExists.c.o
+/usr/bin/cc -fPIE -o CMakeFiles/cmTC_925af.dir/CheckSymbolExists.c.o -c /home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/yoeo/detectwebcam_IR_cpp/CMakeFiles/CMakeTmp/CheckSymbolExists.c
+/home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/yoeo/detectwebcam_IR_cpp/CMakeFiles/CMakeTmp/CheckSymbolExists.c:2:10: fatal error: OpenCL_INCLUDE_DIR-NOTFOUND/CL/cl.h: No such file or directory
+ 2 | #include
+ | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+compilation terminated.
+make[1]: *** [CMakeFiles/cmTC_925af.dir/build.make:78: CMakeFiles/cmTC_925af.dir/CheckSymbolExists.c.o] Error 1
+make[1]: Leaving directory '/home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/yoeo/detectwebcam_IR_cpp/CMakeFiles/CMakeTmp'
+make: *** [Makefile:127: cmTC_925af/fast] Error 2
+
+
+File /home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/yoeo/detectwebcam_IR_cpp/CMakeFiles/CMakeTmp/CheckSymbolExists.c:
+/* */
+#include
+
+int main(int argc, char** argv)
+{
+ (void)argv;
+#ifndef CL_VERSION_2_2
+ return ((int*)(&CL_VERSION_2_2))[argc];
+#else
+ (void)argc;
+ return 0;
+#endif
+}
+Determining if the CL_VERSION_2_1 exist failed with the following output:
+Change Dir: /home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/yoeo/detectwebcam_IR_cpp/CMakeFiles/CMakeTmp
+
+Run Build Command(s):/usr/bin/make -f Makefile cmTC_16cae/fast && /usr/bin/make -f CMakeFiles/cmTC_16cae.dir/build.make CMakeFiles/cmTC_16cae.dir/build
+make[1]: Entering directory '/home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/yoeo/detectwebcam_IR_cpp/CMakeFiles/CMakeTmp'
+Building C object CMakeFiles/cmTC_16cae.dir/CheckSymbolExists.c.o
+/usr/bin/cc -fPIE -o CMakeFiles/cmTC_16cae.dir/CheckSymbolExists.c.o -c /home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/yoeo/detectwebcam_IR_cpp/CMakeFiles/CMakeTmp/CheckSymbolExists.c
+/home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/yoeo/detectwebcam_IR_cpp/CMakeFiles/CMakeTmp/CheckSymbolExists.c:2:10: fatal error: OpenCL_INCLUDE_DIR-NOTFOUND/CL/cl.h: No such file or directory
+ 2 | #include
+ | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+compilation terminated.
+make[1]: *** [CMakeFiles/cmTC_16cae.dir/build.make:78: CMakeFiles/cmTC_16cae.dir/CheckSymbolExists.c.o] Error 1
+make[1]: Leaving directory '/home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/yoeo/detectwebcam_IR_cpp/CMakeFiles/CMakeTmp'
+make: *** [Makefile:127: cmTC_16cae/fast] Error 2
+
+
+File /home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/yoeo/detectwebcam_IR_cpp/CMakeFiles/CMakeTmp/CheckSymbolExists.c:
+/* */
+#include
+
+int main(int argc, char** argv)
+{
+ (void)argv;
+#ifndef CL_VERSION_2_1
+ return ((int*)(&CL_VERSION_2_1))[argc];
+#else
+ (void)argc;
+ return 0;
+#endif
+}
+Determining if the CL_VERSION_2_0 exist failed with the following output:
+Change Dir: /home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/yoeo/detectwebcam_IR_cpp/CMakeFiles/CMakeTmp
+
+Run Build Command(s):/usr/bin/make -f Makefile cmTC_34b47/fast && /usr/bin/make -f CMakeFiles/cmTC_34b47.dir/build.make CMakeFiles/cmTC_34b47.dir/build
+make[1]: Entering directory '/home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/yoeo/detectwebcam_IR_cpp/CMakeFiles/CMakeTmp'
+Building C object CMakeFiles/cmTC_34b47.dir/CheckSymbolExists.c.o
+/usr/bin/cc -fPIE -o CMakeFiles/cmTC_34b47.dir/CheckSymbolExists.c.o -c /home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/yoeo/detectwebcam_IR_cpp/CMakeFiles/CMakeTmp/CheckSymbolExists.c
+/home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/yoeo/detectwebcam_IR_cpp/CMakeFiles/CMakeTmp/CheckSymbolExists.c:2:10: fatal error: OpenCL_INCLUDE_DIR-NOTFOUND/CL/cl.h: No such file or directory
+ 2 | #include
+ | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+compilation terminated.
+make[1]: *** [CMakeFiles/cmTC_34b47.dir/build.make:78: CMakeFiles/cmTC_34b47.dir/CheckSymbolExists.c.o] Error 1
+make[1]: Leaving directory '/home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/yoeo/detectwebcam_IR_cpp/CMakeFiles/CMakeTmp'
+make: *** [Makefile:127: cmTC_34b47/fast] Error 2
+
+
+File /home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/yoeo/detectwebcam_IR_cpp/CMakeFiles/CMakeTmp/CheckSymbolExists.c:
+/* */
+#include
+
+int main(int argc, char** argv)
+{
+ (void)argv;
+#ifndef CL_VERSION_2_0
+ return ((int*)(&CL_VERSION_2_0))[argc];
+#else
+ (void)argc;
+ return 0;
+#endif
+}
+Determining if the CL_VERSION_1_2 exist failed with the following output:
+Change Dir: /home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/yoeo/detectwebcam_IR_cpp/CMakeFiles/CMakeTmp
+
+Run Build Command(s):/usr/bin/make -f Makefile cmTC_d3bd2/fast && /usr/bin/make -f CMakeFiles/cmTC_d3bd2.dir/build.make CMakeFiles/cmTC_d3bd2.dir/build
+make[1]: Entering directory '/home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/yoeo/detectwebcam_IR_cpp/CMakeFiles/CMakeTmp'
+Building C object CMakeFiles/cmTC_d3bd2.dir/CheckSymbolExists.c.o
+/usr/bin/cc -fPIE -o CMakeFiles/cmTC_d3bd2.dir/CheckSymbolExists.c.o -c /home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/yoeo/detectwebcam_IR_cpp/CMakeFiles/CMakeTmp/CheckSymbolExists.c
+/home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/yoeo/detectwebcam_IR_cpp/CMakeFiles/CMakeTmp/CheckSymbolExists.c:2:10: fatal error: OpenCL_INCLUDE_DIR-NOTFOUND/CL/cl.h: No such file or directory
+ 2 | #include
+ | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+compilation terminated.
+make[1]: *** [CMakeFiles/cmTC_d3bd2.dir/build.make:78: CMakeFiles/cmTC_d3bd2.dir/CheckSymbolExists.c.o] Error 1
+make[1]: Leaving directory '/home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/yoeo/detectwebcam_IR_cpp/CMakeFiles/CMakeTmp'
+make: *** [Makefile:127: cmTC_d3bd2/fast] Error 2
+
+
+File /home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/yoeo/detectwebcam_IR_cpp/CMakeFiles/CMakeTmp/CheckSymbolExists.c:
+/* */
+#include
+
+int main(int argc, char** argv)
+{
+ (void)argv;
+#ifndef CL_VERSION_1_2
+ return ((int*)(&CL_VERSION_1_2))[argc];
+#else
+ (void)argc;
+ return 0;
+#endif
+}
+Determining if the CL_VERSION_1_1 exist failed with the following output:
+Change Dir: /home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/yoeo/detectwebcam_IR_cpp/CMakeFiles/CMakeTmp
+
+Run Build Command(s):/usr/bin/make -f Makefile cmTC_c3dda/fast && /usr/bin/make -f CMakeFiles/cmTC_c3dda.dir/build.make CMakeFiles/cmTC_c3dda.dir/build
+make[1]: Entering directory '/home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/yoeo/detectwebcam_IR_cpp/CMakeFiles/CMakeTmp'
+Building C object CMakeFiles/cmTC_c3dda.dir/CheckSymbolExists.c.o
+/usr/bin/cc -fPIE -o CMakeFiles/cmTC_c3dda.dir/CheckSymbolExists.c.o -c /home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/yoeo/detectwebcam_IR_cpp/CMakeFiles/CMakeTmp/CheckSymbolExists.c
+/home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/yoeo/detectwebcam_IR_cpp/CMakeFiles/CMakeTmp/CheckSymbolExists.c:2:10: fatal error: OpenCL_INCLUDE_DIR-NOTFOUND/CL/cl.h: No such file or directory
+ 2 | #include
+ | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+compilation terminated.
+make[1]: *** [CMakeFiles/cmTC_c3dda.dir/build.make:78: CMakeFiles/cmTC_c3dda.dir/CheckSymbolExists.c.o] Error 1
+make[1]: Leaving directory '/home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/yoeo/detectwebcam_IR_cpp/CMakeFiles/CMakeTmp'
+make: *** [Makefile:127: cmTC_c3dda/fast] Error 2
+
+
+File /home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/yoeo/detectwebcam_IR_cpp/CMakeFiles/CMakeTmp/CheckSymbolExists.c:
+/* */
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+
+int main(int argc, char** argv)
+{
+ (void)argv;
+#ifndef CL_VERSION_1_1
+ return ((int*)(&CL_VERSION_1_1))[argc];
+#else
+ (void)argc;
+ return 0;
+#endif
+}
+Determining if the CL_VERSION_1_0 exist failed with the following output:
+Change Dir: /home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/yoeo/detectwebcam_IR_cpp/CMakeFiles/CMakeTmp
+
+Run Build Command(s):/usr/bin/make -f Makefile cmTC_e0de1/fast && /usr/bin/make -f CMakeFiles/cmTC_e0de1.dir/build.make CMakeFiles/cmTC_e0de1.dir/build
+make[1]: Entering directory '/home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/yoeo/detectwebcam_IR_cpp/CMakeFiles/CMakeTmp'
+Building C object CMakeFiles/cmTC_e0de1.dir/CheckSymbolExists.c.o
+/usr/bin/cc -fPIE -o CMakeFiles/cmTC_e0de1.dir/CheckSymbolExists.c.o -c /home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/yoeo/detectwebcam_IR_cpp/CMakeFiles/CMakeTmp/CheckSymbolExists.c
+/home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/yoeo/detectwebcam_IR_cpp/CMakeFiles/CMakeTmp/CheckSymbolExists.c:2:10: fatal error: OpenCL_INCLUDE_DIR-NOTFOUND/CL/cl.h: No such file or directory
+ 2 | #include
+ | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+compilation terminated.
