frigate/frigate/objects.py

import time
import datetime
import threading
import cv2
import prctl
import itertools
import copy
import numpy as np
import multiprocessing as mp
from collections import defaultdict
from scipy.spatial import distance as dist
from frigate.util import draw_box_with_label, LABELS, compute_intersection_rectangle, compute_intersection_over_union, calculate_region

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

    def run(self):
        prctl.set_name("ObjectCleaner")
        while True:

            # wait a bit before checking for expired frames
            time.sleep(0.2)

            for frame_time in list(self.camera.detected_objects.keys()).copy():
                if not frame_time in self.camera.frame_cache:
                    del self.camera.detected_objects[frame_time]
            
            with self.camera.object_tracker.tracked_objects_lock:
                now = datetime.datetime.now().timestamp()
                for id, obj in list(self.camera.object_tracker.tracked_objects.items()):
                    # if the object is more than 10 seconds old
                    # and not in the most recent frame, deregister
                    if (now - obj['frame_time']) > 10 and self.camera.object_tracker.most_recent_frame_time > obj['frame_time']:
                        self.camera.object_tracker.deregister(id)

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

    def run(self):
        prctl.set_name(self.__class__.__name__)
        while True:
            frame = self.camera.detected_objects_queue.get()

            objects = frame['detected_objects']

            for raw_obj in objects:
                name = str(LABELS[raw_obj.label_id])

                if not name in self.camera.objects_to_track:
                    continue

                obj = {
                    'name': name,
                    'score': float(raw_obj.score),
                    'box': {
                        'xmin': int((raw_obj.bounding_box[0][0] * frame['size']) + frame['x_offset']),
                        'ymin': int((raw_obj.bounding_box[0][1] * frame['size']) + frame['y_offset']),
                        'xmax': int((raw_obj.bounding_box[1][0] * frame['size']) + frame['x_offset']),
                        'ymax': int((raw_obj.bounding_box[1][1] * frame['size']) + frame['y_offset'])
                    },
                    'region': {
                        'xmin': frame['x_offset'],
                        'ymin': frame['y_offset'],
                        'xmax': frame['x_offset']+frame['size'],
                        'ymax': frame['y_offset']+frame['size']
                    },
                    'frame_time': frame['frame_time'],
                    'region_id': frame['region_id']
                }
                
                # if the object is within 5 pixels of the region border, and the region is not on the edge
                # consider the object to be clipped
                obj['clipped'] = False
                if ((obj['region']['xmin'] > 5 and obj['box']['xmin']-obj['region']['xmin'] <= 5) or 
                    (obj['region']['ymin'] > 5 and obj['box']['ymin']-obj['region']['ymin'] <= 5) or
                    (self.camera.frame_shape[1]-obj['region']['xmax'] > 5 and obj['region']['xmax']-obj['box']['xmax'] <= 5) or
                    (self.camera.frame_shape[0]-obj['region']['ymax'] > 5 and obj['region']['ymax']-obj['box']['ymax'] <= 5)):
                    obj['clipped'] = True
                
                # Compute the area
                obj['area'] = (obj['box']['xmax']-obj['box']['xmin'])*(obj['box']['ymax']-obj['box']['ymin'])

                self.camera.detected_objects[frame['frame_time']].append(obj)
            
            with self.camera.regions_in_process_lock:
                self.camera.regions_in_process[frame['frame_time']] -= 1
                # print(f"{frame['frame_time']} remaining regions {self.camera.regions_in_process[frame['frame_time']]}")

                if self.camera.regions_in_process[frame['frame_time']] == 0:
                    del self.camera.regions_in_process[frame['frame_time']]
                    # print(f"{frame['frame_time']} no remaining regions")
                    self.camera.finished_frame_queue.put(frame['frame_time'])

# Thread that checks finished frames for clipped objects and sends back
# for processing if needed
class RegionRefiner(threading.Thread):
    def __init__(self, camera):
        threading.Thread.__init__(self)
        self.camera = camera

    def run(self):
        prctl.set_name(self.__class__.__name__)
        while True:
            frame_time = self.camera.finished_frame_queue.get()

            detected_objects = self.camera.detected_objects[frame_time].copy()
            # print(f"{frame_time} finished")

            # group by name
            detected_object_groups = defaultdict(lambda: [])
            for obj in detected_objects:
                detected_object_groups[obj['name']].append(obj)

            look_again = False
            selected_objects = []
            for group in detected_object_groups.values():

