blakeblackshear.frigate/frigate/video.py

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 prctl
from collections import defaultdict
from . util import tonumpyarray, LABELS, draw_box_with_label, calculate_region, EventsPerSecond
from . object_detection import RegionPrepper, RegionRequester
from . objects import ObjectCleaner, BestFrames
from . mqtt import MqttObjectPublisher

# Stores 2 seconds worth of frames so they can be used for other threads
class FrameTracker(threading.Thread):
    def __init__(self, shared_frame, frame_time, frame_ready, frame_lock, recent_frames):
        threading.Thread.__init__(self)
        self.shared_frame = shared_frame
        self.frame_time = frame_time
        self.frame_ready = frame_ready
        self.frame_lock = frame_lock
        self.recent_frames = recent_frames

    def run(self):
        prctl.set_name("FrameTracker")
        while True:
            # wait for a frame
            with self.frame_ready:
                self.frame_ready.wait()

            now = datetime.datetime.now().timestamp()
            # delete any old frames
            stored_frame_times = list(self.recent_frames.keys())
            for k in stored_frame_times:
                if (now - k) > 2:
                    del self.recent_frames[k]

def get_frame_shape(source):
    # capture a single frame and check the frame shape so the correct array
    # size can be allocated in memory
    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)

class CameraWatchdog(threading.Thread):
    def __init__(self, camera):
        threading.Thread.__init__(self)
        self.camera = camera

    def run(self):
        prctl.set_name("CameraWatchdog")
        while True:
            # wait a bit before checking
            time.sleep(10)

            if (datetime.datetime.now().timestamp() - self.camera.frame_time.value) > 300:
                print("last frame is more than 5 minutes old, restarting camera capture...")
                self.camera.start_or_restart_capture()
                time.sleep(5)

# Thread to read the stdout of the ffmpeg process and update the current frame
class CameraCapture(threading.Thread):
    def __init__(self, camera):
        threading.Thread.__init__(self)
        self.camera = camera

    def run(self):
        prctl.set_name("CameraCapture")
        frame_num = 0
        while True:
            if self.camera.ffmpeg_process.poll() != None:
                print("ffmpeg process is not running. exiting capture thread...")
                break

            raw_image = self.camera.ffmpeg_process.stdout.read(self.camera.frame_size)

            if len(raw_image) == 0:
                print("ffmpeg didnt return a frame. something is wrong. exiting capture thread...")
                break

            frame_num += 1
            if (frame_num % self.camera.take_frame) != 0:
                continue

            with self.camera.frame_lock:
                self.camera.frame_time.value = datetime.datetime.now().timestamp()
                
                self.camera.current_frame[:] = (
                    np
                    .frombuffer(raw_image, np.uint8)
                    .reshape(self.camera.frame_shape)
                )
                self.camera.frame_cache[self.camera.frame_time.value] = self.camera.current_frame.copy()
            # Notify with the condition that a new frame is ready
            with self.camera.frame_ready:
                self.camera.frame_ready.notify_all()

            self.camera.fps.update()

class Camera:
    def __init__(self, name, ffmpeg_config, global_objects_config, config, prepped_frame_queue, mqtt_client, mqtt_prefix):
        self.name = name
        self.config = config
        self.detected_objects = []
        self.frame_cache = {}

        self.ffmpeg = config.get('ffmpeg', {})
        self.ffmpeg_input = get_ffmpeg_input(self.ffmpeg['input'])
        self.ffmpeg_global_args = self.ffmpeg.get('global_args', ffmpeg_config['global_args'])
        self.ffmpeg_hwaccel_args = self.ffmpeg.get('hwaccel_args', ffmpeg_config['hwaccel_args'])
        self.ffmpeg_input_args = self.ffmpeg.get('input_args', ffmpeg_config['input_args'])
        self.ffmpeg_output_args = self.ffmpeg.get('output_args', ffmpeg_config['output_args'])

        camera_objects_config = config.get('objects', {})

        self.take_frame = self.config.get('take_frame', 1)
        self.regions = self.config['regions']
        self.frame_shape = get_frame_shape(self.ffmpeg_input)
        self.frame_size = self.frame_shape[0] * self.frame_shape[1] * self.frame_shape[2]
        self.mqtt_client = mqtt_client
        self.mqtt_topic_prefix = '{}/{}'.format(mqtt_prefix, self.name)

        # create a numpy array for the current frame in initialize to zeros
        self.current_frame = np.zeros(self.frame_shape, np.uint8)
        # create shared value for storing the frame_time
        self.frame_time = mp.Value('d', 0.0)
        # Lock to control access to the frame
        self.frame_lock = mp.Lock()
        # Condition for notifying that a new frame is ready
        self.frame_ready = mp.Condition()
        # Condition for notifying that objects were parsed
        self.objects_parsed = mp.Condition()