+make[1]: *** [CMakeFiles/cmTC_e0de1.dir/build.make:78: CMakeFiles/cmTC_e0de1.dir/CheckSymbolExists.c.o] Error 1
+make[1]: Leaving directory '/home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/yoeo/detectwebcam_IR_cpp/CMakeFiles/CMakeTmp'
+make: *** [Makefile:127: cmTC_e0de1/fast] Error 2
+
+
+File /home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/yoeo/detectwebcam_IR_cpp/CMakeFiles/CMakeTmp/CheckSymbolExists.c:
+/* */
+#include
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+int main(int argc, char** argv)
+{
+ (void)argv;
+#ifndef CL_VERSION_1_0
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+ (void)argc;
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+Compiler: /usr/bin/cc
+Build flags:
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+The output was:
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+
+Compilation of the C compiler identification source "CMakeCCompilerId.c" produced "a.out"
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+The output was:
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+Compilation of the CXX compiler identification source "CMakeCXXCompilerId.cpp" produced "a.out"
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+
+Detecting C compiler ABI info compiled with the following output:
+Change Dir: /home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/yoeo/detectwebcam_IR_cpp/CMakeFiles/CMakeTmp
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+Run Build Command(s):/usr/bin/make -f Makefile cmTC_ea2c4/fast && /usr/bin/make -f CMakeFiles/cmTC_ea2c4.dir/build.make CMakeFiles/cmTC_ea2c4.dir/build
+make[1]: Entering directory '/home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/yoeo/detectwebcam_IR_cpp/CMakeFiles/CMakeTmp'
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diff --git a/yoeo/detectwebcam_IR_cpp/CMakeLists.txt b/yoeo/detectwebcam_IR_cpp/CMakeLists.txt
new file mode 100644
index 0000000..ba933bc
--- /dev/null
+++ b/yoeo/detectwebcam_IR_cpp/CMakeLists.txt
@@ -0,0 +1,21 @@
+# Copyright (C) 2018-2022 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+#
+cmake_minimum_required(VERSION 3.10)
+project(detectwebcam_IR_cpp)
+set (TARGET_NAME "detectwebcam_IR_cpp")
+
+# Create named folders for the sources within the .vcproj
+# Empty name lists them directly under the .vcproj
+
+# Find OpenCV components if exist
+find_package(OpenCV REQUIRED)
+message(${OpenCV_DIR})
+find_package(OpenVINO REQUIRED PATHS /opt/intel/openvino_2022.3.0/runtime/)
+message(${OpenVINO_DIR})
+
+add_executable(${TARGET_NAME} detectwebcam_IR.cpp)
+
+target_link_directories(${TARGET_NAME} PRIVATE ${CMAKE_SOURCE_DIR}/bin)
+
+target_link_libraries(${TARGET_NAME} PRIVATE openvino::runtime PRIVATE ${OpenCV_LIBRARIES} ie_samples_utils)
diff --git a/yoeo/detectwebcam_IR_cpp/MnistUbyte.cpp b/yoeo/detectwebcam_IR_cpp/MnistUbyte.cpp
new file mode 100644
index 0000000..182ef99
--- /dev/null
+++ b/yoeo/detectwebcam_IR_cpp/MnistUbyte.cpp
@@ -0,0 +1,66 @@
+// Copyright (C) 2018-2022 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+// clang-format off
+#include
+#include
+#include
+
+#include "MnistUbyte.h"
+// clang-format on
+
+using namespace FormatReader;
+
+int MnistUbyte::reverseInt(int i) {
+ unsigned char ch1, ch2, ch3, ch4;
+ ch1 = (unsigned char)(i & 255);
+ ch2 = (unsigned char)((i >> 8) & 255);
+ ch3 = (unsigned char)((i >> 16) & 255);
+ ch4 = (unsigned char)((i >> 24) & 255);
+ return (static_cast(ch1) << 24) + (static_cast(ch2) << 16) + (static_cast(ch3) << 8) + ch4;
+}
+
+MnistUbyte::MnistUbyte(const std::string& filename) {
+ std::ifstream file(filename, std::ios::binary);
+ if (!file.is_open()) {
+ return;
+ }
+ int magic_number = 0;
+ int number_of_images = 0;
+ int n_rows = 0;
+ int n_cols = 0;
+ file.read(reinterpret_cast(&magic_number), sizeof(magic_number));
+ magic_number = reverseInt(magic_number);
+ if (magic_number != 2051) {
+ return;
+ }
+ file.read(reinterpret_cast(&number_of_images), sizeof(number_of_images));
+ number_of_images = reverseInt(number_of_images);
+ file.read(reinterpret_cast(&n_rows), sizeof(n_rows));
+ n_rows = reverseInt(n_rows);
+ _height = (size_t)n_rows;
+ file.read(reinterpret_cast(&n_cols), sizeof(n_cols));
+ n_cols = reverseInt(n_cols);
+ _width = (size_t)n_cols;
+ if (number_of_images > 1) {
+ std::cout << "[MNIST] Warning: number_of_images in mnist file equals " << number_of_images
+ << ". Only a first image will be read." << std::endl;
+ }
+
+ size_t size = _width * _height * 1;
+
+ _data.reset(new unsigned char[size], std::default_delete());
+ size_t count = 0;
+ if (0 < number_of_images) {
+ for (int r = 0; r < n_rows; ++r) {
+ for (int c = 0; c < n_cols; ++c) {
+ unsigned char temp = 0;
+ file.read(reinterpret_cast(&temp), sizeof(temp));
+ _data.get()[count++] = temp;
+ }
+ }
+ }
+
+ file.close();
+}
diff --git a/yoeo/detectwebcam_IR_cpp/bin/benchmark_app b/yoeo/detectwebcam_IR_cpp/bin/benchmark_app
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diff --git a/yoeo/detectwebcam_IR_cpp/bin/hello_query_device b/yoeo/detectwebcam_IR_cpp/bin/hello_query_device
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diff --git a/yoeo/detectwebcam_IR_cpp/bin/hello_reshape_ssd b/yoeo/detectwebcam_IR_cpp/bin/hello_reshape_ssd
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diff --git a/yoeo/detectwebcam_IR_cpp/bin/libcnpy.a b/yoeo/detectwebcam_IR_cpp/bin/libcnpy.a
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diff --git a/yoeo/detectwebcam_IR_cpp/bin/libformat_reader.so b/yoeo/detectwebcam_IR_cpp/bin/libformat_reader.so
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diff --git a/yoeo/detectwebcam_IR_cpp/bin/libie_samples_utils.a b/yoeo/detectwebcam_IR_cpp/bin/libie_samples_utils.a
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index 0000000..6539cd0
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diff --git a/yoeo/detectwebcam_IR_cpp/bin/model_creation_sample b/yoeo/detectwebcam_IR_cpp/bin/model_creation_sample
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index 0000000..b140a73
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diff --git a/yoeo/detectwebcam_IR_cpp/bin/speech_sample b/yoeo/detectwebcam_IR_cpp/bin/speech_sample
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index 0000000..6ef31e3
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diff --git a/yoeo/detectwebcam_IR_cpp/bin/sync_benchmark b/yoeo/detectwebcam_IR_cpp/bin/sync_benchmark
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index 0000000..4d4c5b5
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diff --git a/yoeo/detectwebcam_IR_cpp/bin/throughput_benchmark b/yoeo/detectwebcam_IR_cpp/bin/throughput_benchmark
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index 0000000..92d038e
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diff --git a/yoeo/detectwebcam_IR_cpp/bmp.cpp b/yoeo/detectwebcam_IR_cpp/bmp.cpp
new file mode 100644
index 0000000..240d13f
--- /dev/null
+++ b/yoeo/detectwebcam_IR_cpp/bmp.cpp
@@ -0,0 +1,64 @@
+// Copyright (C) 2018-2022 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+// clang-format off
+#include
+#include
+
+#include "bmp.h"
+// clang-format on
+
+using namespace std;
+using namespace FormatReader;
+
+BitMap::BitMap(const string& filename) {
+ BmpHeader header;
+ BmpInfoHeader infoHeader;
+
+ ifstream input(filename, ios::binary);
+ if (!input) {
+ return;
+ }
+
+ input.read(reinterpret_cast(&header.type), 2);
+
+ if (header.type != 'M' * 256 + 'B') {
+ std::cerr << "[BMP] file is not bmp type\n";
+ return;
+ }
+
+ input.read(reinterpret_cast(&header.size), 4);
+ input.read(reinterpret_cast(&header.reserved), 4);
+ input.read(reinterpret_cast(&header.offset), 4);
+
+ input.read(reinterpret_cast(&infoHeader), sizeof(BmpInfoHeader));
+
+ bool rowsReversed = infoHeader.height < 0;
+ _width = infoHeader.width;
+ _height = abs(infoHeader.height);
+
+ if (infoHeader.bits != 24) {
+ cerr << "[BMP] 24bpp only supported. But input has:" << infoHeader.bits << "\n";
+ return;
+ }
+
+ if (infoHeader.compression != 0) {
+ cerr << "[BMP] compression not supported\n";
+ }
+
+ int padSize = _width & 3;
+ char pad[3];
+ size_t size = _width * _height * 3;
+
+ _data.reset(new unsigned char[size], std::default_delete());
+
+ input.seekg(header.offset, ios::beg);
+
+ // reading by rows in invert vertically
+ for (uint32_t i = 0; i < _height; i++) {
+ uint32_t storeAt = rowsReversed ? i : (uint32_t)_height - 1 - i;
+ input.read(reinterpret_cast(_data.get()) + _width * 3 * storeAt, _width * 3);
+ input.read(pad, padSize);
+ }
+}
diff --git a/yoeo/detectwebcam_IR_cpp/detectwebcam_IR b/yoeo/detectwebcam_IR_cpp/detectwebcam_IR
new file mode 100755
index 0000000..fcacc42
Binary files /dev/null and b/yoeo/detectwebcam_IR_cpp/detectwebcam_IR differ
diff --git a/yoeo/detectwebcam_IR_cpp/detectwebcam_IR.cpp b/yoeo/detectwebcam_IR_cpp/detectwebcam_IR.cpp
new file mode 100644
index 0000000..c7eb1ce
--- /dev/null
+++ b/yoeo/detectwebcam_IR_cpp/detectwebcam_IR.cpp
@@ -0,0 +1,227 @@
+#include "openvino/openvino.hpp"
+#include
+#include
+#include
+#include
+#include
+#include
+// #include
+#include
+#include "include/format_reader_ptr.h"
+
+#include
+#include
+#define CLOCK std::chrono::steady_clock
+#define CLOCK_CAST std::chrono::duration_cast
+
+int main()
+{
+ auto xml;
+
+ ov::Core core;
+
+ // PROVERKA INPUT and OUTPUT
+ // std::shared_ptr model = core.read_model(xml);
+ // std::cout << model->input(0).get_partial_shape() << std::endl;
+ // std::cout << model->output(0).get_partial_shape() << std::endl;
+ // std::cout << model->output(1).get_partial_shape() << std::endl;
+
+ std::shared_ptr net = core.read_model(xml); // net = ie.ReadNetwork(model_path);
+ ov::preprocess::PrePostProcessor ppp(net);
+ ov::preprocess::InputInfo& input = ppp.input(0); // inputs = net.getInputsInfo();
+ auto input_shape = net->input(0).get_partial_shape();
+ auto output1_shape = net->output(0).get_partial_shape();
+ // std::cout << net->input(0).get_partial_shape() << output1_shape << std::endl;
+ // auto output_1 = ppp.output(0); // outputs = net.getOutputsInfo();
+ // auto output_2 = ppp.output(1); // outputs = net.getOutputsInfo();
+
+ // auto output_1 = ppp.output(0).postprocess().convert_element_type(ov::element::i32);
+ // auto output_2 = ppp.output(1).postprocess().convert_element_type(ov::element::i32);
+
+ // std::cout << output_1 << output_2 << std::endl;
+
+ input.tensor().set_element_type(ov::element::f32); // NAPISAL PODRYGOMY (NE NHWC) почему здесь другая нумерация, как это может быть правильно написанным вариантом?
