Object detection Model Training

object_detection_train.py

# loop over the image paths
for imagePath in paths.list_images(args["class"]):
	# extract the image ID from the image path and load the annotations file
	imageID = imagePath[imagePath.rfind("/") + 1:].split("_")[1]
	imageID = imageID.replace(".jpg", "")
	p = "{}/annotation_{}.mat".format(args["annotations"], imageID)
	annotations = loadmat(p)["box_coord"]

	# loop over the annotations and add each annotation to the list of bounding
	# boxes
	bb = [dlib.rectangle(left=long(x), top=long(y), right=long(w), bottom=long(h))
			for (y, h, x, w) in annotations]
	boxes.append(bb)

	# add the image to the list of images
	images.append(io.imread(imagePath))

# train the object detector
print("[INFO] training detector...")
detector = dlib.train_simple_object_detector(images, boxes, options)

# dump the classifier to file
print("[INFO] dumping classifier to file...")
detector.save(args["output"])