frigate/detect_objects.py

import os
import cv2
import imutils
import time
import datetime
import ctypes
import logging
import multiprocessing as mp
import threading
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

REGIONS = "350,0,300:400,350,250:400,750,250"
#REGIONS = os.getenv('REGIONS')

DETECTED_OBJECTS = []

# 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(cropped_frame, sess, detection_graph, region_size, region_x_offset, region_y_offset):
    # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
    image_np_expanded = np.expand_dims(cropped_frame, 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]):
        score = scores[0, index]
        if score > 0.1:
            box = boxes[0, index].tolist()
            box[0] = (box[0] * region_size) + region_y_offset
            box[1] = (box[1] * region_size) + region_x_offset
            box[2] = (box[2] * region_size) + region_y_offset
            box[3] = (box[3] * region_size) + region_x_offset
            objects += [value, scores[0, index]] + box
        # only get the first 10 objects
        if len(objects) == 60:
            break

    return objects

class ObjectParser(threading.Thread):
    def __init__(self, object_arrays):
        threading.Thread.__init__(self)
        self._object_arrays = object_arrays

    def run(self):
        global DETECTED_OBJECTS
        while True:
            detected_objects = []
            for object_array in self._object_arrays:
                object_index = 0
                while(object_index < 60 and object_array[object_index] > 0):
                    object_class = object_array[object_index]
                    detected_objects.append({
                        'name': str(category_index.get(object_class).get('name')),
                        'score': object_array[object_index+1],
                        'ymin': int(object_array[object_index+2]),
                        'xmin': int(object_array[object_index+3]),
                        'ymax': int(object_array[object_index+4]),
                        'xmax': int(object_array[object_index+5])
                    })
                    object_index += 6
            DETECTED_OBJECTS = detected_objects
            time.sleep(0.01)

def main():
    # Parse selected regions
    regions = []
    for region_string in REGIONS.split(':'):
        region_parts = region_string.split(',')
        regions.append({
            'size': int(region_parts[0]),
            'x_offset': int(region_parts[1]),
            'y_offset': int(region_parts[2])
        })
    # 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()

    shared_memory_objects = []
    for region in regions:
        shared_memory_objects.append({
            # create shared value for storing the time the frame was captured
            'frame_time': mp.Value('d', 0.0),
            # shared value for signaling to the capture process that we are ready for the next frame
            # (1 for ready 0 for not ready)
            'ready_for_frame': mp.Value('i', 1),
            # shared value for motion detection signal (1 for motion 0 for no motion)
            'motion_detected': mp.Value('i', 0),
            # create shared array for storing 10 detected objects
            # 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
            'output_array': mp.Array(ctypes.c_double, 6*10)
        })
        
    # 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 storing the full frame image data
    shared_arr = mp.Array(ctypes.c_uint16, flat_array_length)
    # shape current frame so it can be treated as an image
    frame_arr = tonumpyarray(shared_arr).reshape(frame_shape)

    capture_process = mp.Process(target=fetch_frames, args=(shared_arr, [obj['frame_time'] for obj in shared_memory_objects], frame_shape))
    capture_process.daemon = True

    detection_processes = []
    for index, region in enumerate(regions):
        detection_process = mp.Process(target=process_frames, args=(shared_arr, 
            shared_memory_objects[index]['output_array'],
            shared_memory_objects[index]['frame_time'],
            shared_memory_objects[index]['motion_detected'],
            frame_shape, 
            region['size'], region['x_offset'], region['y_offset']))
        detection_process.daemon = True
        detection_processes.append(detection_process)

    motion_processes = []
    for index, region in enumerate(regions):
        motion_process = mp.Process(target=detect_motion, args=(shared_arr, 
            shared_memory_objects[index]['frame_time'],
            shared_memory_objects[index]['motion_detected'],
            frame_shape, 
            region['size'], region['x_offset'], region['y_offset']))
        motion_process.daemon = True
        motion_processes.append(motion_process)

    object_parser = ObjectParser([obj['output_array'] for obj in shared_memory_objects])
    object_parser.start()

    capture_process.start()
    print("capture_process pid ", capture_process.pid)
    for detection_process in detection_processes:
        detection_process.start()
        print("detection_process pid ", detection_process.pid)
    for motion_process in motion_processes:
        motion_process.start()
        print("motion_process pid ", motion_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():
        global DETECTED_OBJECTS
        while True:
            # max out at 5 FPS
            time.sleep(0.2)
            # make a copy of the current detected objects
            detected_objects = DETECTED_OBJECTS.copy()
            # make a copy of the current frame
            frame = frame_arr.copy()
            # convert to RGB for drawing
            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
            # draw the bounding boxes on the screen
            for obj in DETECTED_OBJECTS:
                vis_util.draw_bounding_box_on_image_array(frame,
                    obj['ymin'],
                    obj['xmin'],
                    obj['ymax'],
                    obj['xmax'],
                    color='red',
                    thickness=2,
                    display_str_list=["{}: {}%".format(obj['name'],int(obj['score']*100))],
                    use_normalized_coordinates=False)