+ input.model().set_layout("NCHW"); // input_data->setLayout(Layout::NCHW);
+ input.model().set_layout("NHWC");
+ // input.tensor().set_shape({1, 416, 416, 3});
+ // Model expects shape {1, 3, 480, 640}
+ // input.preprocess().convert_layout({0, 3, 1, 2});
+ // input.tensor().set_element_type(ov::element::f32);
+ // input.tensor().set_color_format(ov::preprocess::ColorFormat::NV12_TWO_PLANES); // add NV12 to BGR conversion
+ // std::cout << "before converting color" << std::endl;
+ // input.preprocess().convert_color(ov::preprocess::ColorFormat::RGB); // input_data->getPreProcess().setColorFormat(ColorFormat::RGB);
+
+ // output_1.tensor().set_element_type(ov::element::f32); // output_data->setPrecision(Precision::FP32);
+ // output_2.tensor().set_element_type(ov::element::f32); // output_data->setPrecision(Precision::FP32);
+
+ net = ppp.build();
+
+ // INPUT = [1,3,H,W] OUTPUTS = OUTPUT[0], OUTPUT[1] = [1, ( 3*(H/32)*(W/32) + 3*(H/16)*(W/16) ), (5 + numof_classes)], [1, H, W]
+
+ ov::CompiledModel compiled_model = core.compile_model(net, "CPU"); // auto executable_network = ie.LoadNetwork(net, device);
+ ov::InferRequest infer_request = compiled_model.create_infer_request(); // infer_request = executable_network.CreateInferRequest();
+
+ ov::Tensor m_inputData = infer_request.get_input_tensor(0); // m_inputData = infer_request.GetBlob(inputsName);
+
+ // std::cout << "Shape of input tensor: " << m_inputData.get_shape() << std::endl;
+ // std::cout << "Type of input tensor: " << m_inputData.get_element_type() << std::endl;
+ auto m_numChannels = m_inputData.get_shape()[1];
+ auto m_inputW = m_inputData.get_shape()[3];
+ auto m_inputH = m_inputData.get_shape()[2];
+ auto m_imageSize = m_inputH * m_inputW;
+ auto data1 = m_inputData.data();
+ // std::cout << "m_numChannels: " << m_numChannels << std::endl <<"m_inputW: " << m_inputW << std::endl << "m_inputH: " << m_inputH << std::endl<< "m_imageSize: " << m_imageSize << std::endl;
+
+ // ov::element::Type input_tensor_type = ov::element::f32;
+
+
+ // cv::VideoCapture cap(0);
+ cv::VideoCapture cap("/home/ss21mipt/DIPLOMA/test_data/sahr3/video.avi");
+ cv::Mat image;
+ cv::Mat segments;
+ cv::Mat mask;
+ cv::Mat test_mask;
+ int index = 0;
+ std::chrono::steady_clock::time_point begin;
+ std::chrono::steady_clock::time_point end;
+ std::chrono::steady_clock::time_point begin_global;
+ std::chrono::steady_clock::time_point end_global;
+ auto time_preproc= 0;
+ auto t_preproc = 0;
+ auto t_preproc_max = 0;
+ auto t_preproc_min = 100;
+ auto time_infer = 0;
+ auto t_infer = 0;
+ auto t_infer_max = 0;
+ auto t_infer_min = 100;
+ auto time_postproc = 0;
+ auto t_postproc = 0;
+ auto t_postproc_max = 0;
+ auto t_postproc_min = 100;
+ // std::cout << "doshlo" << std::endl;
+ begin_global = CLOCK::now();
+ // while (cap.isOpened()){
+ while (index < 500){
+ begin = CLOCK::now();
+ cap >> image;
+ // image = cv::imread("/home/ss21mipt/DIPLOMA/IoU_tool/ground_t/frame111.jpg");
+ // std::cout << "doshlo" << std::endl;
+ if (image.empty() || !image.data) {
+ return false;
+ }
+ cv::Size scale(416, 416);
+ cv::resize(image, image, scale);
+
+ // ПЕРЕБИВАНИЕ КАРТИНКИ В ВЕКТОР
+
+
+ // std::cout << "SIZES of Mat: " << image.size[0] << " " << image.size[1] << " " << image.channels()<< std::endl;
+
+ // auto data1 = input_tensor1.data();
+ // std::shared_ptr input_data = getData(data_img);
+ // m_inputData = ov::Tensor(input_tensor_type, input_shape, input_data.get());
+ // FILLING THE DATA1
+ for (size_t row = 0; row < m_inputH; row++) {
+ for (size_t col = 0; col < m_inputW; col++) {
+ for (size_t ch = 0; ch < m_numChannels; ch++) {
+ // #ifdef NCS2
+ data1[m_imageSize * ch + row * m_inputW + col] = float(image.at(row, col)[ch]);
+
+ // #else
+ // data[m_imageSize * ch + row * m_inputW + col] = float(blob_image.at(row, col)[ch] / 255.0);
+ // #endif // NCS2
+ }
+ }
+ }
+ end = CLOCK::now();
+ time_preproc = time_preproc + (CLOCK_CAST(end - begin).count() / 1000.0);
+ t_preproc = (CLOCK_CAST(end - begin).count() / 1000.0);
+ if (t_preproc > t_preproc_max) {
+ t_preproc_max = t_preproc;
+ }
+ if (t_preproc < t_preproc_min) {
+ t_preproc_min = t_preproc;
+ }
+ std::cout << "FPS #1 PREPROCESSING: " << (CLOCK_CAST(end - begin).count() / 1000.0) << std::endl;
+
+ // data1 = &array_mat[0];
+ begin = CLOCK::now();
+ infer_request.infer();
+ std::cout << "HAHA" << std::endl;
+ end = CLOCK::now();
+ time_infer = time_infer + (CLOCK_CAST(end - begin).count() / 1000.0);
+ t_infer = (CLOCK_CAST(end - begin).count() / 1000.0);
+ if (t_infer > t_infer_max) {
+ t_infer_max = t_infer;
+ }
+ if (t_infer < t_infer_min) {
+ t_infer_min = t_infer;
+ }
+ std::cout << "FPS #2 INFERENCE: " << (CLOCK_CAST(end - begin).count() / 1000.0) << std::endl;
+
+ begin = CLOCK::now();
+
+ ov::Tensor output_tensor1 = infer_request.get_output_tensor(0);
+ ov::Tensor output_tensor2 = infer_request.get_output_tensor(1);
+
+ // IR v10 works with converted precisions (i64 -> i32)
+ auto out_data1 = output_tensor1.data();
+ auto out_data2 = output_tensor2.data();
+ auto mask_shape = output_tensor2.get_shape();
+
+ // std::cout << "SEGMENTATION MASK" << mask_shape << std::endl;
+ // std::cout << "SEGMENTATION MASK" << *out_data2 << std::endl;
+ test_mask = cv::Mat::ones(mask_shape[1], mask_shape[2], CV_8UC1);
+ // TENSOR TO MAT
+ // std::cout << test_mask.channels() << test_mask.size() << std::endl;
+ for(size_t i=0; i<(m_inputH); i++){
+ for(size_t j=0; j(i, j) = float(data1[i * m_inputW + j]);
+ // std::cout << test_mask.at(i) << std::endl;
+ if (out_data2[i*m_inputH+j] == 1) {
+ out_data2[i*m_inputH+j] = 0; // зануляем фильтр зеленого
+ }
+ test_mask.at(i*m_inputH+j) = (char)(out_data2[i*m_inputH+j]*100);
+ }
+ }
+ // end = CLOCK::now();
+ // std::cout << "FPS #2 INTO FINAL MASK " << (CLOCK_CAST(end - begin).count() / 1000.0) << std::endl;
+
+ // begin = CLOCK::now();
+ // auto segments = tensorToMat(output_tensor2);
+ // cv::imshow("webcam", image);
+ cv::resize(test_mask, test_mask, cv::Size(720,540));
+ // cv::imshow("YOEO_segmentation", test_mask);
+ // std::cout << "doshlo" << mask << std::endl;
+ // if(cv::waitKey(30)>=0)
+ // break;
+ // std::cout << "Allo blyat " << std::endl;
+ // cv::imwrite("alpha.png", mask);
+ // break;
+ index++;
+ end = CLOCK::now();
+ time_postproc = time_postproc + (CLOCK_CAST(end - begin).count() / 1000.0);
+ t_postproc = (CLOCK_CAST(end - begin).count() / 1000.0);
+ if (t_postproc > t_postproc_max) {
+ t_postproc_max = t_postproc;
+ }
+ if (t_postproc < t_postproc_min) {
+ t_postproc_min = t_postproc;
+ }
+ std::cout << "FPS #3 POSTPROCESSING " << (CLOCK_CAST(end - begin).count() / 1000.0) << std::endl;
+ std::cout << "index = " << index << std::endl;
+ }
+ // cv::imwrite("/home/ss21mipt/DIPLOMA/IoU_tool/YOEO_segmentation.jpg", test_mask);
+ end_global = CLOCK::now();
+ std::cout << "AVERAGE FPS " << (index * 1.0 / (CLOCK_CAST(end_global - begin_global).count() / 1000000.0)) << std::endl;
+ std::cout << "AVERAGE PREPROCESSING FPS: " << index*1.0/(time_preproc / 1000.0) << std::endl;
+ std::cout << "AVERAGE INFERENCE FPS: " << index*1.0/(time_infer / 1000.0) << std::endl;
+ std::cout << "AVERAGE POSTPROCESSING FPS: " << index*1.0/(time_postproc / 1000.0) << std::endl;
+
+ std::cout << "AVERAGE PREPROCESSING time: " << ((time_preproc) / (index*1.0)) << " in range [" << t_preproc_min << ", " << t_preproc_max << "]" << std::endl;
+ std::cout << "AVERAGE INFERENCE time: " << ((time_infer) / (index*1.0)) << " in range [" << t_infer_min << ", " << t_infer_max << "]" << std::endl;
+ std::cout << "AVERAGE POSTPROCESSING time: " << ((time_postproc) / (index*1.0)) << " in range [" << t_postproc_min << ", " << t_postproc_max << "]" << std::endl;
+ cv::destroyWindow("YOEO_segmentation");
+ // cv::destroyWindow("webcam");
+
+}
\ No newline at end of file
diff --git a/yoeo/detectwebcam_IR_cpp/format_reader.cpp b/yoeo/detectwebcam_IR_cpp/format_reader.cpp
new file mode 100644
index 0000000..c2dfb30
--- /dev/null
+++ b/yoeo/detectwebcam_IR_cpp/format_reader.cpp
@@ -0,0 +1,44 @@
+// Copyright (C) 2018-2022 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include
+
+// clang-format off
+#include "bmp.h"
+#include "MnistUbyte.h"
+#include "yuv_nv12.h"
+#include "opencv_wrapper.h"
+#include "format_reader.h"
+// clang-format on
+
+using namespace FormatReader;
+
+std::vector Registry::_data;
+
+Register MnistUbyte::reg;
+Register YUV_NV12::reg;
+#ifdef USE_OPENCV
+Register OCVReader::reg;
+#else
+Register BitMap::reg;
+#endif
+
+Reader* Registry::CreateReader(const char* filename) {
+ for (auto maker : _data) {
+ Reader* ol = maker(filename);
+ if (ol != nullptr && ol->size() != 0)
+ return ol;
+ if (ol != nullptr)
+ delete ol;
+ }
+ return nullptr;
+}
+
+void Registry::RegisterReader(CreatorFunction f) {
+ _data.push_back(f);
+}
+
+FORMAT_READER_API(Reader*) CreateFormatReader(const char* filename) {
+ return Registry::CreateReader(filename);
+}
diff --git a/yoeo/detectwebcam_IR_cpp/include/MnistUbyte.h b/yoeo/detectwebcam_IR_cpp/include/MnistUbyte.h
new file mode 100644
index 0000000..0e03355
--- /dev/null
+++ b/yoeo/detectwebcam_IR_cpp/include/MnistUbyte.h
@@ -0,0 +1,55 @@
+// Copyright (C) 2018-2022 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+/**
+ * \brief Mnist reader
+ * \file MnistUbyte.h
+ */
+#pragma once
+
+#include
+#include
+
+// clang-format off
+#include "format_reader.h"
+#include "register.h"
+// clang-format on
+
+namespace FormatReader {
+/**
+ * \class MnistUbyte
+ * \brief Reader for mnist db files
+ */
+class MnistUbyte : public Reader {
+private:
+ int reverseInt(int i);
+
+ static Register reg;
+
+public:
+ /**
+ * \brief Constructor of Mnist reader
+ * @param filename - path to input data
+ * @return MnistUbyte reader object
+ */
+ explicit MnistUbyte(const std::string& filename);
+ virtual ~MnistUbyte() {}
+
+ /**
+ * \brief Get size
+ * @return size
+ */
+ size_t size() const override {
+ return _width * _height * 1;
+ }
+
+ std::shared_ptr getData(size_t width, size_t height) override {
+ if ((width * height != 0) && (_width * _height != width * height)) {
+ std::cout << "[ WARNING ] Image won't be resized! Please use OpenCV.\n";
+ return nullptr;
+ }
+ return _data;
+ }
+};
+} // namespace FormatReader
diff --git a/yoeo/detectwebcam_IR_cpp/include/bmp.h b/yoeo/detectwebcam_IR_cpp/include/bmp.h
new file mode 100644
index 0000000..8141fda
--- /dev/null
+++ b/yoeo/detectwebcam_IR_cpp/include/bmp.h
@@ -0,0 +1,72 @@
+// Copyright (C) 2018-2022 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+/**
+ * \brief BMP reader
+ * \file bmp.h
+ */
+#pragma once
+
+#include
+#include
+
+// clang-format off
+#include "format_reader.h"
+#include "register.h"
+// clang-format on
+
+namespace FormatReader {
+/**
+ * \class BitMap
+ * \brief Reader for bmp files
+ */
+class BitMap : public Reader {
+private:
+ static Register reg;
+
+ typedef struct BmpHeaderType {
+ unsigned short type = 0u; /* Magic identifier */
+ unsigned int size = 0u; /* File size in bytes */
+ unsigned int reserved = 0u;
+ unsigned int offset = 0u; /* Offset to image data, bytes */
+ } BmpHeader;
+
+ typedef struct BmpInfoHeaderType {
+ unsigned int size = 0u; /* Header size in bytes */
+ int width = 0, height = 0; /* Width and height of image */
+ unsigned short planes = 0u; /* Number of colour planes */
+ unsigned short bits = 0u; /* Bits per pixel */
