Object Detector.py

# python Object Detector.py -i images/Hitex.jpg -y Model

import numpy as np
import argparse
import time
import cv2
import os

ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True,
	help="path to input image")
ap.add_argument("-y", "--yolo", required=True,
	help="base path to YOLO directory")
ap.add_argument("-c", "--confidence", type=float, default=0.5,
	help="minimum probability to filter weak detections")
ap.add_argument("-t", "--threshold", type=float, default=0.3,
	help="threshold when applyong non-maxima suppression")
args = vars(ap.parse_args())

# load the COCO class labels
labelsPath = os.path.sep.join([args["yolo"], "coco.names"])
LABELS = open(labelsPath).read().strip().split("\n")

# Path for weights and configuration
weightsPath = os.path.sep.join([args["yolo"], "yolov3.weights"])
configPath = os.path.sep.join([args["yolo"], "yolov3.cfg"])

print("loading YOLO from disk...")
net = cv2.dnn.readNetFromDarknet(configPath, weightsPath)

# load image
image = cv2.imread(args["image"])
(H, W) = image.shape[:2]

ln = net.getLayerNames()
ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]


blob = cv2.dnn.blobFromImage(image, 1 / 255.0, (416, 416),
	swapRB=True, crop=False)
net.setInput(blob)
start = time.time()
layerOutputs = net.forward(ln)
end = time.time()

print("Time for processing : {:.6f} seconds".format(end - start))

confidences = []
classIDs = []

for output in layerOutputs:
	for detection in output:
		# extract the class ID and confidence (i.e., probability) of
		# the current object detection
		scores = detection[5:]
		classID = np.argmax(scores)
		confidence = scores[classID]

		if confidence > args["confidence"]:
			confidences.append(float(confidence))
			classIDs.append(classID)

print(classIDs)
for i in range(len(classIDs)):
    print(LABELS[classIDs[i]],confidences[i])