blakeblackshear.frigate/detect_objects.py

import os
import cv2
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

REGION_SIZE = 300
REGION_X_OFFSET = 1250
REGION_Y_OFFSET = 180

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():
    # 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 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)
    # create shared array for storing 10 detected objects
    shared_output_arr = mp.Array(ctypes.c_double, 6*10)

    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, REGION_SIZE, REGION_X_OFFSET, REGION_Y_OFFSET))
    detection_process.daemon = True

    object_parser = ObjectParser([shared_output_arr])
    object_parser.start()

    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():
        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)
            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()
    detection_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_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:
                    arr[:] = frame
                    # signal to the detection_process by setting the shared_frame_time
                    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, 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
    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 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 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))

if __name__ == '__main__':
    mp.freeze_support()
    main()