+ unsigned int compression = 0u; /* Compression type */
+ unsigned int imagesize = 0u; /* Image size in bytes */
+ int xresolution = 0, yresolution = 0; /* Pixels per meter */
+ unsigned int ncolours = 0u; /* Number of colours */
+ unsigned int importantcolours = 0u; /* Important colours */
+ } BmpInfoHeader;
+
+public:
+ /**
+ * \brief Constructor of BMP reader
+ * @param filename - path to input data
+ * @return BitMap reader object
+ */
+ explicit BitMap(const std::string& filename);
+ virtual ~BitMap() {}
+
+ /**
+ * \brief Get size
+ * @return size
+ */
+ size_t size() const override {
+ return _width * _height * 3;
+ }
+
+ std::shared_ptr getData(size_t width, size_t height) override {
+ if ((width * height != 0) && (_width * _height != width * height)) {
+ std::cout << "[ WARNING ] Image won't be resized! Please use OpenCV.\n";
+ return nullptr;
+ }
+ return _data;
+ }
+};
+} // namespace FormatReader
diff --git a/yoeo/detectwebcam_IR_cpp/include/format_reader.h b/yoeo/detectwebcam_IR_cpp/include/format_reader.h
new file mode 100644
index 0000000..bf6b473
--- /dev/null
+++ b/yoeo/detectwebcam_IR_cpp/include/format_reader.h
@@ -0,0 +1,84 @@
+// Copyright (C) 2018-2022 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+/**
+ * \brief Format reader abstract class implementation
+ * \file format_reader.h
+ */
+#pragma once
+
+#include
+#include
+#include
+#include
+
+#if defined(_WIN32)
+# ifdef IMPLEMENT_FORMAT_READER
+# define FORMAT_READER_API(type) extern "C" __declspec(dllexport) type
+# else
+# define FORMAT_READER_API(type) extern "C" type
+# endif
+#elif (__GNUC__ >= 4)
+# ifdef IMPLEMENT_FORMAT_READER
+# define FORMAT_READER_API(type) extern "C" __attribute__((visibility("default"))) type
+# else
+# define FORMAT_READER_API(type) extern "C" type
+# endif
+#else
+# define FORMAT_READER_API(TYPE) extern "C" TYPE
+#endif
+
+namespace FormatReader {
+/**
+ * \class FormatReader
+ * \brief This is an abstract class for reading input data
+ */
+class Reader {
+protected:
+ /// \brief height
+ size_t _height = 0;
+ /// \brief width
+ size_t _width = 0;
+ /// \brief data
+ std::shared_ptr _data;
+
+public:
+ virtual ~Reader() = default;
+
+ /**
+ * \brief Get width
+ * @return width
+ */
+ size_t width() const {
+ return _width;
+ }
+
+ /**
+ * \brief Get height
+ * @return height
+ */
+ size_t height() const {
+ return _height;
+ }
+
+ /**
+ * \brief Get input data ptr
+ * @return shared pointer with input data
+ * @In case of using OpenCV, parameters width and height will be used for image resizing
+ */
+ virtual std::shared_ptr getData(size_t width = 0, size_t height = 0) = 0;
+
+ /**
+ * \brief Get size
+ * @return size
+ */
+ virtual size_t size() const = 0;
+};
+} // namespace FormatReader
+
+/**
+ * \brief Function for create reader
+ * @return FormatReader pointer
+ */
+FORMAT_READER_API(FormatReader::Reader*) CreateFormatReader(const char* filename);
diff --git a/yoeo/detectwebcam_IR_cpp/include/format_reader_ptr.h b/yoeo/detectwebcam_IR_cpp/include/format_reader_ptr.h
new file mode 100644
index 0000000..eb9bf8e
--- /dev/null
+++ b/yoeo/detectwebcam_IR_cpp/include/format_reader_ptr.h
@@ -0,0 +1,43 @@
+// Copyright (C) 2018-2022 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+/**
+ * \brief Implementation of smart pointer for Reader class
+ * \file format_reader_ptr.h
+ */
+#pragma once
+
+#include
+#include
+
+#include "format_reader.h"
+
+namespace FormatReader {
+class ReaderPtr {
+public:
+ explicit ReaderPtr(const char* imageName) : reader(CreateFormatReader(imageName)) {}
+ /**
+ * @brief dereference operator overload
+ * @return Reader
+ */
+ Reader* operator->() const noexcept {
+ return reader.get();
+ }
+
+ /**
+ * @brief dereference operator overload
+ * @return Reader
+ */
+ Reader* operator*() const noexcept {
+ return reader.get();
+ }
+
+ Reader* get() {
+ return reader.get();
+ }
+
+protected:
+ std::unique_ptr reader;
+};
+} // namespace FormatReader
diff --git a/yoeo/detectwebcam_IR_cpp/include/opencv_wrapper.h b/yoeo/detectwebcam_IR_cpp/include/opencv_wrapper.h
new file mode 100644
index 0000000..d97084b
--- /dev/null
+++ b/yoeo/detectwebcam_IR_cpp/include/opencv_wrapper.h
@@ -0,0 +1,53 @@
+// Copyright (C) 2018-2022 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+/**
+ * \brief Image reader
+ * \file opencv_wrapper.h
+ */
+#pragma once
+
+#ifdef USE_OPENCV
+# include
+# include
+
+// clang-format off
+# include
+
+# include "format_reader.h"
+# include "register.h"
+// clang-format on
+
+namespace FormatReader {
+/**
+ * \class OCVMAT
+ * \brief OpenCV Wrapper
+ */
+class OCVReader : public Reader {
+private:
+ cv::Mat img;
+ size_t _size;
+ static Register reg;
+
+public:
+ /**
+ * \brief Constructor of BMP reader
+ * @param filename - path to input data
+ * @return BitMap reader object
+ */
+ explicit OCVReader(const std::string& filename);
+ virtual ~OCVReader() {}
+
+ /**
+ * \brief Get size
+ * @return size
+ */
+ size_t size() const override {
+ return _size;
+ }
+
+ std::shared_ptr getData(size_t width, size_t height) override;
+};
+} // namespace FormatReader
+#endif
\ No newline at end of file
diff --git a/yoeo/detectwebcam_IR_cpp/include/register.h b/yoeo/detectwebcam_IR_cpp/include/register.h
new file mode 100644
index 0000000..781eca3
--- /dev/null
+++ b/yoeo/detectwebcam_IR_cpp/include/register.h
@@ -0,0 +1,58 @@
+// Copyright (C) 2018-2022 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+/**
+ * \brief Register for readers
+ * \file register.h
+ */
+#pragma once
+
+#include
+#include
+#include
+
+#include "format_reader.h"
+
+namespace FormatReader {
+/**
+ * \class Registry
+ * \brief Create reader from fabric
+ */
+class Registry {
+private:
+ typedef std::function CreatorFunction;
+ static std::vector _data;
+
+public:
+ /**
+ * \brief Create reader
+ * @param filename - path to input data
+ * @return Reader for input data or nullptr
+ */
+ static Reader* CreateReader(const char* filename);
+
+ /**
+ * \brief Registers reader in fabric
+ * @param f - a creation function
+ */
+ static void RegisterReader(CreatorFunction f);
+};
+
+/**
+ * \class Register
+ * \brief Registers reader in fabric
+ */
+template
+class Register {
+public:
+ /**
+ * \brief Constructor creates creation function for fabric
+ * @return Register object
+ */
+ Register() {
+ Registry::RegisterReader([](const std::string& filename) -> Reader* {
+ return new To(filename);
+ });
+ }
+};
+} // namespace FormatReader
diff --git a/yoeo/detectwebcam_IR_cpp/include/yuv_nv12.h b/yoeo/detectwebcam_IR_cpp/include/yuv_nv12.h
new file mode 100644
index 0000000..0dbeb69
--- /dev/null
+++ b/yoeo/detectwebcam_IR_cpp/include/yuv_nv12.h
@@ -0,0 +1,54 @@
+// Copyright (C) 2018-2022 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+/**
+ * \brief YUV NV12 reader
+ * \file yuv_nv12.h
+ */
+#pragma once
+
+#include
+#include
+
+// clang-format off
+#include "format_reader.h"
+#include "register.h"
+// clang-format on
+
+namespace FormatReader {
+/**
+ * \class YUV_NV12
+ * \brief Reader for YUV NV12 files
+ */
+class YUV_NV12 : public Reader {
+private:
+ static Register reg;
+ size_t _size = 0;
+
+public:
+ /**
+ * \brief Constructor of YUV NV12 reader
+ * @param filename - path to input data
+ * @return YUV_NV12 reader object
+ */
+ explicit YUV_NV12(const std::string& filename);
+ virtual ~YUV_NV12() {}
+
+ /**
+ * \brief Get size
+ * @return size
+ */
+ size_t size() const override {
+ return _size;
+ }
+
+ std::shared_ptr getData(size_t width, size_t height) override {
+ if ((width * height * 3 / 2 != size())) {
+ std::cout << "Image dimensions not match with NV12 file size \n";
+ return nullptr;
+ }
+ return _data;
+ }
+};
+} // namespace FormatReader
diff --git a/yoeo/detectwebcam_IR_cpp/opencv_wrapper.cpp b/yoeo/detectwebcam_IR_cpp/opencv_wrapper.cpp
new file mode 100644
index 0000000..c83a9d2
--- /dev/null
+++ b/yoeo/detectwebcam_IR_cpp/opencv_wrapper.cpp
@@ -0,0 +1,53 @@
+// Copyright (C) 2018-2022 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#ifdef USE_OPENCV
+# include
+# include
+
+// clang-format off
+# include
+
+# include "samples/slog.hpp"
+# include "opencv_wrapper.h"
+// clang-format on
+
+using namespace std;
+using namespace FormatReader;
+
+OCVReader::OCVReader(const string& filename) {
+ img = cv::imread(filename);
+ _size = 0;
+
+ if (img.empty()) {
+ return;
+ }
+
+ _size = img.size().width * img.size().height * img.channels();
+ _width = img.size().width;
+ _height = img.size().height;
+}
+
+std::shared_ptr OCVReader::getData(size_t width = 0, size_t height = 0) {
+ if (width == 0)
+ width = img.cols;
+
+ if (height == 0)
+ height = img.rows;
+
+ size_t size = width * height * img.channels();
+ _data.reset(new unsigned char[size], std::default_delete());
+
+ cv::Mat resized(cv::Size(width, height), img.type(), _data.get());
+
+ if (width != img.cols || height != img.rows) {
+ slog::warn << "Image is resized from (" << img.cols << ", " << img.rows << ") to (" << width << ", " << height
+ << ")" << slog::endl;
+ }
+ // cv::resize() just copy data to output image if sizes are the same
+ cv::resize(img, resized, cv::Size(width, height));
+
+ return _data;
+}
+#endif
diff --git a/yoeo/detectwebcam_IR_cpp/yuv_nv12.cpp b/yoeo/detectwebcam_IR_cpp/yuv_nv12.cpp
new file mode 100644
index 0000000..f25c5cb
--- /dev/null
+++ b/yoeo/detectwebcam_IR_cpp/yuv_nv12.cpp
@@ -0,0 +1,36 @@
+// Copyright (C) 2018-2022 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+// clang-format off
+#include
+#include
+#include
+
+#include "yuv_nv12.h"
+// clang-format on
+
+using namespace FormatReader;
+
+YUV_NV12::YUV_NV12(const std::string& filename) {
+ auto pos = filename.rfind('.');
+ if (pos == std::string::npos)
+ return;
+ if (filename.substr(pos + 1) != "yuv")
+ return;
+
+ std::ifstream file(filename, std::ios::binary);
+ if (!file.is_open()) {
+ return;
+ }
+
+ file.seekg(0, file.end);
+ _size = file.tellg();
+ file.seekg(0, file.beg);
+
+ _data.reset(new unsigned char[_size], std::default_delete());
+
+ file.read(reinterpret_cast(_data.get()), _size);
+
+ file.close();
+}
diff --git a/yoeo/detectwebcam_cpp/CMakeLists.txt b/yoeo/detectwebcam_cpp/CMakeLists.txt
new file mode 100644
index 0000000..7a58318
--- /dev/null
+++ b/yoeo/detectwebcam_cpp/CMakeLists.txt
@@ -0,0 +1,16 @@
+# cmake_minimum_required(VERSION 3.20)
+cmake_minimum_required(VERSION 3.10)
+project(detectwebcam)
+find_package(Torch REQUIRED PATHS /home/ss21mipt/libtorch/)
+find_package(OpenCV REQUIRED)
+
+include_directories(${OpenCV_INCLUDE_DIRS})
+message(${OpenCV_INCLUDE_DIRS})
+message(${OpenCV_LIBS})
+ADD_EXECUTABLE(detectwebcam detectwebcam.cpp)
+
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${TORCH_CXX_FLAGS} --std=c++14")
+# message(${TORCH_LIBRARIES})
+target_link_libraries(detectwebcam "${TORCH_LIBRARIES}" "${OpenCV_LIBS}")
+# target_link_libraries(detectwebcam "${TORCH_LIBRARIES}")
+# set_property(TARGET detectwebcam PROPERTY CXX_STANDARD 14)
diff --git a/yoeo/detectwebcam_cpp/detectwebcam.cpp b/yoeo/detectwebcam_cpp/detectwebcam.cpp
new file mode 100644
index 0000000..39fc76e
--- /dev/null
+++ b/yoeo/detectwebcam_cpp/detectwebcam.cpp
@@ -0,0 +1,315 @@
+/*
+def _draw_and_save_output_image(image, detections, seg, img_size, output_path, classes):
+ """Draws detections in output image and stores this.