        # Queue for prepped frames, max size set to (number of regions * 5)
        max_queue_size = len(self.config['regions'])*5
        self.resize_queue = queue.Queue(max_queue_size)
        
        # initialize the frame cache
        self.cached_frame_with_objects = {
            'frame_bytes': [],
            'frame_time': 0
        }

        self.ffmpeg_process = None
        self.capture_thread = None
        self.fps = EventsPerSecond()

        # for each region, merge the object config
        self.detection_prep_threads = []
        for region in self.config['regions']:
            region_objects = region.get('objects', {})
            # build objects config for region
            objects_with_config = set().union(global_objects_config.keys(), camera_objects_config.keys(), region_objects.keys())
            merged_objects_config = defaultdict(lambda: {})
            for obj in objects_with_config:
                merged_objects_config[obj] = {**global_objects_config.get(obj,{}), **camera_objects_config.get(obj, {}), **region_objects.get(obj, {})}
            
            region['objects'] = merged_objects_config

        # start a thread to queue resize requests for regions
        self.region_requester = RegionRequester(self)
        self.region_requester.start()

        # start a thread to cache recent frames for processing
        self.frame_tracker = FrameTracker(self.current_frame, self.frame_time, 
            self.frame_ready, self.frame_lock, self.frame_cache)
        self.frame_tracker.start()

        # start a thread to resize regions
        self.region_prepper = RegionPrepper(self.frame_cache, self.resize_queue, prepped_frame_queue)
        self.region_prepper.start()

        # start a thread to store the highest scoring recent frames for monitored object types
        self.best_frames = BestFrames(self.objects_parsed, self.frame_cache, self.detected_objects)
        self.best_frames.start()

        # start a thread to expire objects from the detected objects list
        self.object_cleaner = ObjectCleaner(self.objects_parsed, self.detected_objects)
        self.object_cleaner.start()

        # start a thread to publish object scores
        mqtt_publisher = MqttObjectPublisher(self.mqtt_client, self.mqtt_topic_prefix, self.objects_parsed, self.detected_objects, self.best_frames)
        mqtt_publisher.start()

        # create a watchdog thread for capture process
        self.watchdog = CameraWatchdog(self)

        # load in the mask for object detection
        if 'mask' in self.config:
            self.mask = cv2.imread("/config/{}".format(self.config['mask']), cv2.IMREAD_GRAYSCALE)
        else:
            self.mask = None

        if self.mask is None:
            self.mask = np.zeros((self.frame_shape[0], self.frame_shape[1], 1), np.uint8)
            self.mask[:] = 255


    def start_or_restart_capture(self):
        if not self.ffmpeg_process is None:
            print("Terminating the existing ffmpeg process...")
            self.ffmpeg_process.terminate()
            try:
                print("Waiting for ffmpeg to exit gracefully...")
                self.ffmpeg_process.wait(timeout=30)
            except sp.TimeoutExpired:
                print("FFmpeg didnt exit. Force killing...")
                self.ffmpeg_process.kill()
                self.ffmpeg_process.wait()

            print("Waiting for the capture thread to exit...")
            self.capture_thread.join()
            self.ffmpeg_process = None
            self.capture_thread = None
            
        # create the process to capture frames from the input stream and store in a shared array
        print("Creating a new ffmpeg process...")
        self.start_ffmpeg()
        
        print("Creating a new capture thread...")
        self.capture_thread = CameraCapture(self)
        print("Starting a new capture thread...")
        self.capture_thread.start()
        self.fps.start()
    
    def start_ffmpeg(self):
        ffmpeg_cmd = (['ffmpeg'] +
            self.ffmpeg_global_args +
            self.ffmpeg_hwaccel_args +
            self.ffmpeg_input_args +
            ['-i', self.ffmpeg_input] +
            self.ffmpeg_output_args +
            ['pipe:'])

        print(" ".join(ffmpeg_cmd))
        
        self.ffmpeg_process = sp.Popen(ffmpeg_cmd, stdout = sp.PIPE, bufsize=self.frame_size)
    
    def start(self):
        self.start_or_restart_capture()
        # start the object detection prep threads
        for detection_prep_thread in self.detection_prep_threads:
            detection_prep_thread.start()
        self.watchdog.start()
    
    def join(self):
        self.capture_thread.join()
    
    def get_capture_pid(self):
        return self.ffmpeg_process.pid
    
    def add_objects(self, frame):
        objects = frame['detected_objects']

        if len(objects) == 0:
            return

        for raw_obj in objects:
            obj = {
                'score': float(raw_obj.score),
                'box': raw_obj.bounding_box.flatten().tolist(),
                'name': str(LABELS[raw_obj.label_id]),
                'frame_time': frame['frame_time'],
                'region_id': frame['region_id']
            }

