import os import cv2 import time import datetime import ctypes import logging import multiprocessing as mp from contextlib import closing import numpy as np import tensorflow as tf from object_detection.utils import label_map_util from object_detection.utils import visualization_utils as vis_util from flask import Flask, Response, make_response RTSP_URL = os.getenv('RTSP_URL') # Path to frozen detection graph. This is the actual model that is used for the object detection. PATH_TO_CKPT = '/frozen_inference_graph.pb' # List of the strings that is used to add correct label for each box. PATH_TO_LABELS = '/label_map.pbtext' # TODO: make dynamic? NUM_CLASSES = 90 # Loading label 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) def detect_objects(image_np, sess, detection_graph): # Expand dimensions since the model expects images to have shape: [1, None, None, 3] image_np_expanded = np.expand_dims(image_np, axis=0) 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. 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. scores = detection_graph.get_tensor_by_name('detection_scores:0') classes = detection_graph.get_tensor_by_name('detection_classes:0') 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}) # build an array of detected objects objects = [] for index, value in enumerate(classes[0]): object_dict = {} if scores[0, index] > 0.5: object_dict[(category_index.get(value)).get('name').encode('utf8')] = \ scores[0, index] objects.append(object_dict) # draw boxes for detected objects on image 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, use_normalized_coordinates=True, line_thickness=4) return objects, image_np def main(): # capture a single frame and check the frame shape so the correct array # size can be allocated in memory video = cv2.VideoCapture(RTSP_URL) ret, frame = video.read() if ret: frame_shape = frame.shape else: print("Unable to capture video stream") exit(1) video.release() # create shared value for storing the time the frame was captured # note: this must be a double even though the value you are storing # is a float. otherwise it stops updating the value in shared # memory. probably something to do with the size of the memory block shared_frame_time = mp.Value('d', 0.0) # compute the flattened array length from the array shape flat_array_length = frame_shape[0] * frame_shape[1] * frame_shape[2] # create shared array for passing the image data from capture to detect_objects shared_arr = mp.Array(ctypes.c_uint16, flat_array_length) # create shared array for passing the image data from detect_objects to flask shared_output_arr = mp.Array(ctypes.c_uint16, flat_array_length) # create a numpy array with the image shape from the shared memory array # this is used by flask to output an mjpeg stream frame_output_arr = tonumpyarray(shared_output_arr).reshape(frame_shape) capture_process = mp.Process(target=fetch_frames, args=(shared_arr, shared_frame_time, frame_shape)) capture_process.daemon = True detection_process = mp.Process(target=process_frames, args=(shared_arr, shared_output_arr, shared_frame_time, frame_shape)) detection_process.daemon = True capture_process.start() print("capture_process pid ", capture_process.pid) detection_process.start() print("detection_process pid ", detection_process.pid) app = Flask(__name__) @app.route('/') def index(): # return a multipart response return Response(imagestream(), mimetype='multipart/x-mixed-replace; boundary=frame') def imagestream(): while True: # max out at 5 FPS time.sleep(0.2) # convert back to BGR frame_bgr = cv2.cvtColor(frame_output_arr, cv2.COLOR_RGB2BGR) # encode the image into a jpg ret, jpg = cv2.imencode('.jpg', frame_bgr) yield (b'--frame\r\n' b'Content-Type: image/jpeg\r\n\r\n' + jpg.tobytes() + b'\r\n\r\n') app.run(host='0.0.0.0', debug=False) capture_process.join() detection_process.join() # convert shared memory array into numpy array def tonumpyarray(mp_arr): return np.frombuffer(mp_arr.get_obj(), dtype=np.uint16) # fetch the frames as fast a possible, only decoding the frames when the # detection_process has consumed the current frame def fetch_frames(shared_arr, shared_frame_time, frame_shape): # convert shared memory array into numpy and shape into image array arr = tonumpyarray(shared_arr).reshape(frame_shape) # start the video capture video = cv2.VideoCapture(RTSP_URL) # keep the buffer small so we minimize old data video.set(cv2.CAP_PROP_BUFFERSIZE,1) while True: # grab the frame, but dont decode it yet ret = video.grab() # snapshot the time the frame was grabbed frame_time = datetime.datetime.now() if ret: # if the detection_process is ready for the next frame decode it # otherwise skip this frame and move onto the next one if shared_frame_time.value == 0.0: # go ahead and decode the current frame ret, frame = video.retrieve() if ret: # copy the frame into the numpy array arr[:] = frame # signal to the detection_process by setting the shared_frame_time shared_frame_time.value = frame_time.timestamp() video.release() # do the actual object detection def process_frames(shared_arr, shared_output_arr, shared_frame_time, frame_shape): # shape shared input array into frame for processing arr = tonumpyarray(shared_arr).reshape(frame_shape) # shape shared output array into frame so it can be copied into output_arr = tonumpyarray(shared_output_arr).reshape(frame_shape) # Load a (frozen) Tensorflow model into memory before the processing loop 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='') sess = tf.Session(graph=detection_graph) no_frames_available = -1 while True: # if there isnt a frame ready for processing if shared_frame_time.value == 0.0: # save the first time there were no frames available if no_frames_available == -1: no_frames_available = datetime.datetime.now().timestamp() # if there havent been any frames available in 30 seconds, # sleep to avoid using so much cpu if the camera feed is down if no_frames_available > 0 and (datetime.datetime.now().timestamp() - no_frames_available) > 30: time.sleep(1) print("sleeping because no frames have been available in a while") else: # rest a little bit to avoid maxing out the CPU time.sleep(0.01) continue # we got a valid frame, so reset the timer no_frames_available = -1 # if the frame is more than 0.5 second old, discard it if (datetime.datetime.now().timestamp() - shared_frame_time.value) > 0.5: # signal that we need a new frame shared_frame_time.value = 0.0 # rest a little bit to avoid maxing out the CPU time.sleep(0.01) continue # make a copy of the frame frame = arr.copy() frame_time = shared_frame_time.value # signal that the frame has been used so a new one will be ready shared_frame_time.value = 0.0 # convert to RGB frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) # do the object detection objects, frame_overlay = detect_objects(frame_rgb, sess, detection_graph) # copy the output frame with the bounding boxes to the output array output_arr[:] = frame_overlay if(len(objects) > 0): print(objects) if __name__ == '__main__': mp.freeze_support() main()