import os import time import datetime import cv2 import queue import threading import ctypes import multiprocessing as mp import subprocess as sp import numpy as np import hashlib import pyarrow.plasma as plasma import SharedArray as sa import copy import itertools import json from collections import defaultdict from frigate.util import draw_box_with_label, area, calculate_region, clipped, intersection_over_union, intersection, EventsPerSecond from frigate.objects import ObjectTracker from frigate.edgetpu import RemoteObjectDetector from frigate.motion import MotionDetector def get_frame_shape(source): ffprobe_cmd = " ".join([ 'ffprobe', '-v', 'panic', '-show_error', '-show_streams', '-of', 'json', '"'+source+'"' ]) print(ffprobe_cmd) p = sp.Popen(ffprobe_cmd, stdout=sp.PIPE, shell=True) (output, err) = p.communicate() p_status = p.wait() info = json.loads(output) print(info) video_info = [s for s in info['streams'] if s['codec_type'] == 'video'][0] if video_info['height'] != 0 and video_info['width'] != 0: return (video_info['height'], video_info['width'], 3) # fallback to using opencv if ffprobe didnt succeed video = cv2.VideoCapture(source) ret, frame = video.read() frame_shape = frame.shape video.release() return frame_shape def get_ffmpeg_input(ffmpeg_input): frigate_vars = {k: v for k, v in os.environ.items() if k.startswith('FRIGATE_')} return ffmpeg_input.format(**frigate_vars) def filtered(obj, objects_to_track, object_filters, mask): object_name = obj[0] if not object_name in objects_to_track: return True if object_name in object_filters: obj_settings = object_filters[object_name] # if the min area is larger than the # detected object, don't add it to detected objects if obj_settings.get('min_area',-1) > obj[3]: return True # if the detected object is larger than the # max area, don't add it to detected objects if obj_settings.get('max_area', 24000000) < obj[3]: return True # if the score is lower than the threshold, skip if obj_settings.get('threshold', 0) > obj[1]: return True # compute the coordinates of the object and make sure # the location isnt outside the bounds of the image (can happen from rounding) y_location = min(int(obj[2][3]), len(mask)-1) x_location = min(int((obj[2][2]-obj[2][0])/2.0)+obj[2][0], len(mask[0])-1) # if the object is in a masked location, don't add it to detected objects if mask[y_location][x_location] == [0]: return True return False def create_tensor_input(frame, region): cropped_frame = frame[region[1]:region[3], region[0]:region[2]] # Resize to 300x300 if needed if cropped_frame.shape != (300, 300, 3): cropped_frame = cv2.resize(cropped_frame, dsize=(300, 300), interpolation=cv2.INTER_LINEAR) # Expand dimensions since the model expects images to have shape: [1, 300, 300, 3] return np.expand_dims(cropped_frame, axis=0) def track_camera(name, config, ffmpeg_global_config, global_objects_config, detect_lock, detect_ready, frame_ready, detected_objects_queue, fps, skipped_fps, detection_fps): print(f"Starting process for {name}: {os.getpid()}") # Merge the ffmpeg config with the global config ffmpeg = config.get('ffmpeg', {}) ffmpeg_input = get_ffmpeg_input(ffmpeg['input']) ffmpeg_global_args = ffmpeg.get('global_args', ffmpeg_global_config['global_args']) ffmpeg_hwaccel_args = ffmpeg.get('hwaccel_args', ffmpeg_global_config['hwaccel_args']) ffmpeg_input_args = ffmpeg.get('input_args', ffmpeg_global_config['input_args']) ffmpeg_output_args = ffmpeg.get('output_args', ffmpeg_global_config['output_args']) # Merge the tracked object config with the global config camera_objects_config = config.get('objects', {}) # combine tracked objects lists objects_to_track = set().union(global_objects_config.get('track', ['person', 'car', 'truck']), camera_objects_config.get('track', [])) # merge object filters global_object_filters = global_objects_config.get('filters', {}) camera_object_filters = camera_objects_config.get('filters', {}) objects_with_config = set().union(global_object_filters.keys(), camera_object_filters.keys()) object_filters = {} for obj in objects_with_config: object_filters[obj] = {**global_object_filters.get(obj, {}), **camera_object_filters.get(obj, {})} expected_fps = config['fps'] take_frame = config.get('take_frame', 1) if 'width' in config and 'height' in config: frame_shape = (config['height'], config['width'], 3) else: frame_shape = get_frame_shape(ffmpeg_input) frame_size = frame_shape[0] * frame_shape[1] * frame_shape[2] try: sa.delete(name) except: pass frame = sa.create(name, shape=frame_shape, dtype=np.uint8) # load in the mask for object detection if 'mask' in config: mask = cv2.imread("/config/{}".format(config['mask']), cv2.IMREAD_GRAYSCALE) else: mask = None if mask is None: mask = np.zeros((frame_shape[0], frame_shape[1], 1), np.uint8) mask[:] = 255 motion_detector = MotionDetector(frame_shape, mask, resize_factor=6) object_detector = RemoteObjectDetector('/labelmap.txt', detect_lock, detect_ready, frame_ready) object_tracker = ObjectTracker(10) ffmpeg_cmd = (['ffmpeg'] + ffmpeg_global_args + ffmpeg_hwaccel_args + ffmpeg_input_args + ['-i', ffmpeg_input] + ffmpeg_output_args + ['pipe:']) print(" ".join(ffmpeg_cmd)) ffmpeg_process = sp.Popen(ffmpeg_cmd, stdout = sp.PIPE, bufsize=frame_size) plasma_client = plasma.connect("/tmp/plasma") frame_num = 0 avg_wait = 0.0 fps_tracker = EventsPerSecond() skipped_fps_tracker = EventsPerSecond() fps_tracker.start() skipped_fps_tracker.start() object_detector.fps.start() while True: start = datetime.datetime.now().timestamp() frame_bytes = ffmpeg_process.stdout.read(frame_size) duration = datetime.datetime.now().timestamp()-start avg_wait = (avg_wait*99+duration)/100 if not frame_bytes: break # limit frame rate frame_num += 1 if (frame_num % take_frame) != 0: continue fps_tracker.update() fps.value = fps_tracker.eps() detection_fps.value = object_detector.fps.eps() frame_time = datetime.datetime.now().timestamp() # Store frame in numpy array frame[:] = (np .frombuffer(frame_bytes, np.uint8) .reshape(frame_shape)) # look for motion motion_boxes = motion_detector.detect(frame) # skip object detection if we are below the min_fps and wait time is less than half the average if frame_num > 100 and fps.value < expected_fps-1 and duration < 0.5*avg_wait: skipped_fps_tracker.update() skipped_fps.value = skipped_fps_tracker.eps() continue skipped_fps.value = skipped_fps_tracker.eps() tracked_objects = object_tracker.tracked_objects.values() # merge areas of motion that intersect with a known tracked object into a single area to look at areas_of_interest = [] used_motion_boxes = [] for obj in tracked_objects: x_min, y_min, x_max, y_max = obj['box'] for m_index, motion_box in enumerate(motion_boxes): if area(intersection(obj['box'], motion_box))/area(motion_box) > .5: used_motion_boxes.append(m_index) x_min = min(obj['box'][0], motion_box[0]) y_min = min(obj['box'][1], motion_box[1]) x_max = max(obj['box'][2], motion_box[2]) y_max = max(obj['box'][3], motion_box[3]) areas_of_interest.append((x_min, y_min, x_max, y_max)) unused_motion_boxes = set(range(0, len(motion_boxes))).difference(used_motion_boxes) # compute motion regions motion_regions = [calculate_region(frame_shape, motion_boxes[i][0], motion_boxes[i][1], motion_boxes[i][2], motion_boxes[i][3], 1.2) for i in unused_motion_boxes] # compute tracked object regions object_regions = [calculate_region(frame_shape, a[0], a[1], a[2], a[3], 1.2) for a in areas_of_interest] # merge regions with high IOU merged_regions = motion_regions+object_regions while True: max_iou = 0.0 max_indices = None region_indices = range(len(merged_regions)) for a, b in itertools.combinations(region_indices, 2): iou = intersection_over_union(merged_regions[a], merged_regions[b]) if iou > max_iou: max_iou = iou max_indices = (a, b) if max_iou > 0.1: a = merged_regions[max_indices[0]] b = merged_regions[max_indices[1]] merged_regions.append(calculate_region(frame_shape, min(a[0], b[0]), min(a[1], b[1]), max(a[2], b[2]), max(a[3], b[3]), 1 )) del merged_regions[max(max_indices[0], max_indices[1])] del merged_regions[min(max_indices[0], max_indices[1])] else: break # resize regions and detect detections = [] for region in merged_regions: tensor_input = create_tensor_input(frame, region) region_detections = object_detector.detect(tensor_input) for d in region_detections: box = d[2] size = region[2]-region[0] x_min = int((box[1] * size) + region[0]) y_min = int((box[0] * size) + region[1]) x_max = int((box[3] * size) + region[0]) y_max = int((box[2] * size) + region[1]) det = (d[0], d[1], (x_min, y_min, x_max, y_max), (x_max-x_min)*(y_max-y_min), region) if filtered(det, objects_to_track, object_filters, mask): continue detections.append(det) ######### # merge objects, check for clipped objects and look again up to N times ######### refining = True refine_count = 0 while refining and refine_count < 4: refining = False # group by name detected_object_groups = defaultdict(lambda: []) for detection in detections: detected_object_groups[detection[0]].append(detection) selected_objects = [] for group in detected_object_groups.values(): # apply non-maxima suppression to suppress weak, overlapping bounding boxes boxes = [(o[2][0], o[2][1], o[2][2]-o[2][0], o[2][3]-o[2][1]) for o in group] confidences = [o[1] for o in group] idxs = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4) for index in idxs: obj = group[index[0]] if clipped(obj, frame_shape): #obj['clipped']: box = obj[2] # calculate a new region that will hopefully get the entire object region = calculate_region(frame_shape, box[0], box[1], box[2], box[3]) tensor_input = create_tensor_input(frame, region) # run detection on new region refined_detections = object_detector.detect(tensor_input) for d in refined_detections: box = d[2] size = region[2]-region[0] x_min = int((box[1] * size) + region[0]) y_min = int((box[0] * size) + region[1]) x_max = int((box[3] * size) + region[0]) y_max = int((box[2] * size) + region[1]) det = (d[0], d[1], (x_min, y_min, x_max, y_max), (x_max-x_min)*(y_max-y_min), region) if filtered(det, objects_to_track, object_filters, mask): continue selected_objects.append(det) refining = True else: selected_objects.append(obj) # set the detections list to only include top, complete objects # and new detections detections = selected_objects if refining: refine_count += 1 # now that we have refined our detections, we need to track objects object_tracker.match_and_update(frame_time, detections) # put the frame in the plasma store object_id = hashlib.sha1(str.encode(f"{name}{frame_time}")).digest() plasma_client.put(frame, plasma.ObjectID(object_id)) # add to the queue detected_objects_queue.put((name, frame_time, object_tracker.tracked_objects))