mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-11-21 19:07:46 +01:00
355 lines
13 KiB
Python
Executable File
355 lines
13 KiB
Python
Executable File
import os
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import time
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import datetime
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import cv2
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import queue
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import threading
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import ctypes
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import multiprocessing as mp
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import subprocess as sp
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import numpy as np
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import copy
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import itertools
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import json
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import base64
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from typing import Dict, List
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from collections import defaultdict
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from frigate.config import CameraConfig
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from frigate.util import draw_box_with_label, yuv_region_2_rgb, area, calculate_region, clipped, intersection_over_union, intersection, EventsPerSecond, listen, FrameManager, SharedMemoryFrameManager
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from frigate.objects import ObjectTracker
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from frigate.edgetpu import RemoteObjectDetector
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from frigate.motion import MotionDetector
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def filtered(obj, objects_to_track, object_filters, mask=None):
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object_name = obj[0]
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if not object_name in objects_to_track:
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return True
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if object_name in object_filters:
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obj_settings = object_filters[object_name]
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# if the min area is larger than the
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# detected object, don't add it to detected objects
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if obj_settings.min_area > obj[3]:
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return True
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# if the detected object is larger than the
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# max area, don't add it to detected objects
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if obj_settings.max_area < obj[3]:
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return True
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# if the score is lower than the min_score, skip
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if obj_settings.min_score > obj[1]:
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return True
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# compute the coordinates of the object and make sure
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# the location isnt outside the bounds of the image (can happen from rounding)
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y_location = min(int(obj[2][3]), len(mask)-1)
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x_location = min(int((obj[2][2]-obj[2][0])/2.0)+obj[2][0], len(mask[0])-1)
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# if the object is in a masked location, don't add it to detected objects
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if (not mask is None) and (mask[y_location][x_location] == 0):
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return True
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return False
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def create_tensor_input(frame, region):
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cropped_frame = yuv_region_2_rgb(frame, region)
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# Resize to 300x300 if needed
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if cropped_frame.shape != (300, 300, 3):
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cropped_frame = cv2.resize(cropped_frame, dsize=(300, 300), interpolation=cv2.INTER_LINEAR)
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# Expand dimensions since the model expects images to have shape: [1, 300, 300, 3]
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return np.expand_dims(cropped_frame, axis=0)
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def start_or_restart_ffmpeg(ffmpeg_cmd, frame_size, ffmpeg_process=None):
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if not ffmpeg_process is None:
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print("Terminating the existing ffmpeg process...")
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ffmpeg_process.terminate()
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try:
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print("Waiting for ffmpeg to exit gracefully...")
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ffmpeg_process.communicate(timeout=30)
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except sp.TimeoutExpired:
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print("FFmpeg didnt exit. Force killing...")
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ffmpeg_process.kill()
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ffmpeg_process.communicate()
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ffmpeg_process = None
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print("Creating ffmpeg process...")
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print(" ".join(ffmpeg_cmd))
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process = sp.Popen(ffmpeg_cmd, stdout = sp.PIPE, stdin = sp.DEVNULL, bufsize=frame_size*10, start_new_session=True)
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return process
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def capture_frames(ffmpeg_process, camera_name, frame_shape, frame_manager: FrameManager,
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frame_queue, fps:mp.Value, skipped_fps: mp.Value, current_frame: mp.Value):
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frame_size = frame_shape[0] * frame_shape[1]
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frame_rate = EventsPerSecond()
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frame_rate.start()
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skipped_eps = EventsPerSecond()
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skipped_eps.start()
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while True:
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fps.value = frame_rate.eps()
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skipped_fps = skipped_eps.eps()
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current_frame.value = datetime.datetime.now().timestamp()
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frame_name = f"{camera_name}{current_frame.value}"
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frame_buffer = frame_manager.create(frame_name, frame_size)
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try:
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frame_buffer[:] = ffmpeg_process.stdout.read(frame_size)
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except:
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print(f"{camera_name}: ffmpeg sent a broken frame. something is wrong.")
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if ffmpeg_process.poll() != None:
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print(f"{camera_name}: ffmpeg process is not running. exiting capture thread...")
