diff --git a/.gitignore b/.gitignore index 2e509347e..8de9cbdf9 100644 --- a/.gitignore +++ b/.gitignore @@ -8,6 +8,7 @@ models *.mp4 *.ts *.db +*.csv frigate/version.py web/build web/node_modules diff --git a/frigate/process_clip.py b/process_clip.py similarity index 71% rename from frigate/process_clip.py rename to process_clip.py index ee9240338..9a2ea30e2 100644 --- a/frigate/process_clip.py +++ b/process_clip.py @@ -1,23 +1,26 @@ -import datetime +import sys +from typing_extensions import runtime + +sys.path.append("/lab/frigate") + import json import logging import multiprocessing as mp import os import subprocess as sp import sys -from unittest import TestCase, main import click +import csv import cv2 import numpy as np -from frigate.config import FRIGATE_CONFIG_SCHEMA, FrigateConfig +from frigate.config import FrigateConfig from frigate.edgetpu import LocalObjectDetector from frigate.motion import MotionDetector from frigate.object_processing import CameraState from frigate.objects import ObjectTracker from frigate.util import ( - DictFrameManager, EventsPerSecond, SharedMemoryFrameManager, draw_box_with_label, @@ -96,20 +99,22 @@ class ProcessClip: ffmpeg_process.wait() ffmpeg_process.communicate() - def process_frames(self, objects_to_track=["person"], object_filters={}): + def process_frames( + self, object_detector, 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 - ) + motion_detector = MotionDetector(self.frame_shape, self.camera_config.motion) + motion_detector.save_images = False - 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), } + + detection_enabled = mp.Value("d", 1) stop_event = mp.Event() model_shape = (self.config.model.height, self.config.model.width) @@ -118,6 +123,7 @@ class ProcessClip: self.frame_queue, self.frame_shape, model_shape, + self.camera_config.detect, self.frame_manager, motion_detector, object_detector, @@ -126,25 +132,16 @@ class ProcessClip: process_info, objects_to_track, object_filters, - mask, + detection_enabled, 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) + def stats(self, debug_path=None): + total_regions = 0 + total_motion_boxes = 0 + object_ids = set() + total_frames = 0 while not self.detected_objects_queue.empty(): ( @@ -154,7 +151,8 @@ class ProcessClip: motion_boxes, regions, ) = self.detected_objects_queue.get() - if not debug_path is None: + + if debug_path: self.save_debug_frame( debug_path, frame_time, current_tracked_objects.values() ) @@ -162,10 +160,22 @@ class ProcessClip: self.camera_state.update( frame_time, current_tracked_objects, motion_boxes, regions ) + total_regions += len(regions) + total_motion_boxes += len(motion_boxes) + for id, obj in self.camera_state.tracked_objects.items(): + if not obj.false_positive: + object_ids.add(id) - self.frame_manager.delete(self.camera_state.previous_frame_id) + total_frames += 1 - return {"object_detected": obj_detected, "top_score": top_computed_score} + self.frame_manager.delete(self.camera_state.previous_frame_id) + + return { + "total_regions": total_regions, + "total_motion_boxes": total_motion_boxes, + "true_positive_objects": len(object_ids), + "total_frames": total_frames, + } def save_debug_frame(self, debug_path, frame_time, tracked_objects): current_frame = cv2.cvtColor( @@ -178,7 +188,6 @@ class ProcessClip: for obj in tracked_objects: thickness = 2 color = (0, 0, 175) - if obj["frame_time"] != frame_time: thickness = 1 color = (255, 0, 0) @@ -221,10 +230,9 @@ class ProcessClip: @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("-o", "--output", default=None, help="File to save csv of data") @click.option("--debug-path", default=None, help="Path to output frames for debugging.") -def process(path, label, threshold, scores, debug_path): +def process(path, label, output, debug_path): clips = [] if os.path.isdir(path): files = os.listdir(path) @@ -235,51 +243,74 @@ def process(path, label, threshold, scores, debug_path): json_config = { "mqtt": {"host": "mqtt"}, + "detectors": {"coral": {"type": "edgetpu", "device": "usb"}}, "cameras": { "camera": { "ffmpeg": { "inputs": [ { "path": "path.mp4", - "global_args": "", - "input_args": "", + "global_args": "-hide_banner", + "input_args": "-loglevel info", "roles": ["detect"], } ] }, - "height": 1920, - "width": 1080, + "rtmp": {"enabled": False}, + "record": {"enabled": False}, } }, } + object_detector = LocalObjectDetector(labels="/labelmap.txt") + 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)) + frigate_config = FrigateConfig(**json_config) + runtime_config = frigate_config.runtime_config - process_clip = ProcessClip(c, frame_shape, config) + process_clip = ProcessClip(c, frame_shape, runtime_config) process_clip.load_frames() - process_clip.process_frames(objects_to_track=[label]) + process_clip.process_frames(object_detector, objects_to_track=[label]) - results.append((c, process_clip.top_object(debug_path))) + results.append((c, process_clip.stats(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"]) + positive_count = sum( + 1 for result in results if result[1]["true_positive_objects"] > 0 + ) print( f"Objects were detected in {positive_count}/{len(results)}({positive_count/len(results)*100:.2f}%) clip(s)." ) + if output: + # now we will open a file for writing + data_file = open(output, "w") + + # create the csv writer object + csv_writer = csv.writer(data_file) + + # Counter variable used for writing + # headers to the CSV file + count = 0 + + for result in results: + if count == 0: + + # Writing headers of CSV file + header = ["file"] + list(result[1].keys()) + csv_writer.writerow(header) + count += 1 + + # Writing data of CSV file + csv_writer.writerow([result[0]] + list(result[1].values())) + + data_file.close() + if __name__ == "__main__": process()