            # find the matching region
            region = self.regions[frame['region_id']]

            # Compute some extra properties
            obj.update({
                'xmin': int((obj['box'][0] * frame['size']) + frame['x_offset']),
                'ymin': int((obj['box'][1] * frame['size']) + frame['y_offset']),
                'xmax': int((obj['box'][2] * frame['size']) + frame['x_offset']),
                'ymax': int((obj['box'][3] * frame['size']) + frame['y_offset'])
            })
            
            # Compute the area
            obj['area'] = (obj['xmax']-obj['xmin'])*(obj['ymax']-obj['ymin'])

            object_name = obj['name']

            if object_name in region['objects']:
                obj_settings = region['objects'][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['area']:
                    continue
                
                # if the detected object is larger than the
                # max area, don't add it to detected objects
                if obj_settings.get('max_area', region['size']**2) < obj['area']:
                    continue

                # if the score is lower than the threshold, skip
                if obj_settings.get('threshold', 0) > obj['score']:
                    continue
            
                # 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['ymax']), len(self.mask)-1)
                x_location = min(int((obj['xmax']-obj['xmin'])/2.0)+obj['xmin'], len(self.mask[0])-1)

                # if the object is in a masked location, don't add it to detected objects
                if self.mask[y_location][x_location] == [0]:
                    continue
            
            # look to see if the bounding box is too close to the region border and the region border is not the edge of the frame
            # if ((frame['x_offset'] > 0 and obj['box'][0] < 0.01) or 
            #     (frame['y_offset'] > 0 and obj['box'][1] < 0.01) or
            #     (frame['x_offset']+frame['size'] < self.frame_shape[1] and obj['box'][2] > 0.99) or
            #     (frame['y_offset']+frame['size'] < self.frame_shape[0] and obj['box'][3] > 0.99)):

            #     size, x_offset, y_offset = calculate_region(self.frame_shape, obj['xmin'], obj['ymin'], obj['xmax'], obj['ymax'])
                # This triggers WAY too often with stationary objects on the edge of a region. 
                # Every frame triggers it and fills the queue...
                # I need to create a new region and add it to the list of regions, but 
                # it needs to check for a duplicate region first.

                # self.resize_queue.put({
                #     'camera_name': self.name,
                #     'frame_time': frame['frame_time'],
                #     'region_id': frame['region_id'],
                #     'size': size,
                #     'x_offset': x_offset,
                #     'y_offset': y_offset
                # })
                # print('object too close to region border')
                #continue

            self.detected_objects.append(obj)

        with self.objects_parsed:
            self.objects_parsed.notify_all()
    
    def get_best(self, label):
        return self.best_frames.best_frames.get(label)

    def stats(self):
        return {
            'camera_fps': self.fps.eps(60),
            'resize_queue': self.resize_queue.qsize()
        }
    
    def get_current_frame_with_objects(self):
        # make a copy of the current detected objects
        detected_objects = self.detected_objects.copy()
        # lock and make a copy of the current frame
        with self.frame_lock:
            frame = self.current_frame.copy()
            frame_time = self.frame_time.value
        
        if frame_time == self.cached_frame_with_objects['frame_time']:
            return self.cached_frame_with_objects['frame_bytes']

        # draw the bounding boxes on the screen
        for obj in detected_objects:
            draw_box_with_label(frame, obj['xmin'], obj['ymin'], obj['xmax'], obj['ymax'], obj['name'], obj['score'], obj['area'])

        for region in self.regions:
            color = (255,255,255)
            cv2.rectangle(frame, (region['x_offset'], region['y_offset']), 
                (region['x_offset']+region['size'], region['y_offset']+region['size']), 
                color, 2)
        
        # print a timestamp
        time_to_show = datetime.datetime.fromtimestamp(frame_time).strftime("%m/%d/%Y %H:%M:%S")
        cv2.putText(frame, time_to_show, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, fontScale=.8, color=(255, 255, 255), thickness=2)
        
        # print fps
        cv2.putText(frame, str(self.fps.eps())+'FPS', (10, 60), cv2.FONT_HERSHEY_SIMPLEX, fontScale=.8, color=(255, 255, 255), thickness=2)

        # convert to BGR
        frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)

        # encode the image into a jpg
        ret, jpg = cv2.imencode('.jpg', frame)

        frame_bytes = jpg.tobytes()

        self.cached_frame_with_objects = {
            'frame_bytes': frame_bytes,
            'frame_time': frame_time
        }

        return frame_bytes