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frame_manager.delete(frame_name)
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break
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continue
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frame_rate.update()
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# if the queue is full, skip this frame
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if frame_queue.full():
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skipped_eps.update()
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frame_manager.delete(frame_name)
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continue
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# close the frame
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frame_manager.close(frame_name)
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# add to the queue
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frame_queue.put(current_frame.value)
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class CameraWatchdog(threading.Thread):
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def __init__(self, name, config, frame_queue, camera_fps, ffmpeg_pid):
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threading.Thread.__init__(self)
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self.name = name
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self.config = config
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self.capture_thread = None
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self.ffmpeg_process = None
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self.camera_fps = camera_fps
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self.ffmpeg_pid = ffmpeg_pid
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self.frame_queue = frame_queue
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self.frame_shape = self.config.frame_shape_yuv
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self.frame_size = self.frame_shape[0] * self.frame_shape[1]
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def run(self):
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self.start_ffmpeg()
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time.sleep(10)
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while True:
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now = datetime.datetime.now().timestamp()
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if not self.capture_thread.is_alive():
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self.start_ffmpeg()
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elif now - self.capture_thread.current_frame.value > 5:
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print(f"No frames received from {self.name} in 5 seconds. Exiting ffmpeg...")
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self.ffmpeg_process.terminate()
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try:
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print("Waiting for ffmpeg to exit gracefully...")
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self.ffmpeg_process.communicate(timeout=30)
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except sp.TimeoutExpired:
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print("FFmpeg didnt exit. Force killing...")
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self.ffmpeg_process.kill()
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self.ffmpeg_process.communicate()
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# wait a bit before checking again
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time.sleep(10)
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def start_ffmpeg(self):
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self.ffmpeg_process = start_or_restart_ffmpeg(self.config.ffmpeg_cmd, self.frame_size)
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self.ffmpeg_pid.value = self.ffmpeg_process.pid
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self.capture_thread = CameraCapture(self.name, self.ffmpeg_process, self.frame_shape, self.frame_queue,
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self.camera_fps)
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self.capture_thread.start()
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class CameraCapture(threading.Thread):
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def __init__(self, name, ffmpeg_process, frame_shape, frame_queue, fps):
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threading.Thread.__init__(self)
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self.name = name
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self.frame_shape = frame_shape
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self.frame_queue = frame_queue
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self.fps = fps
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self.skipped_fps = EventsPerSecond()
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self.frame_manager = SharedMemoryFrameManager()
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self.ffmpeg_process = ffmpeg_process
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self.current_frame = mp.Value('d', 0.0)
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self.last_frame = 0
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def run(self):
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self.skipped_fps.start()
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capture_frames(self.ffmpeg_process, self.name, self.frame_shape, self.frame_manager, self.frame_queue,
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self.fps, self.skipped_fps, self.current_frame)
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def capture_camera(name, config: CameraConfig, process_info):
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frame_queue = process_info['frame_queue']
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camera_watchdog = CameraWatchdog(name, config, frame_queue, process_info['camera_fps'], process_info['ffmpeg_pid'])
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camera_watchdog.start()
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camera_watchdog.join()
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def track_camera(name, config: CameraConfig, detection_queue, result_connection, detected_objects_queue, process_info):
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listen()
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frame_queue = process_info['frame_queue']
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frame_shape = config.frame_shape
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objects_to_track = config.objects.track
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object_filters = config.objects.filters
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mask = config.mask
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motion_detector = MotionDetector(frame_shape, mask, resize_factor=6)
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object_detector = RemoteObjectDetector(name, '/labelmap.txt', detection_queue, result_connection)
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object_tracker = ObjectTracker(10)
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frame_manager = SharedMemoryFrameManager()
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process_frames(name, frame_queue, frame_shape, frame_manager, motion_detector, object_detector,
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object_tracker, detected_objects_queue, process_info, objects_to_track, object_filters, mask)
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print(f"{name}: exiting subprocess")
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def reduce_boxes(boxes):
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if len(boxes) == 0:
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return []
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reduced_boxes = cv2.groupRectangles([list(b) for b in itertools.chain(boxes, boxes)], 1, 0.2)[0]
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return [tuple(b) for b in reduced_boxes]
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def detect(object_detector, frame, region, objects_to_track, object_filters, mask):
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tensor_input = create_tensor_input(frame, region)
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detections = []
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region_detections = object_detector.detect(tensor_input)
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for d in region_detections:
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box = d[2]
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size = region[2]-region[0]
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x_min = int((box[1] * size) + region[0])