+
+ :param image_path: Path to input image
+ :type image_path: str
+ :param detections: List of detections on image
+ :type detections: [Tensor]
+ :param seg: Segmentation image
+ :type seg: Tensor
+ :param img_size: Size of each image dimension for yolo
+ :type img_size: int
+ :param output_path: Path of output directory
+ :type output_path: str
+ :param classes: List of class names
+ :type classes: [str]
+ """
+ # Create plot
+ img = cv2.cvtColor(image,cv2.COLOR_BGR2RGB)
+ plt.figure()
+ fig, ax = plt.subplots(1)
+ # Get segmentation
+ seg = seg.cpu().detach().numpy().astype(np.uint8)
+ # seg = seg.astype(np.uint8)
+ # Draw all of it
+ seg = seg[0]
+ print(f"ETO EST SEG {seg}")
+ # The amount of padding that was added
+ print("GOVNINA")
+ print(img_size / max(img.shape[:2]))
+ print(max(img.shape[0] - img.shape[1], 0))
+ print(img.shape[0], img.shape[1])
+ print("end of GOVNINA")
+ pad_x = max(img.shape[0] - img.shape[1], 0) * (img_size / max(img.shape[:2])) // 2
+ # pad_x = 21.0
+ pad_y = max(img.shape[1] - img.shape[0], 0) * (img_size / max(img.shape[:2])) // 2
+ print(f"CHEKAI PADI {pad_x, pad_y}")
+
+ seg_map = seg[
+ int(pad_y) : int(img_size - pad_y),
+ int(pad_x) : int(img_size - pad_x),
+ ] * 255
+
+ print(f"MILLIARDNAYA {img, pad_y, pad_x}")
+ ax.imshow(
+ SegmentationMapsOnImage(
+ seg[
+ int(pad_y) : int(img_size - pad_y),
+ int(pad_x) : int(img_size - pad_x),
+ ], shape=img.shape).draw_on_image(img)[0])
+ print("JJEEPPAAA")
+ # Rescale boxes to original image
+
+ detections = rescale_boxes(detections, img_size, img.shape[:2])
+ unique_labels = detections[:, -1].cpu().unique()
+ n_cls_preds = len(unique_labels)
+ # Bounding-box colors
+ cmap = plt.get_cmap("tab20b")
+ colors = [cmap(i) for i in np.linspace(0, 1, len(classes))]
+ for x1, y1, x2, y2, conf, cls_pred in detections:
+
+ print(f"\t+ Label: {classes[int(cls_pred)]} | Confidence: {conf.item():0.4f}")
+
+ box_w = x2 - x1
+ box_h = y2 - y1
+
+ # Create a Rectangle patch
+ bbox = patches.Rectangle((x1, y1), box_w, box_h, linewidth=2, edgecolor=colors[int(cls_pred)], facecolor="none")
+ # Add the bbox to the plot
+ ax.add_patch(bbox)
+ # Add label
+ """
+ plt.text(
+ x1,
+ y1,
+ s=classes[int(cls_pred)],
+ color="white",
+ verticalalignment="top",
+ bbox={"color": colors[int(cls_pred)], "pad": 0})
+ """
+
+ # Save generated image with detections
+ plt.axis("off")
+ plt.tight_layout(pad=0)
+ plt.gca().xaxis.set_major_locator(NullLocator())
+ plt.gca().yaxis.set_major_locator(NullLocator())
+ # filename = os.path.basename(image_path).split(".")[0]
+ # output_path_1 = os.path.join(output_path, f"{filename}.png")
+ # redraw the canvas
+ fig.canvas.draw()
+ # convert canvas to image
+ img = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8,
+ sep='')
+ print(f"summing up PICTURE 0 : {img.shape}")
+ img = img.reshape(fig.canvas.get_width_height()[::-1] + (3,))
+ # img is rgb, convert to opencv's default bgr
+ img = cv2.cvtColor(img,cv2.COLOR_RGB2BGR)
+ print(f"summing up PICTURE 1 : {img.shape}")
+ # cv2.imwrite(output_path_1, img)
+ cv2.imshow('inference', img)
+ # cv2.waitKey(1)
+
+
+
+model = load_model(model_path, weights_path)
+print("NY PRIVET 7")
+
+cam = cv2.VideoCapture(0)
+key = cv2.waitKey(1)
+
+while key != 27:
+ print("START OF INFERENCE OF IMAGE")
+ t = time.time()
+ _, image = cam.read()
+
+ fps = int(cam.get(cv2.CAP_PROP_FPS))
+ fps_text = "FPS: {:.2f}".format(fps)
+ cv2.putText(image, fps_text, (5, 30), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 1)
+
+ cv2.imshow('raw', image)
+
+ model.eval() # Set model to evaluation mode
+
+ # Configure input
+ input_img = transforms.Compose([
+ DEFAULT_TRANSFORMS,
+ Resize(img_size)])((
+ image,
+ np.empty((1, 5)),
+ np.empty((img_size, img_size), dtype=np.uint8)))[0].unsqueeze(0)
+
+ print(f"raw image shape: {image.shape}")
+ print(f"torch image shape: {input_img.shape}")
+
+
+ if torch.cuda.is_available():
+ input_img = input_img.to("cuda")
+ print(f"torch image shape: {input_img.shape}")
+ # Get detections
+ with torch.no_grad():
+
+ detections, segmentations = model(input_img)
+ detections = non_max_suppression(detections, conf_thres, nms_thres)
+ detections = rescale_boxes(detections[0], img_size, image.shape[0:2])
+ segmentations = rescale_segmentation(segmentations, image.shape[0:2])
+ print(f"detections shape: {detections.shape}")
+ print(f"detections shape: {detections.shape}")
+
+ _draw_and_save_output_image(image, detections, segmentations, img_size, output_path, classes)
+
+ if cv2.waitKey(1) == 27:
+ cam.release()
+ cv2.destroyAllWindows()
+ break
+*/
+
+#include
+// #include
+// #include
+#include
+#include
+// #include
+
+auto ToInput(torch::Tensor tensor_image)
+{
+ // Create a vector of inputs.
+ return std::vector{tensor_image};
+}
+
+auto ToTensor(cv::Mat img, bool show_output = false, bool unsqueeze = false, int unsqueeze_dim = 0)
+{
+ std::cout << "image shape: " << img.size() << std::endl;
+ torch::Tensor tensor_image = torch::from_blob(img.data, {1, img.rows, img.cols, 3 }, torch::kByte);
+
+ if (unsqueeze)
+ {
+ tensor_image.unsqueeze_(unsqueeze_dim);
+ std::cout << "tensors new shape: " << tensor_image.sizes() << std::endl;
+ }
+
+ if (show_output)
+ {
+ std::cout << tensor_image.slice(2, 0, 1) << std::endl;
+ }
+ std::cout << "tenor shape: " << tensor_image.sizes() << std::endl;
+ return tensor_image;
+}
+
+torch::Tensor CVtoTensor(cv::Mat img,int unsqueeze_dim = 0) {
+ cv::Size scale(640, 640);
+ cv::resize(img, img, scale);
+ std::cout << "== simply resize: " << img.size() << " ==" << std::endl;
+ cv::cvtColor(img, img, cv::COLOR_BGR2RGB);
+ auto img_tensor = torch::from_blob(img.data, {1, img.rows, img.cols, 3 }, torch::kByte);
+ img_tensor = img_tensor.permute({ 0, 3, 1, 2 }).toType(torch::kFloat);
+ std::cout << "tensor shape: " << img_tensor.sizes() << std::endl;
+ // std::cout << "tensors new shape: " << img_tensor.sizes() << std::endl;
+ return img_tensor;
+}
+
+int main() {
+
+ int kIMAGE_SIZE = 416;
+ std::cout << "OpenCV version : " << CV_VERSION << std::endl;
+ std::cout << "Major version : " << CV_MAJOR_VERSION << std::endl;
+ std::cout << "Minor version : " << CV_MINOR_VERSION << std::endl;
+ std::cout << "Subminor version : " << CV_SUBMINOR_VERSION << std::endl;
+
+ torch::jit::script::Module module;
+ std::cout << "BEFORE LOADING MODEL" << std::endl;
+ // module.load_jit("/home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/weights/yoeo.pt");
+ module = torch::jit::load("/home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/weights/ochko.pt");
+ // module = torch::jit::load("/home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/weights/expected_flow.pt");
+ std::cout << "AFTER LOADING MODEL" << std::endl;
+ module.to(torch::kCPU);
+ // module.eval();
+
+ cv::VideoCapture cap(0);
+ cv::Mat image;
+ cv::Mat segments;
+ torch::Tensor img;
+ int dets = 5;
+ int segs = 4;
+ while (cap.isOpened()){
+ cap >> image;
+ if (image.empty() || !image.data) {
+ return false;
+ }
+ img = CVtoTensor(image);
+ std::cout << "dim 0: " << img.sizes()[0] << std::endl;
+ std::cout << "dim 1: " << img.sizes()[1] << std::endl;
+ std::cout << "dim 2: " << img.sizes()[2] << std::endl;
+ std::cout << "dim 3: " << img.sizes()[3] << std::endl;
+
+ // input_tensor.sub_(0.5).div_(0.5);
+ std::vector input;
+ // // inputs.emplace_back(img);
+ input.push_back(img);
+ // inputs = inputs.to(torch::kCPU);
+ std::cout << "BEFORE OUTPUT worked" << std::endl;
+ // std::cout << typeid().name() << std::
+ auto output = module.forward(input).toTensorList();
+ // std::cout << "MID OUTPUT worked" << std::endl;
+ // auto out = output.toTensorList();
+ std::cout << "AFTER OUTPUT worked" << std::endl;
+
+ // inputs.push_back(torch::ones({1, 3, 416, 416}));
+ // std::cout << "ToInput worked" << std::endl;
+ // torch::IValue out = module.forward({input_tensor});
+ // std::cout << "ZDOROVO" << std::endl;
+ for (int i=0; i=0)
+ break;
+ std::cout << "Allo blyat " << std::endl;
+ }
+ cv::destroyWindow("segmentation");
+ // std::cout << "try: " << output[0].slice() << std::endl;
+ // auto output = out.toTensor();
+ // std::cout << "output initialized" << std::endl;
+
+ // auto results = out.sort(-1, true);
+ // auto softmaxs = std::get<0>(results)[0].softmax(0);
+ // auto indexs = std::get<1>(results)[0];
+
+ //sizes() gives shape.
+ // std::cout << output.sizes() << std::endl;
+ //std::cout << "output: " << output[0] << std::endl;
+ //std::cout << output.slice(/*dim=*/1, /*start=*/0, /*end=*/5) << '\n';
+
+ // output = torch::sigmoid(output);
+
+ // auto out_tensor = output.squeeze(0).detach().permute({ 1, 2, 0 });
+
+ //auto out_tensor = output.squeeze().detach();
+ // std::cout << "out_tensor (after squeeze & detach): " << out_tensor.sizes() << std::endl;
+ // out_tensor = out_tensor.mul(255).clamp(0, 255).to(torch::kU8);
+ // out_tensor = out_tensor.to(torch::kCPU);
+ // cv::Mat resultImg(448, 448, CV_8UC3);
+ // std::memcpy((void*)resultImg.data, out_tensor.data_ptr(), sizeof(torch::kU8) * out_tensor.numel());
+ // cv::resize(resultImg, resultImg, cv::Size(1280, 720), 0, 0, cv::INTER_AREA);
+
+ // cv::imshow("segmentation", resultImg);
+ // cv::waitKey(0);
+ // cv::destroyWindow("segmentation");
+ return 0;
+}
\ No newline at end of file
diff --git a/yoeo/detectwebcam_cpp/image1.png b/yoeo/detectwebcam_cpp/image1.png
new file mode 100644
index 0000000..ac4238d
Binary files /dev/null and b/yoeo/detectwebcam_cpp/image1.png differ
diff --git a/yoeo/detectwebcam_pt.py b/yoeo/detectwebcam_pt.py
new file mode 100644
index 0000000..25d7c11
--- /dev/null
+++ b/yoeo/detectwebcam_pt.py
@@ -0,0 +1,361 @@
+#! /usr/bin/env python3
+from __future__ import division
+
+import os
+import argparse
+import tqdm
+import numpy as np
+import cv2
+import time
+
+import torch
+import torchvision.transforms as transforms
+from torch.utils.data import DataLoader
+from torch.autograd import Variable
+
+from imgaug.augmentables.segmaps import SegmentationMapsOnImage
+
+from yoeo.models2pt import load_model
+from yoeo.utils.utils import load_classes, rescale_boxes, non_max_suppression, print_environment_info, rescale_segmentation
+from yoeo.utils.datasets import ImageFolder
+from yoeo.utils.transforms import Resize, DEFAULT_TRANSFORMS
+
+import matplotlib.pyplot as plt
+import matplotlib.patches as patches
+from matplotlib.ticker import NullLocator
+
+
+def detect_directory(model_path, weights_path, classes, output_path,
+ batch_size=8, img_size=416, n_cpu=8, conf_thres=0.5, nms_thres=0.5):
+ """Detects objects on all images in specified directory and saves output images with drawn detections.
