The sess.run() function by the Tensorflow object detection module takes about 2.5 seconds to detect bounding bozes in a 600x600 image while the object detection function by the HOG desctiptor offered by OpenCV takes 0.270 seconds. How's that possible? How can I speed up this code?
def run(image, detection_graph):
with tf.Session(graph=detection_graph) as sess:
# Definite input and output Tensors for detection_graph
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
# Each score represent how level of confidence for each of the objects.
# Score is shown on the result image, together with the class label.
detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
# the array based representation of the image will be used later in order to prepare the
# result image with boxes and labels on it.
image_np = image
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(image_np, axis=0)
# Actual detection.
(boxes, scores) = sess.run(
return boxes, scores