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y_min = int((box[0] * size) + region[1])
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x_max = int((box[3] * size) + region[0])
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y_max = int((box[2] * size) + region[1])
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det = (d[0],
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d[1],
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(x_min, y_min, x_max, y_max),
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(x_max-x_min)*(y_max-y_min),
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region)
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# apply object filters
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if filtered(det, objects_to_track, object_filters, mask):
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continue
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detections.append(det)
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return detections
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def process_frames(camera_name: str, frame_queue: mp.Queue, frame_shape,
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frame_manager: FrameManager, motion_detector: MotionDetector,
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object_detector: RemoteObjectDetector, object_tracker: ObjectTracker,
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detected_objects_queue: mp.Queue, process_info: Dict,
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objects_to_track: List[str], object_filters, mask,
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exit_on_empty: bool = False):
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fps = process_info['process_fps']
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detection_fps = process_info['detection_fps']
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current_frame_time = process_info['detection_frame']
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fps_tracker = EventsPerSecond()
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fps_tracker.start()
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while True:
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if exit_on_empty and frame_queue.empty():
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print(f"Exiting track_objects...")
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break
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try:
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frame_time = frame_queue.get(True, 10)
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except queue.Empty:
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continue
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current_frame_time.value = frame_time
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frame = frame_manager.get(f"{camera_name}{frame_time}", (frame_shape[0]*3//2, frame_shape[1]))
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if frame is None:
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print(f"{camera_name}: frame {frame_time} is not in memory store.")
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continue
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# look for motion
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motion_boxes = motion_detector.detect(frame)
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tracked_object_boxes = [obj['box'] for obj in object_tracker.tracked_objects.values()]
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# combine motion boxes with known locations of existing objects
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combined_boxes = reduce_boxes(motion_boxes + tracked_object_boxes)
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# compute regions
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regions = [calculate_region(frame_shape, a[0], a[1], a[2], a[3], 1.2)
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for a in combined_boxes]
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# combine overlapping regions
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combined_regions = reduce_boxes(regions)
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# re-compute regions
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regions = [calculate_region(frame_shape, a[0], a[1], a[2], a[3], 1.0)
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for a in combined_regions]
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# resize regions and detect
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detections = []
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for region in regions:
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detections.extend(detect(object_detector, frame, region, objects_to_track, object_filters, mask))
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#########
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# merge objects, check for clipped objects and look again up to 4 times
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#########
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refining = True
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refine_count = 0
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while refining and refine_count < 4:
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refining = False
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# group by name
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detected_object_groups = defaultdict(lambda: [])
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for detection in detections:
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detected_object_groups[detection[0]].append(detection)
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selected_objects = []
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for group in detected_object_groups.values():
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# apply non-maxima suppression to suppress weak, overlapping bounding boxes
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boxes = [(o[2][0], o[2][1], o[2][2]-o[2][0], o[2][3]-o[2][1])
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for o in group]
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confidences = [o[1] for o in group]
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idxs = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
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for index in idxs:
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obj = group[index[0]]
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if clipped(obj, frame_shape):
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box = obj[2]
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# calculate a new region that will hopefully get the entire object
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region = calculate_region(frame_shape,
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box[0], box[1],
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box[2], box[3])
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selected_objects.extend(detect(object_detector, frame, region, objects_to_track, object_filters, mask))
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refining = True
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else:
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selected_objects.append(obj)
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# set the detections list to only include top, complete objects
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# and new detections
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detections = selected_objects
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if refining:
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refine_count += 1
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# now that we have refined our detections, we need to track objects
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object_tracker.match_and_update(frame_time, detections)
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# add to the queue if not full
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if(detected_objects_queue.full()):
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frame_manager.delete(f"{camera_name}{frame_time}")
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continue
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else:
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fps_tracker.update()
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fps.value = fps_tracker.eps()
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detected_objects_queue.put((camera_name, frame_time, object_tracker.tracked_objects))
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detection_fps.value = object_detector.fps.eps()
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frame_manager.close(f"{camera_name}{frame_time}")
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