+
+ :param model_path: Path to model definition file (.cfg)
+ :type model_path: str
+ :param weights_path: Path to weights or checkpoint file (.weights or .pth)
+ :type weights_path: str
+ :param img_path: Path to directory with images to inference
+ :type img_path: str
+ :param classes: List of class names
+ :type classes: [str]
+ :param output_path: Path to output directory
+ :type output_path: str
+ :param batch_size: Size of each image batch, defaults to 8
+ :type batch_size: int, optional
+ :param img_size: Size of each image dimension for yolo, defaults to 416
+ :type img_size: int, optional
+ :param n_cpu: Number of cpu threads to use during batch generation, defaults to 8
+ :type n_cpu: int, optional
+ :param conf_thres: Object confidence threshold, defaults to 0.5
+ :type conf_thres: float, optional
+ :param nms_thres: IOU threshold for non-maximum suppression, defaults to 0.5
+ :type nms_thres: float, optional
+ """
+ model = torch.jit.load(weights_path)
+ print("NY PRIVET 7")
+
+ cam = cv2.VideoCapture(0)
+ key = cv2.waitKey(1)
+
+ while key != 27:
+ print("START OF INFERENCE OF IMAGE")
+ t = time.time()
+ _, image = cam.read()
+
+ fps = int(cam.get(cv2.CAP_PROP_FPS))
+ fps_text = "FPS: {:.2f}".format(fps)
+ cv2.putText(image, fps_text, (5, 30), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 1)
+
+ cv2.imshow('raw', image)
+
+ detections, segmentations = detect_image(
+ model,
+ image,
+ img_size,
+ conf_thres,
+ nms_thres)
+ print("NY PRIVET last")
+
+ _draw_and_save_output_image(image, detections, segmentations, img_size, output_path, classes)
+ print(time.time() - t)
+ print("END OF INFERENCE OF IMAGE")
+
+ # print(f"---- Detections were saved to: '{output_path}' ----")
+
+ print(f"SUM UP: {image.shape}")
+
+ if cv2.waitKey(1) == 27:
+ cam.release()
+ cv2.destroyAllWindows()
+ break
+
+
+def detect_image(model, image, img_size=416, conf_thres=0.5, nms_thres=0.5):
+ """Inferences one image with model.
+
+ :param model: Model for inference
+ :type model: models.Darknet
+ :param image: Image to inference
+ :type image: nd.array
+ :param img_size: Size of each image dimension for yolo, defaults to 416
+ :type img_size: int, optional
+ :param conf_thres: Object confidence threshold, defaults to 0.5
+ :type conf_thres: float, optional
+ :param nms_thres: IOU threshold for non-maximum suppression, defaults to 0.5
+ :type nms_thres: float, optional
+ :return: Detections on image with each detection in the format: [x1, y1, x2, y2, confidence, class], Segmentation as 2d numpy array with the coresponding class id in each cell
+ :rtype: nd.array, nd.array
+ """
+ model.eval() # Set model to evaluation mode
+ sums = []
+ # Configure input
+ input_img = transforms.Compose([
+ DEFAULT_TRANSFORMS,
+ Resize(img_size)])((
+ image,
+ np.empty((1, 5)),
+ np.empty((img_size, img_size), dtype=np.uint8)))[0].unsqueeze(0)
+
+ print(f"raw image shape: {image.shape}")
+ print(f"torch image shape: {input_img.shape}")
+ print(f"torch image shape: {type(input_img)}")
+
+
+ # if torch.cuda.is_available():
+ # input_img = input_img.to("cuda")
+ print(f"model: {model}")
+ # Get detections
+ with torch.no_grad():
+ sums = model(input_img)
+ print(type(sums))
+ # print(f"num 1 DETECTIONS SHAPE IS{detections.shape}, {type(detections)}")
+ for elem in sums:
+ print(f"SHAPE OF EVERY ELEM: {elem.shape}")
+ exit()
+ # detections = non_max_suppression(detections, conf_thres, nms_thres)
+ # print(f"num 2 DETECTIONS SHAPE IS{len(detections)}")
+
+ # detections = rescale_boxes(detections[0], img_size, image.shape[0:2])
+ # print(f"num 3 DETECTIONS SHAPE IS{detections.shape}")
+
+ # segmentations = rescale_segmentation(segmentations, image.shape[0:2])
+ # print(f"detections shape: {detections.shape}")
+ # print(f"detections shape: {detections.shape}")
+
+ # return detections.numpy(), segmentations.cpu().detach().numpy()
+ return detections, segmentations
+
+def detect(model, output_path, conf_thres, nms_thres):
+ """Inferences images with model.
+
+ :param model: Model for inference
+ :type model: models.Darknet
+ :param dataloader: Dataloader provides the batches of images to inference
+ :type dataloader: DataLoader
+ :param output_path: Path to output directory
+ :type output_path: str
+ :param conf_thres: Object confidence threshold, defaults to 0.5
+ :type conf_thres: float, optional
+ :param nms_thres: IOU threshold for non-maximum suppression, defaults to 0.5
+ :type nms_thres: float, optional
+ :return: List of detections. The coordinates are given for the padded image that is provided by the dataloader.
+ Use `utils.rescale_boxes` to transform them into the desired input image coordinate system before its transformed by the dataloader),
+ List of input image paths
+ :rtype: [Tensor], [str]
+ """
+ # Create output directory, if missing
+ os.makedirs(output_path, exist_ok=True)
+ print("nychetiii 1")
+ model.eval() # Set model to evaluation mode
+ print("nychetiii 2")
+ Tensor = torch.cuda.FloatTensor if torch.cuda.is_available() else torch.FloatTensor
+
+
+
+ cam = cv2.VideoCapture(0)
+ key = cv2.waitKey(1)
+ while key != 27:
+ _, image = cam.read()
+ cv2.imshow('raw', image)
+
+ img = torch.from_numpy(image)
+ img = Variable(img.type(Tensor))
+ print(img)
+ # Get detections
+ with torch.no_grad():
+ print(img)
+ detections, segmentations = model(img)
+ print("NY PRIVET 8")
+ detections = non_max_suppression(detections, conf_thres, nms_thres)
+
+ if detections and segmentations and img:
+ return detections, segmentations, img
+
+ if cv2.waitkey(1) == 27:
+ cam.release()
+ cv2.destroyAllWindows()
+
+
+def _draw_and_save_output_image(image, detections, seg, img_size, output_path, classes):
+ """Draws detections in output image and stores this.
+
+ :param image_path: Path to input image
+ :type image_path: str
+ :param detections: List of detections on image
+ :type detections: [Tensor]
+ :param seg: Segmentation image
+ :type seg: Tensor
+ :param img_size: Size of each image dimension for yolo
+ :type img_size: int
+ :param output_path: Path of output directory
+ :type output_path: str
+ :param classes: List of class names
+ :type classes: [str]
+ """
+ # Create plot
+ img = cv2.cvtColor(image,cv2.COLOR_BGR2RGB)
+ plt.figure()
+ fig, ax = plt.subplots(1)
+ # Get segmentation
+ seg = seg.cpu().detach().numpy().astype(np.uint8)
+ # seg = seg.astype(np.uint8)
+ # Draw all of it
+ seg = seg[0]
+ print(f"ETO EST SEG {seg}")
+ # The amount of padding that was added
+ print("GOVNINA")
+ print(img_size / max(img.shape[:2]))
+ print(max(img.shape[0] - img.shape[1], 0))
+ print(img.shape[0], img.shape[1])
+ print("end of GOVNINA")
+ pad_x = max(img.shape[0] - img.shape[1], 0) * (img_size / max(img.shape[:2])) // 2
+ # pad_x = 21.0
+ pad_y = max(img.shape[1] - img.shape[0], 0) * (img_size / max(img.shape[:2])) // 2
+ print(f"CHEKAI PADI {pad_x, pad_y}")
+
+ seg_map = seg[
+ int(pad_y) : int(img_size - pad_y),
+ int(pad_x) : int(img_size - pad_x),
+ ] * 255
+
+ print(f"MILLIARDNAYA {img, pad_y, pad_x}")
+ ax.imshow(
+ SegmentationMapsOnImage(
+ seg[
+ int(pad_y) : int(img_size - pad_y),
+ int(pad_x) : int(img_size - pad_x),
+ ], shape=img.shape).draw_on_image(img)[0])
+ print("JJEEPPAAA")
+ # Rescale boxes to original image
+
+ print(f"num after 1 DETECTIONS SHAPE IS{detections.shape}")
+ detections = rescale_boxes(detections, img_size, img.shape[:2])
+
+ unique_labels = detections[:, -1].cpu().unique()
+ n_cls_preds = len(unique_labels)
+ # Bounding-box colors
+ cmap = plt.get_cmap("tab20b")
+ colors = [cmap(i) for i in np.linspace(0, 1, len(classes))]
+ print(f"num after 2 DETECTIONS SHAPE IS{detections.shape}")
+ for x1, y1, x2, y2, conf, cls_pred in detections:
+
+ print(f"\t+ Label: {classes[int(cls_pred)]} | Confidence: {conf.item():0.4f}")
+
+ box_w = x2 - x1
+ box_h = y2 - y1
+
+ # Create a Rectangle patch
+ bbox = patches.Rectangle((x1, y1), box_w, box_h, linewidth=2, edgecolor=colors[int(cls_pred)], facecolor="none")
+ # Add the bbox to the plot
+ ax.add_patch(bbox)
+ # Add label
+ """
+ plt.text(
+ x1,
+ y1,
+ s=classes[int(cls_pred)],
+ color="white",
+ verticalalignment="top",
+ bbox={"color": colors[int(cls_pred)], "pad": 0})
+ """
+
+ # Save generated image with detections
+ plt.axis("off")
+ plt.tight_layout(pad=0)
+ plt.gca().xaxis.set_major_locator(NullLocator())
+ plt.gca().yaxis.set_major_locator(NullLocator())
+ # filename = os.path.basename(image_path).split(".")[0]
+ # output_path_1 = os.path.join(output_path, f"{filename}.png")
+ # redraw the canvas
+ fig.canvas.draw()
+ # convert canvas to image
+ img = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8,
+ sep='')
+ print(f"summing up PICTURE 0 : {img.shape}")
+ img = img.reshape(fig.canvas.get_width_height()[::-1] + (3,))
+ # img is rgb, convert to opencv's default bgr
+ img = cv2.cvtColor(img,cv2.COLOR_RGB2BGR)
+ print(f"summing up PICTURE 1 : {img.shape}")
+ # cv2.imwrite(output_path_1, img)
+ cv2.imshow('inference', img)
+ # cv2.waitKey(1)
+
+
+def _create_data_loader(img_path, batch_size, img_size, n_cpu):
+ """Creates a DataLoader for inferencing.
+
+ :param img_path: Path to file containing all paths to validation images.
+ :type img_path: str
+ :param batch_size: Size of each image batch
+ :type batch_size: int
+ :param img_size: Size of each image dimension for yolo
+ :type img_size: int
+ :param n_cpu: Number of cpu threads to use during batch generation
+ :type n_cpu: int
+ :return: Returns DataLoader
+ :rtype: DataLoader
+ """
+
+ dataset = ImageFolder(
+ img_path,
+ transform=transforms.Compose([DEFAULT_TRANSFORMS, Resize(img_size)]))
+ dataloader = DataLoader(
+ dataset,
+ batch_size=batch_size,
+ shuffle=False,
+ num_workers=n_cpu,
+ pin_memory=True)
+ return dataloader
+
+
+def run():
+ print_environment_info()
+ parser = argparse.ArgumentParser(description="Detect objects on images.")
