00_basic_obj_det.py

import sys
import os
sys.path.append(os.environ['TF_RESEARCH'])

import numpy as np
import six.moves.urllib as urllib
import tarfile
import tensorflow as tf
import cv2

from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util

# Define the video stream
cap = cv2.VideoCapture(0)  # Change only if you have more than one webcams

# construct paths
# base path where we will save our models
PATH_TO_OBJ_DETECTION = 'C:/tensorflow/models/research/object_detection'
# What model to download.
# Models can bee found here: https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md
MODEL_NAME = 'ssd_inception_v2_coco_2018_01_28'
MODEL_FILE = MODEL_NAME + '.tar.gz'
DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection'
# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_CKPT = PATH_TO_OBJ_DETECTION + '/' + MODEL_NAME + '/frozen_inference_graph.pb'
# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = PATH_TO_OBJ_DETECTION + '/data/mscoco_label_map.pbtxt'
# path to download model
DESTINATION_MODEL_TAR_PATH = PATH_TO_OBJ_DETECTION + '/' + MODEL_FILE
DOWNLOAD_BASE_URL = DOWNLOAD_BASE + '/' + MODEL_FILE

# print var paths info
print("[INFO] VARS PATHS:")
print(" PATH_TO_OBJ_DETECTION = {}".format(PATH_TO_OBJ_DETECTION))
print(" MODEL_FILE = {}".format(MODEL_FILE))
print(" PATH_TO_CKPT = {}".format(PATH_TO_CKPT))
print(" PATH_TO_LABELS = {}".format(PATH_TO_LABELS))
print(" DESTINATION_MODEL_TAR_PATH = {}".format(DESTINATION_MODEL_TAR_PATH))
print(" DOWNLOAD_BASE_URL = {}".format(DOWNLOAD_BASE_URL))
print("**********")

# Number of classes to detect
NUM_CLASSES = 90

# Download model if need it
if not os.path.exists(DESTINATION_MODEL_TAR_PATH):
    print("[INFO] downloading model '{}'...".format(MODEL_NAME))
    opener = urllib.request.URLopener()
    opener.retrieve(DOWNLOAD_BASE_URL, DESTINATION_MODEL_TAR_PATH)
    tar_file = tarfile.open(DESTINATION_MODEL_TAR_PATH)
    for file in tar_file.getmembers():
        file_name = os.path.basename(file.name)
        if 'frozen_inference_graph.pb' in file_name:
            print("[INFO] extracting model '{}'...".format(MODEL_NAME))
            tar_file.extract(file, PATH_TO_OBJ_DETECTION)

# Load a (frozen) Tensorflow model into memory.
print("[INFO] loading frozen model...")
detection_graph = tf.Graph()
with detection_graph.as_default():
    od_graph_def = tf.GraphDef()
    with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
        serialized_graph = fid.read()
        od_graph_def.ParseFromString(serialized_graph)
        tf.import_graph_def(od_graph_def, name='')

# Loading label map
# Label maps map indices to category names, so that when our convolution network predicts `5`, we know that this corresponds to `airplane`.  Here we use internal utility functions, but anything that returns a dictionary mapping integers to appropriate string labels would be fine
print("[INFO] loading labels map...")
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(
    label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)

# Helper code
def load_image_into_numpy_array(image):
    (im_width, im_height) = image.size
    return np.array(image.getdata()).reshape(
        (im_height, im_width, 3)).astype(np.uint8)

# Detection
print("[INFO] detecting from webcam...")
with detection_graph.as_default():
    with tf.Session(graph=detection_graph) as sess:
        while True:
            # Read frame from camera
            ret, image_np = cap.read()
            # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
            image_np_expanded = np.expand_dims(image_np, axis=0)
            # Extract image tensor
            image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
            # Extract detection boxes
            boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
            # Extract detection scores
            scores = detection_graph.get_tensor_by_name('detection_scores:0')
            # Extract detection classes
            classes = detection_graph.get_tensor_by_name('detection_classes:0')
            # Extract number of detectionsd
            num_detections = detection_graph.get_tensor_by_name(
                'num_detections:0')
            # Actual detection.
            (boxes, scores, classes, num_detections) = sess.run(
                [boxes, scores, classes, num_detections],
                feed_dict={image_tensor: image_np_expanded})
            # Visualization of the results of a detection.
            vis_util.visualize_boxes_and_labels_on_image_array(
                image_np,
                np.squeeze(boxes),
                np.squeeze(classes).astype(np.int32),
                np.squeeze(scores),
                category_index,
                min_score_thresh=.5,
                use_normalized_coordinates=True,
                line_thickness=8)

            # Display output
            cv2.imshow('object detection', cv2.resize(image_np, (800, 600)))

            # Return found objects
            print([category_index.get(i) for i in classes[0]])
            print(boxes.shape)
            print(num_detections)

            if cv2.waitKey(25) & 0xFF == ord('q'):
                cv2.destroyAllWindows()
                break