import os import time import datetime import cv2 import queue import threading import ctypes import pyarrow.plasma as plasma import multiprocessing as mp import subprocess as sp import numpy as np import copy import itertools import json import base64 from collections import defaultdict from frigate.util import draw_box_with_label, area, calculate_region, clipped, intersection_over_union, intersection, EventsPerSecond, listen, PlasmaManager 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 start_or_restart_ffmpeg(ffmpeg_cmd, frame_size, ffmpeg_process=None): if not ffmpeg_process is None: print("Terminating the existing ffmpeg process...") ffmpeg_process.terminate() try: print("Waiting for ffmpeg to exit gracefully...") ffmpeg_process.communicate(timeout=30) except sp.TimeoutExpired: print("FFmpeg didnt exit. Force killing...") ffmpeg_process.kill() ffmpeg_process.communicate() ffmpeg_process = None print("Creating ffmpeg process...") print(" ".join(ffmpeg_cmd)) process = sp.Popen(ffmpeg_cmd, stdout = sp.PIPE, stdin = sp.DEVNULL, bufsize=frame_size*10, start_new_session=True) return process class CameraCapture(threading.Thread): def __init__(self, name, ffmpeg_process, frame_shape, frame_queue, take_frame, fps, detection_frame, stop_event): threading.Thread.__init__(self) self.name = name self.frame_shape = frame_shape self.frame_size = frame_shape[0] * frame_shape[1] * frame_shape[2] self.frame_queue = frame_queue self.take_frame = take_frame self.fps = fps self.skipped_fps = EventsPerSecond() self.plasma_client = PlasmaManager(stop_event) self.ffmpeg_process = ffmpeg_process self.current_frame = 0 self.last_frame = 0 self.detection_frame = detection_frame self.stop_event = stop_event def run(self): frame_num = 0 self.skipped_fps.start() while True: if self.stop_event.is_set(): print(f"{self.name}: stop event set. exiting capture thread...") break if self.ffmpeg_process.poll() != None: print(f"{self.name}: ffmpeg process is not running. exiting capture thread...") break frame_bytes = self.ffmpeg_process.stdout.read(self.frame_size) self.current_frame = datetime.datetime.now().timestamp() if len(frame_bytes) == 0: print(f"{self.name}: ffmpeg didnt return a frame. something is wrong.") continue self.fps.update() frame_num += 1 if (frame_num % self.take_frame) != 0: self.skipped_fps.update() continue # if the detection process is more than 1 second behind, skip this frame if self.detection_frame.value > 0.0 and (self.last_frame - self.detection_frame.value) > 1: self.skipped_fps.update() continue # put the frame in the plasma store self.plasma_client.put(f"{self.name}{self.current_frame}", np .frombuffer(frame_bytes, np.uint8) .reshape(self.frame_shape) ) # add to the queue self.frame_queue.put(self.current_frame) self.last_frame = self.current_frame def track_camera(name, config, global_objects_config, frame_queue, frame_shape, detection_queue, detected_objects_queue, fps, detection_fps, read_start, detection_frame): print(f"Starting process for {name}: {os.getpid()}") listen() detection_frame.value = 0.0 # 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, {})} frame = np.zeros(frame_shape, np.uint8) # load in the mask for object detection if 'mask' in config: if config['mask'].startswith('base64,'): img = base64.b64decode(config['mask'][7:]) npimg = np.fromstring(img, dtype=np.uint8) mask = cv2.imdecode(npimg, cv2.IMREAD_GRAYSCALE) else: 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(name, '/labelmap.txt', detection_queue) object_tracker = ObjectTracker(10) plasma_client = PlasmaManager() avg_wait = 0.0 fps_tracker = EventsPerSecond() fps_tracker.start() object_detector.fps.start() while True: read_start.value = datetime.datetime.now().timestamp() frame_time = frame_queue.get() duration = datetime.datetime.now().timestamp()-read_start.value read_start.value = 0.0 avg_wait = (avg_wait*99+duration)/100 detection_frame.value = frame_time # Get frame from plasma store frame = plasma_client.get(f"{name}{frame_time}") if frame is plasma.ObjectNotAvailable: continue fps_tracker.update() fps.value = fps_tracker.eps() detection_fps.value = object_detector.fps.eps() # look for motion motion_boxes = motion_detector.detect(frame) 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 intersection_over_union(motion_box, obj['box']) > .2: 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): 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) # add to the queue detected_objects_queue.put((name, frame_time, object_tracker.tracked_objects)) print(f"{name}: exiting subprocess")