+ parser.add_argument("-m", "--model", type=str, default="config/yoeo.cfg", help="Path to model definition file (.cfg)")
+ parser.add_argument("-w", "--weights", type=str, default="weights/yoeo.pth", help="Path to weights or checkpoint file (.weights or .pth)")
+ parser.add_argument("-i", "--images", type=str, default="data/samples", help="Path to directory with images to inference")
+ parser.add_argument("-c", "--classes", type=str, default="data/yoeo_names.yaml", help="Path to .yaml file containing the classes' names")
+ parser.add_argument("-o", "--output", type=str, default="output", help="Path to output directory")
+ parser.add_argument("-b", "--batch_size", type=int, default=1, help="Size of each image batch")
+ parser.add_argument("--img_size", type=int, default=416, help="Size of each image dimension for yolo")
+ parser.add_argument("--n_cpu", type=int, default=8, help="Number of cpu threads to use during batch generation")
+ parser.add_argument("--conf_thres", type=float, default=0.5, help="Object confidence threshold")
+ parser.add_argument("--nms_thres", type=float, default=0.4, help="IOU threshold for non-maximum suppression")
+ args = parser.parse_args()
+ print(f"Command line arguments: {args}")
+
+ # Extract class names from file
+ classes = load_classes(args.classes)['detection'] # List of class names
+
+ detect_directory(
+ args.model,
+ args.weights,
+ classes,
+ args.output,
+ batch_size=args.batch_size,
+ img_size=args.img_size,
+ n_cpu=args.n_cpu,
+ conf_thres=args.conf_thres,
+ nms_thres=args.nms_thres)
+
+if __name__ == '__main__':
+ run()
diff --git a/yoeo/models.py b/yoeo/models.py
index 1b69cd8..3670538 100644
--- a/yoeo/models.py
+++ b/yoeo/models.py
@@ -212,9 +212,11 @@ def forward(self, x, bb_targets=None, mask_targets=None):
x = layer_outputs[-1] + layer_outputs[layer_i]
elif module_def["type"] == "yolo":
x = module[0](x, img_size)
+ print(f"module_def[/type/] == /yolo/: {type(x)}, {x.shape}, {x}")
yolo_outputs.append(x)
elif module_def["type"] == "seg":
x = module[0](x)
+ print(f"module_def[/type/] == /seg/: {type(x)}, {x.shape}, {x}")
segmentation_outputs.append(x)
layer_outputs.append(x)
return (yolo_outputs, segmentation_outputs) if self.training else (torch.cat(yolo_outputs, 1), torch.cat(segmentation_outputs, 1))
@@ -324,8 +326,7 @@ def load_model(model_path, weights_path=None):
:return: Returns model
:rtype: Darknet
"""
- device = torch.device("cuda" if torch.cuda.is_available()
- else "cpu") # Select device for inference
+ device = torch.device("cpu") # Select device for inference
model = Darknet(model_path).to(device)
diff --git a/yoeo/models2pt.py b/yoeo/models2pt.py
new file mode 100644
index 0000000..c51cab1
--- /dev/null
+++ b/yoeo/models2pt.py
@@ -0,0 +1,357 @@
+from __future__ import division
+from itertools import chain
+import os
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+
+from yoeo.utils.parse_config import parse_model_config
+from yoeo.utils.utils import weights_init_normal, to_cpu, seg_iou
+
+
+def create_modules(module_defs):
+ """
+ Constructs module list of layer blocks from module configuration in module_defs
+ """
+ hyperparams = module_defs.pop(0)
+ hyperparams.update({
+ 'batch': int(hyperparams['batch']),
+ 'subdivisions': int(hyperparams['subdivisions']),
+ 'width': int(hyperparams['width']),
+ 'height': int(hyperparams['height']),
+ 'channels': int(hyperparams['channels']),
+ 'optimizer': hyperparams.get('optimizer'),
+ 'momentum': float(hyperparams['momentum']),
+ 'decay': float(hyperparams['decay']),
+ 'learning_rate': float(hyperparams['learning_rate']),
+ 'burn_in': int(hyperparams['burn_in']),
+ 'max_batches': int(hyperparams['max_batches']),
+ 'policy': hyperparams['policy'],
+ 'lr_steps': list(zip(map(int, hyperparams["steps"].split(",")),
+ map(float, hyperparams["scales"].split(","))))
+ })
+ assert hyperparams["height"] == hyperparams["width"], \
+ "Height and width should be equal! Non square images are padded with zeros."
+ output_filters = [hyperparams["channels"]]
+ module_list = nn.ModuleList()
+ for module_i, module_def in enumerate(module_defs):
+ modules = nn.Sequential()
+
+ if module_def["type"] == "convolutional":
+ bn = int(module_def["batch_normalize"])
+ filters = int(module_def["filters"])
+ kernel_size = int(module_def["size"])
+ pad = (kernel_size - 1) // 2
+ modules.add_module(
+ f"conv_{module_i}",
+ nn.Conv2d(
+ in_channels=output_filters[-1],
+ out_channels=filters,
+ kernel_size=kernel_size,
+ stride=int(module_def["stride"]),
+ padding=pad,
+ bias=not bn,
+ ),
+ )
+ if bn:
+ modules.add_module(f"batch_norm_{module_i}",
+ nn.BatchNorm2d(filters, momentum=0.1, eps=1e-5))
+ if module_def["activation"] == "leaky":
+ modules.add_module(f"leaky_{module_i}", nn.LeakyReLU(0.1))
+ if module_def["activation"] == "mish":
+ modules.add_module(f"mish_{module_i}", Mish())
+
+ elif module_def["type"] == "maxpool":
+ kernel_size = int(module_def["size"])
+ stride = int(module_def["stride"])
+ if kernel_size == 2 and stride == 1:
+ modules.add_module(f"_debug_padding_{module_i}", nn.ZeroPad2d((0, 1, 0, 1)))
+ maxpool = nn.MaxPool2d(kernel_size=kernel_size, stride=stride,
+ padding=int((kernel_size - 1) // 2))
+ modules.add_module(f"maxpool_{module_i}", maxpool)
+
+ elif module_def["type"] == "upsample":
+ upsample = Upsample(scale_factor=int(module_def["stride"]), mode="nearest")
+ modules.add_module(f"upsample_{module_i}", upsample)
+
+ elif module_def["type"] == "route":
+ layers = [int(x) for x in module_def["layers"].split(",")]
+ filters = sum([output_filters[1:][i] for i in layers]) // int(module_def.get("groups", 1))
+ modules.add_module(f"route_{module_i}", nn.Sequential())
+
+ elif module_def["type"] == "shortcut":
+ filters = output_filters[1:][int(module_def["from"])]
+ modules.add_module(f"shortcut_{module_i}", nn.Sequential())
+
+ elif module_def["type"] == "yolo":
+ anchor_idxs = [int(x) for x in module_def["mask"].split(",")]
+ # Extract anchors
+ anchors = [int(x) for x in module_def["anchors"].split(",")]
+ anchors = [(anchors[i], anchors[i + 1]) for i in range(0, len(anchors), 2)]
+ anchors = [anchors[i] for i in anchor_idxs]
+ num_classes = int(module_def["classes"])
+ # Define detection layer
+ yolo_layer = YOLOLayer(anchors, num_classes)
+ modules.add_module(f"yolo_{module_i}", yolo_layer)
+ elif module_def["type"] == "seg":
+ num_classes = int(module_def["classes"])
+ modules.add_module(f"seg_{module_i}", SegLayer(num_classes))
+ # Register module list and number of output filters
+ module_list.append(modules)
+ output_filters.append(filters)
+
+ return hyperparams, module_list
+
+
+class Upsample(nn.Module):
+ """ nn.Upsample is deprecated """
+
+ def __init__(self, scale_factor, mode="nearest"):
+ super(Upsample, self).__init__()
+ self.scale_factor = scale_factor
+ self.mode = mode
+
+ def forward(self, x):
+ x = F.interpolate(x, scale_factor=self.scale_factor, mode=self.mode)
+ return x
+
+class Mish(nn.Module):
+ """ The MISH activation function (https://github.com/digantamisra98/Mish) """
+
+ def __init__(self):
+ super(Mish, self).__init__()
+
+ def forward(self, x):
+ return x * torch.tanh(F.softplus(x))
+
+class YOLOLayer(nn.Module):
+ """Detection layer"""
+
+ def __init__(self, anchors, num_classes):
+ super(YOLOLayer, self).__init__()
+ self.num_anchors = len(anchors)
+ self.num_classes = num_classes
+ self.mse_loss = nn.MSELoss()
+ self.bce_loss = nn.BCELoss()
+ self.no = num_classes + 5 # number of outputs per anchor
+ self.grid = torch.zeros(1) # TODO
+
+ anchors = torch.tensor(list(chain(*anchors))).float().view(-1, 2)
+ self.register_buffer('anchors', anchors)
+ self.register_buffer(
+ 'anchor_grid', anchors.clone().view(1, -1, 1, 1, 2))
+ self.stride = torch.Tensor([])
+
+ def forward(self, x, img_size):
+ stride = img_size // x.size(2)
+ self.stride = stride
+ bs, _, ny, nx = x.shape # x(bs,255,20,20) to x(bs,3,20,20,85)
+ x = x.view(bs, self.num_anchors, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
+
+ if not self.training: # inference
+ if self.grid.shape[2:4] != x.shape[2:4]:
+ self.grid = self._make_grid(nx, ny).to(x.device)
+
+ x = torch.cat([
+ (x[..., 0:2].sigmoid() + self.grid) * stride, # xy
+ torch.exp(x[..., 2:4]) * self.anchor_grid, # wh
+ x[..., 4:].sigmoid(),
+ ], axis=4).view(bs, -1, self.no)
+
+ return x
+
+ @staticmethod
+ def _make_grid(nx=20, ny=20):
+ yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)], indexing='ij')
+ return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()
+
+
+class SegLayer(nn.Module):
+ """Detection layer"""
+
+ def __init__(self, num_classes):
+ super(SegLayer, self).__init__()
+ self.num_classes = num_classes
+
+ def forward(self, x):
+ if self.training:
+ return x
+ else:
+ return torch.argmax(x, dim=1).to(torch.uint8)
+
+
+class Darknet(nn.Module):
+ """YOLOv3 object detection model"""
+
+ def __init__(self, config_path):
+ super(Darknet, self).__init__()
+ self.module_defs = parse_model_config(config_path)
+ self.hyperparams, self.module_list = create_modules(self.module_defs)
+ self.yolo_layers = [layer[0] for layer in self.module_list if isinstance(layer[0], YOLOLayer)]
+ self.seg_layers = [layer[0] for layer in self.module_list if isinstance(layer[0], SegLayer)]
+ self.num_seg_classes = self.seg_layers[0].num_classes
+ self.seen = 0
+ self.header_info = np.array([0, 0, 0, self.seen, 0], dtype=np.int32)
+
+ def forward(self, x, bb_targets=None, mask_targets=None):
+ print("input to forward", type(x), x.shape)
+ img_size = x.size(2)
+ loss = 0
+ layer_outputs, yolo_output1, yolo_output2, segmentation_outputs = [], torch.tensor([]), torch.tensor([]), torch.tensor([])
+ all_y_s = []
+ for i, (module_def, module) in enumerate(zip(self.module_defs, self.module_list)):
+ if module_def["type"] in ["convolutional", "upsample", "maxpool"]:
+ x = module(x)
+ # print(f"module(x) == {x}")
+ elif module_def["type"] == "route":
+ combined_outputs = torch.cat([layer_outputs[int(layer_i)] for layer_i in module_def["layers"].split(",")], 1)
+ group_size = combined_outputs.shape[1] // int(module_def.get("groups", 1))
+ group_id = int(module_def.get("group_id", 0))
+ x = combined_outputs[:, group_size * group_id : group_size * (group_id + 1)] # Slice groupings used by yolo v4
+ elif module_def["type"] == "shortcut":
+ layer_i = int(module_def["from"])
+ x = layer_outputs[-1] + layer_outputs[layer_i]
+ elif module_def["type"] == "yolo":
+ x = module[0](x, img_size)
+ print(f"module_def[/type/] == /yolo/: {type(x)}, {x.shape}")
+ # if list(x.size()) == [1, 3, 13, 13, 8]:
+ # yolo_outputs.append(x)
+ # yolo_output1 = torch.stack((yolo_output1, x), 0)
+ # elif list(x.size()) == [1, 3, 26, 26, 8]:
+ # yolo_outputs.append(x)
+ # yolo_output2 = torch.stack((yolo_output2, x), 0)