            for region in regions:
                cv2.rectangle(frame, (region['x_offset'], region['y_offset']), 
                    (region['x_offset']+region['size'], region['y_offset']+region['size']), 
                    (255,255,255), 2)
            # convert back to BGR
            frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
            # encode the image into a jpg
            ret, jpg = cv2.imencode('.jpg', frame)
            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()
    for detection_process in detection_processes:
        detection_process.join()
    for motion_process in motion_processes:
        motion_process.join()
    object_parser.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_times, 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 all(shared_frame_time.value == 0.0 for shared_frame_time in shared_frame_times):
                # go ahead and decode the current frame
                ret, frame = video.retrieve()
                if ret:
                    arr[:] = frame
                    # signal to the detection_processes by setting the shared_frame_time
                    for shared_frame_time in shared_frame_times:
                        shared_frame_time.value = frame_time.timestamp()
            else:
                # sleep a little to reduce CPU usage
                time.sleep(0.01)
    
    video.release()

# do the actual object detection
def process_frames(shared_arr, shared_output_arr, shared_frame_time, shared_motion, frame_shape, region_size, region_x_offset, region_y_offset):
    # shape shared input array into frame for processing
    arr = tonumpyarray(shared_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
    frame_time = 0.0
    while True:
        now = datetime.datetime.now().timestamp()
        # if there is no motion detected
        if shared_motion.value == 0:
            time.sleep(0.01)
            continue

        # if there isnt a new frame ready for processing
        if shared_frame_time.value == frame_time:
            # save the first time there were no frames available
            if no_frames_available == -1:
                no_frames_available = now
            # 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 (now - 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, ignore it
        if (now - shared_frame_time.value) > 0.5:
            # rest a little bit to avoid maxing out the CPU
            time.sleep(0.01)
            continue
        
        # make a copy of the cropped frame
        cropped_frame = arr[region_y_offset:region_y_offset+region_size, region_x_offset:region_x_offset+region_size].copy()
        frame_time = shared_frame_time.value

        # convert to RGB
        cropped_frame_rgb = cv2.cvtColor(cropped_frame, cv2.COLOR_BGR2RGB)
        # do the object detection
        objects = detect_objects(cropped_frame_rgb, sess, detection_graph, region_size, region_x_offset, region_y_offset)
        # copy the detected objects to the output array, filling the array when needed
        shared_output_arr[:] = objects + [0.0] * (60-len(objects))

# do the actual object detection
def detect_motion(shared_arr, shared_frame_time, shared_motion, frame_shape, region_size, region_x_offset, region_y_offset):
    # shape shared input array into frame for processing
    arr = tonumpyarray(shared_arr).reshape(frame_shape)

    no_frames_available = -1
    avg_frame = None
    last_motion = -1
    while True:
        now = datetime.datetime.now().timestamp()
        # if it has been 30 seconds since the last motion, clear the flag
        if last_motion > 0 and (now - last_motion) > 30:
            last_motion = -1
            shared_motion.value = 0
            print("motion cleared")
        # 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 = now
            # 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 (now - 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 (now - 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 cropped frame
        cropped_frame = arr[region_y_offset:region_y_offset+region_size, region_x_offset:region_x_offset+region_size].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 grayscale
        gray = cv2.cvtColor(cropped_frame, cv2.COLOR_BGR2GRAY)
        # convert to uint8
        gray = (gray/256).astype('uint8')
        # apply gaussian blur
        gray = cv2.GaussianBlur(gray, (21, 21), 0)

        if avg_frame is None:
            avg_frame = gray.copy().astype("float")
            continue
        
        # look at the delta from the avg_frame
        cv2.accumulateWeighted(gray, avg_frame, 0.5)
        frameDelta = cv2.absdiff(gray, cv2.convertScaleAbs(avg_frame))
        thresh = cv2.threshold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1]
 
        # dilate the thresholded image to fill in holes, then find contours
        # on thresholded image
        thresh = cv2.dilate(thresh, None, iterations=2)
        cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
            cv2.CHAIN_APPROX_SIMPLE)
        cnts = imutils.grab_contours(cnts)

        # loop over the contours
        for c in cnts:
            # if the contour is too small, ignore it
            if cv2.contourArea(c) < 50:
                continue
            
            print("motion_detected")
            last_motion = now
            shared_motion.value = 1

            # compute the bounding box for the contour, draw it on the frame,
            # and update the text
            (x, y, w, h) = cv2.boundingRect(c)
            cv2.rectangle(cropped_frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
            cv2.imwrite("motion%d.png" % frame_time, cropped_frame)
if __name__ == '__main__':
    mp.freeze_support()
    main()