import datetime import json import logging import multiprocessing as mp import os import subprocess as sp import sys from unittest import TestCase, main import click import cv2 import numpy as np from frigate.config import FRIGATE_CONFIG_SCHEMA, FrigateConfig from frigate.edgetpu import LocalObjectDetector from frigate.motion import MotionDetector from frigate.object_processing import COLOR_MAP, CameraState from frigate.objects import ObjectTracker from frigate.util import ( DictFrameManager, EventsPerSecond, SharedMemoryFrameManager, draw_box_with_label, ) from frigate.video import capture_frames, process_frames, start_or_restart_ffmpeg logging.basicConfig() logging.root.setLevel(logging.DEBUG) logger = logging.getLogger(__name__) def get_frame_shape(source): ffprobe_cmd = " ".join( [ "ffprobe", "-v", "panic", "-show_error", "-show_streams", "-of", "json", '"' + source + '"', ] ) p = sp.Popen(ffprobe_cmd, stdout=sp.PIPE, shell=True) (output, err) = p.communicate() p_status = p.wait() info = json.loads(output) 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 class ProcessClip: def __init__(self, clip_path, frame_shape, config: FrigateConfig): self.clip_path = clip_path self.camera_name = "camera" self.config = config self.camera_config = self.config.cameras["camera"] self.frame_shape = self.camera_config.frame_shape self.ffmpeg_cmd = [ c["cmd"] for c in self.camera_config.ffmpeg_cmds if "detect" in c["roles"] ][0] self.frame_manager = SharedMemoryFrameManager() self.frame_queue = mp.Queue() self.detected_objects_queue = mp.Queue() self.camera_state = CameraState(self.camera_name, config, self.frame_manager) def load_frames(self): fps = EventsPerSecond() skipped_fps = EventsPerSecond() current_frame = mp.Value("d", 0.0) frame_size = ( self.camera_config.frame_shape_yuv[0] * self.camera_config.frame_shape_yuv[1] ) ffmpeg_process = start_or_restart_ffmpeg( self.ffmpeg_cmd, logger, sp.DEVNULL, frame_size ) capture_frames( ffmpeg_process, self.camera_name, self.camera_config.frame_shape_yuv, self.frame_manager, self.frame_queue, fps, skipped_fps, current_frame, ) ffmpeg_process.wait() ffmpeg_process.communicate() def process_frames(self, objects_to_track=["person"], object_filters={}): mask = np.zeros((self.frame_shape[0], self.frame_shape[1], 1), np.uint8) mask[:] = 255 motion_detector = MotionDetector( self.frame_shape, mask, self.camera_config.motion ) object_detector = LocalObjectDetector(labels="/labelmap.txt") object_tracker = ObjectTracker(self.camera_config.detect) process_info = { "process_fps": mp.Value("d", 0.0), "detection_fps": mp.Value("d", 0.0), "detection_frame": mp.Value("d", 0.0), } stop_event = mp.Event() model_shape = (self.config.model.height, self.config.model.width) process_frames( self.camera_name, self.frame_queue, self.frame_shape, model_shape, self.frame_manager, motion_detector, object_detector, object_tracker, self.detected_objects_queue, process_info, objects_to_track, object_filters, mask, stop_event, exit_on_empty=True, ) def top_object(self, debug_path=None): obj_detected = False top_computed_score = 0.0 def handle_event(name, obj, frame_time): nonlocal obj_detected nonlocal top_computed_score if obj.computed_score > top_computed_score: top_computed_score = obj.computed_score if not obj.false_positive: obj_detected = True self.camera_state.on("new", handle_event) self.camera_state.on("update", handle_event) while not self.detected_objects_queue.empty(): ( camera_name, frame_time, current_tracked_objects, motion_boxes, regions, ) = self.detected_objects_queue.get() if not debug_path is None: self.save_debug_frame( debug_path, frame_time, current_tracked_objects.values() ) self.camera_state.update( frame_time, current_tracked_objects, motion_boxes, regions ) self.frame_manager.delete(self.camera_state.previous_frame_id) return {"object_detected": obj_detected, "top_score": top_computed_score} def save_debug_frame(self, debug_path, frame_time, tracked_objects): current_frame = cv2.cvtColor( self.frame_manager.get( f"{self.camera_name}{frame_time}", self.camera_config.frame_shape_yuv ), cv2.COLOR_YUV2BGR_I420, ) # draw the bounding boxes on the frame for obj in tracked_objects: thickness = 2 color = (0, 0, 175) if obj["frame_time"] != frame_time: thickness = 1 color = (255, 0, 0) else: color = (255, 255, 0) # draw the bounding boxes on the frame box = obj["box"] draw_box_with_label( current_frame, box[0], box[1], box[2], box[3], obj["id"], f"{int(obj['score']*100)}% {int(obj['area'])}", thickness=thickness, color=color, ) # draw the regions on the frame region = obj["region"] draw_box_with_label( current_frame, region[0], region[1], region[2], region[3], "region", "", thickness=1, color=(0, 255, 0), ) cv2.imwrite( f"{os.path.join(debug_path, os.path.basename(self.clip_path))}.{int(frame_time*1000000)}.jpg", current_frame, ) @click.command() @click.option("-p", "--path", required=True, help="Path to clip or directory to test.") @click.option("-l", "--label", default="person", help="Label name to detect.") @click.option("-t", "--threshold", default=0.85, help="Threshold value for objects.") @click.option("-s", "--scores", default=None, help="File to save csv of top scores") @click.option("--debug-path", default=None, help="Path to output frames for debugging.") def process(path, label, threshold, scores, debug_path): clips = [] if os.path.isdir(path): files = os.listdir(path) files.sort() clips = [os.path.join(path, file) for file in files] elif os.path.isfile(path): clips.append(path) json_config = { "mqtt": {"host": "mqtt"}, "cameras": { "camera": { "ffmpeg": { "inputs": [ { "path": "path.mp4", "global_args": "", "input_args": "", "roles": ["detect"], } ] }, "height": 1920, "width": 1080, } }, } results = [] for c in clips: logger.info(c) frame_shape = get_frame_shape(c) json_config["cameras"]["camera"]["height"] = frame_shape[0] json_config["cameras"]["camera"]["width"] = frame_shape[1] json_config["cameras"]["camera"]["ffmpeg"]["inputs"][0]["path"] = c config = FrigateConfig(config=FRIGATE_CONFIG_SCHEMA(json_config)) process_clip = ProcessClip(c, frame_shape, config) process_clip.load_frames() process_clip.process_frames(objects_to_track=[label]) results.append((c, process_clip.top_object(debug_path))) if not scores is None: with open(scores, "w") as writer: for result in results: writer.write(f"{result[0]},{result[1]['top_score']}\n") positive_count = sum(1 for result in results if result[1]["object_detected"]) print( f"Objects were detected in {positive_count}/{len(results)}({positive_count/len(results)*100:.2f}%) clip(s)." ) if __name__ == "__main__": process()