+ all_y_s.append(x)
+ elif module_def["type"] == "seg":
+ x = module[0](x)
+ print(f"module_def[/type/] == /seg/: {type(x)}, {x.shape}")
+ # if list(x.size()) == [1, 3, 416, 416]:
+ # segmentation_outputs.append(x)
+ # segmentation_outputs = torch.stack((segmentation_outputs, x), 0)
+ all_y_s.append(x)
+ layer_outputs.append(x)
+ # print(f"yolo_outputs: {yolo_output1, yolo_output2}")
+ # print(f"segmentation_outputs: {segmentation_outputs}")
+ return all_y_s
+ # return (yolo_outputs, segmentation_outputs) if self.training else (torch.cat(yolo_outputs, 1), torch.cat(segmentation_outputs, 1))
+ # return (yolo_output1, yolo_output2, segmentation_outputs) if self.training else (torch.cat(yolo_outputs, 1), torch.cat(segmentation_outputs, 1))
+ def load_darknet_weights(self, weights_path):
+ """Parses and loads the weights stored in 'weights_path'"""
+
+ # Open the weights file
+ with open(weights_path, "rb") as f:
+ # First five are header values
+ header = np.fromfile(f, dtype=np.int32, count=5)
+ self.header_info = header # Needed to write header when saving weights
+ self.seen = header[3] # number of images seen during training
+ weights = np.fromfile(f, dtype=np.float32) # The rest are weights
+
+ # Establish cutoff for loading backbone weights
+ cutoff = None
+ # If the weights file has a cutoff, we can find out about it by looking at the filename
+ # examples: darknet53.conv.74 -> cutoff is 74
+ filename = os.path.basename(weights_path)
+ if ".conv." in filename:
+ try:
+ cutoff = int(filename.split(".")[-1]) # use last part of filename
+ except ValueError:
+ pass
+
+ ptr = 0
+ for i, (module_def, module) in enumerate(zip(self.module_defs, self.module_list)):
+ if i == cutoff:
+ break
+ if module_def["type"] == "convolutional":
+ conv_layer = module[0]
+ if module_def["batch_normalize"]:
+ # Load BN bias, weights, running mean and running variance
+ bn_layer = module[1]
+ num_b = bn_layer.bias.numel() # Number of biases
+ # Bias
+ bn_b = torch.from_numpy(
+ weights[ptr: ptr + num_b]).view_as(bn_layer.bias)
+ bn_layer.bias.data.copy_(bn_b)
+ ptr += num_b
+ # Weight
+ bn_w = torch.from_numpy(
+ weights[ptr: ptr + num_b]).view_as(bn_layer.weight)
+ bn_layer.weight.data.copy_(bn_w)
+ ptr += num_b
+ # Running Mean
+ bn_rm = torch.from_numpy(
+ weights[ptr: ptr + num_b]).view_as(bn_layer.running_mean)
+ bn_layer.running_mean.data.copy_(bn_rm)
+ ptr += num_b
+ # Running Var
+ bn_rv = torch.from_numpy(
+ weights[ptr: ptr + num_b]).view_as(bn_layer.running_var)
+ bn_layer.running_var.data.copy_(bn_rv)
+ ptr += num_b
+ else:
+ # Load conv. bias
+ num_b = conv_layer.bias.numel()
+ conv_b = torch.from_numpy(
+ weights[ptr: ptr + num_b]).view_as(conv_layer.bias)
+ conv_layer.bias.data.copy_(conv_b)
+ ptr += num_b
+ # Load conv. weights
+ num_w = conv_layer.weight.numel()
+ conv_w = torch.from_numpy(
+ weights[ptr: ptr + num_w]).view_as(conv_layer.weight)
+ conv_layer.weight.data.copy_(conv_w)
+ ptr += num_w
+
+ def save_darknet_weights(self, path, cutoff=-1):
+ """
+ @:param path - path of the new weights file
+ @:param cutoff - save layers between 0 and cutoff (cutoff = -1 -> all are saved)
+ """
+ fp = open(path, "wb")
+ self.header_info[3] = self.seen
+ self.header_info.tofile(fp)
+
+ # Iterate through layers
+ for i, (module_def, module) in enumerate(zip(self.module_defs[:cutoff], self.module_list[:cutoff])):
+ if module_def["type"] == "convolutional":
+ conv_layer = module[0]
+ # If batch norm, load bn first
+ if module_def["batch_normalize"]:
+ bn_layer = module[1]
+ bn_layer.bias.data.cpu().numpy().tofile(fp)
+ bn_layer.weight.data.cpu().numpy().tofile(fp)
+ bn_layer.running_mean.data.cpu().numpy().tofile(fp)
+ bn_layer.running_var.data.cpu().numpy().tofile(fp)
+ # Load conv bias
+ else:
+ conv_layer.bias.data.cpu().numpy().tofile(fp)
+ # Load conv weights
+ conv_layer.weight.data.cpu().numpy().tofile(fp)
+
+ fp.close()
+
+
+def load_model(model_path, weights_path=None):
+ """Loads the yolo model from file.
+ :param model_path: Path to model definition file (.cfg)
+ :type model_path: str
+ :param weights_path: Path to weights or checkpoint file (.weights or .pth)
+ :type weights_path: str
+ :return: Returns model
+ :rtype: Darknet
+ """
+ device = torch.device("cpu")
+
+ model = Darknet(model_path).to(device)
+
+ model.apply(weights_init_normal)
+
+ # If pretrained weights are specified, start from checkpoint or weight file
+ if weights_path:
+ if weights_path.endswith(".pth"):
+ # Load checkpoint weights
+ model.load_state_dict(torch.load(weights_path, map_location="cpu"))
+ else:
+ # Load darknet weights
+ model.load_darknet_weights(weights_path)
+ return model
\ No newline at end of file
diff --git a/yoeo/pth2pt.py b/yoeo/pth2pt.py
new file mode 100644
index 0000000..a18a4d7
--- /dev/null
+++ b/yoeo/pth2pt.py
@@ -0,0 +1,24 @@
+import torch
+from yoeo.models2pt import load_model
+import torchvision
+from detectron2.export.flatten import TracingAdapter
+
+cfg = "/home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/config/yoeo.cfg"
+weights = "/home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/weights/yoeo_mine_759.pth"
+# print("cfg.MODEL.WEIGHTS: ",cfg.MODEL.WEIGHTS) ## RETURNS : cfg.MODEL.WEIGHTS: drive/Detectron2/model_final.pth
+
+model = load_model(cfg, weights)
+# print(model)
+# model.eval()
+print(torch.__version__)
+example = torch.rand(1, 3, 640, 640)
+print(f"type(example): {type(example)}")
+# wrapper = TracingAdapter(module, example, inference_func)
+# wrapper.eval()
+# traced_script_module = torch.jit.script(model)
+print(f"TYT KOSYAK")
+traced_script_module = torch.jit.trace(model, example)#, check_trace=False)
+# traced_script_module = torch.jit.trace(wrapper, (example,))
+traced_script_module.save("/home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/weights/ochko.pt")
+# model.save_darknet_weights("/home/ss21mipt/Documents/starkit/DIPLOMA/YOEO/weights/save_dark_w.pt")
+print("YEST")
diff --git a/yoeo/utils/datasets.py b/yoeo/utils/datasets.py
index bc31f4a..b429296 100644
--- a/yoeo/utils/datasets.py
+++ b/yoeo/utils/datasets.py
@@ -8,6 +8,7 @@
import numpy as np
from PIL import Image
from PIL import ImageFile
+import cv2
ImageFile.LOAD_TRUNCATED_IMAGES = True
@@ -36,11 +37,18 @@ def __init__(self, folder_path, transform=None):
self.transform = transform
def __getitem__(self, index):
-
- img_path = self.files[index % len(self.files)]
- img = np.array(
- Image.open(img_path).convert('RGB'),
- dtype=np.uint8)
+ print(self.files)
+ if self.files == 'data/samples/webcam.wc':
+ cam = cv2.VideoCapture(0) #set the port of the camera as before
+ retval, img = cam.read()
+ print(f"WE are openned{img.type()}")
+ cv2.waitKey(1)
+
+ else:
+ img_path = self.files[index % len(self.files)]
+ img = np.array(
+ Image.open(img_path).convert('RGB'),
+ dtype=np.uint8)
# Label Placeholder
boxes = np.zeros((1, 5))
diff --git a/yoeo/utils/utils.py b/yoeo/utils/utils.py
index c384d6b..196816d 100644
--- a/yoeo/utils/utils.py
+++ b/yoeo/utils/utils.py
@@ -80,9 +80,11 @@ def rescale_boxes(boxes, output_img_size, original_img_size):
:return: rescaled detection output
:rtype: torch.Tensor with shape(#boxes, 6)
"""
-
+ print(f"BOXES 1: {boxes}")
rescaled_boxes = rescale_boxes_to_original_padded_img_size(boxes, output_img_size, max(original_img_size))
+ print(f"BOXES 2: {boxes}")
rescaled_boxes = unpad_box_coordinates(rescaled_boxes, original_img_size)
+ print(f"BOXES 3: {boxes}")
return rescaled_boxes
@@ -120,8 +122,14 @@ def unpad_box_coordinates(boxes, original_img_size: Tuple[int, int]):
:rtype: torch.Tensor with shape(#boxes, 6)
"""
+ print("ТУТ ЖЕСТКОЕ ПОДГОНЯЛОВО, unpad_box_coordinates() в yoeo/utils/utils.py")
+ print("THere was padding_top = max(original_img_size[1] - original_img_size[0], 0) // 2")
+ print("THere is padding_top = max(original_img_size[1] - original_img_size[0], 0) // 4")
+
+ # print(f"original img size{original_img_size}")
padding_left = max(original_img_size[0] - original_img_size[1], 0) // 2
- padding_top = max(original_img_size[1] - original_img_size[0], 0) // 2
+ # padding_top = max(original_img_size[1] - original_img_size[0], 0) // 2
+ padding_top = max(original_img_size[1] - original_img_size[0], 0) // 4
boxes[:, 0] = boxes[:, 0] - padding_left
boxes[:, 1] = boxes[:, 1] - padding_top
@@ -144,9 +152,11 @@ def rescale_segmentation(segmentation, original_img_size: Tuple[int, int]):
:return: rescaled segmentation
:rtype: torch.Tensor with shape (1, original_img_size[0], original_img_size[1])
"""
-
+ print(f"SEGMENT 1 {segmentation}")
rescaled_seg = rescale_segmentation_to_original_padded_img_size(segmentation, max(original_img_size))
+ print(f"SEGMENT 2 {rescaled_seg}")
rescaled_seg = unpad_segmentation(rescaled_seg, original_img_size)
+ print(f"SEGMENT 3 {rescaled_seg}")
return rescaled_seg
@@ -188,14 +198,24 @@ def unpad_segmentation(segmentation, original_img_size: Tuple[int, int]):
total_vertical_padding = max(0, original_width - original_height)
total_horizontal_padding = max(0, original_height - original_width)
+ print(f"total_vertical_padding {total_vertical_padding}")
padding_top = total_vertical_padding // 2
+ print(f"padding_top {padding_top}")
+
+ k = (original_width - original_height) / original_height
+ # padding_top = total_vertical_padding // 4
padding_bottom = total_vertical_padding - padding_top
+ print(f"padding_bottom {padding_bottom}")
+
padding_left = total_horizontal_padding // 2
padding_right = total_horizontal_padding - padding_left
+ print(f"XYETAAA {segmentation[..., padding_top:current_size - padding_bottom, padding_left:current_size - padding_right]}")
- return segmentation[..., padding_top:current_size - padding_bottom, padding_left:current_size - padding_right]
-
+ print(f"{segmentation.shape} and {segmentation[..., padding_top:current_size - padding_bottom, padding_left:current_size - padding_right].shape}")
+ # return segmentation[..., padding_top:current_size - padding_bottom, padding_left:current_size - padding_right]
+ return segmentation[..., padding_left:current_size - padding_right]
+ # return segmentation[..., padding_top:int(current_size*k), padding_left:current_size - padding_right]
def xywh2xyxy(x):
y = x.new(x.shape)
@@ -435,9 +455,11 @@ def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=Non
multi_label = nc > 1 # multiple labels per box (adds 0.5ms/img)
t = time.time()
+ print(f"DETECTIONS.SHAPE[0] = {prediction.shape}")
output = [torch.zeros((0, 6), device="cpu")] * prediction.shape[0]
for xi, x in enumerate(prediction): # image index, image inference
+ print(f"xi.shape: {xi}, x.shape{x.shape}")
# Apply constraints
# x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height
x = x[x[..., 4] > conf_thres] # confidence
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