                # apply non-maxima suppression to suppress weak, overlapping bounding boxes
                boxes = [(o['box']['xmin'], o['box']['ymin'], o['box']['xmax']-o['box']['xmin'], o['box']['ymax']-o['box']['ymin'])
                    for o in group]
                confidences = [o['score'] for o in group]
                idxs = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)

                for index in idxs:
                    obj = group[index[0]]
                    selected_objects.append(obj)
                    if obj['clipped']:
                        box = obj['box']
                        # calculate a new region that will hopefully get the entire object
                        (size, x_offset, y_offset) = calculate_region(self.camera.frame_shape, 
                            box['xmin'], box['ymin'],
                            box['xmax'], box['ymax'])
                        # print(f"{frame_time} new region: {size} {x_offset} {y_offset}")

                        with self.camera.regions_in_process_lock:
                            if not frame_time in self.camera.regions_in_process:
                                self.camera.regions_in_process[frame_time] = 1
                            else:
                                self.camera.regions_in_process[frame_time] += 1

                        # add it to the queue
                        self.camera.resize_queue.put({
                            'camera_name': self.camera.name,
                            'frame_time': frame_time,
                            'region_id': -1,
                            'size': size,
                            'x_offset': x_offset,
                            'y_offset': y_offset
                        })
                        self.camera.dynamic_region_fps.update()
                        look_again = True

            # if we are looking again, then this frame is not ready for processing
            if look_again:
                # remove the clipped objects
                self.camera.detected_objects[frame_time] = [o for o in selected_objects if not o['clipped']]
                continue

            # filter objects based on camera settings
            selected_objects = [o for o in selected_objects if not self.filtered(o)]

            self.camera.detected_objects[frame_time] = selected_objects
            
            # print(f"{frame_time} is actually finished")

            # keep adding frames to the refined queue as long as they are finished
            with self.camera.regions_in_process_lock:
                while self.camera.frame_queue.qsize() > 0 and self.camera.frame_queue.queue[0] not in self.camera.regions_in_process:
                    self.camera.last_processed_frame = self.camera.frame_queue.get()
                    self.camera.refined_frame_queue.put(self.camera.last_processed_frame)

    def filtered(self, obj):
        object_name = obj['name']
        
        if object_name in self.camera.object_filters:
            obj_settings = self.camera.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['area']:
                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', self.camera.frame_shape[0]*self.camera.frame_shape[1]) < obj['area']:
                return True

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

            # if the object is in a masked location, don't add it to detected objects
            if self.camera.mask[y_location][x_location] == [0]:
                return True
            
            return False
             
    def has_overlap(self, new_obj, obj, overlap=.7):
        # compute intersection rectangle with existing object and new objects region
        existing_obj_current_region = compute_intersection_rectangle(obj['box'], new_obj['region'])

        # compute intersection rectangle with new object and existing objects region
        new_obj_existing_region = compute_intersection_rectangle(new_obj['box'], obj['region'])

        # compute iou for the two intersection rectangles that were just computed
        iou = compute_intersection_over_union(existing_obj_current_region, new_obj_existing_region)

        # if intersection is greater than overlap
        if iou > overlap:
            return True
        else:
            return False
    
    def find_group(self, new_obj, groups):
        for index, group in enumerate(groups):
            for obj in group:
                if self.has_overlap(new_obj, obj):
                    return index
        return None

class ObjectTracker(threading.Thread):
    def __init__(self, camera, max_disappeared):
        threading.Thread.__init__(self)
        self.camera = camera
        self.tracked_objects = {}
        self.tracked_objects_lock = mp.Lock()
        self.most_recent_frame_time = None
    
    def run(self):
        prctl.set_name(self.__class__.__name__)
        while True:
            frame_time = self.camera.refined_frame_queue.get()
            with self.tracked_objects_lock:
                self.match_and_update(self.camera.detected_objects[frame_time])
                self.most_recent_frame_time = frame_time
                self.camera.frame_output_queue.put((frame_time, copy.deepcopy(self.tracked_objects)))
            if len(self.tracked_objects) > 0:
                with self.camera.objects_tracked:
                    self.camera.objects_tracked.notify_all()

    def register(self, index, obj):
        id = f"{str(obj['frame_time'])}-{index}"
        obj['id'] = id
        obj['top_score'] = obj['score']
        self.add_history(obj)
        self.tracked_objects[id] = obj

    def deregister(self, id):
        del self.tracked_objects[id]
    
    def update(self, id, new_obj):
        self.tracked_objects[id].update(new_obj)
        self.add_history(self.tracked_objects[id])
        if self.tracked_objects[id]['score'] > self.tracked_objects[id]['top_score']:
            self.tracked_objects[id]['top_score'] = self.tracked_objects[id]['score']
    
    def add_history(self, obj):
        entry = {
            'score': obj['score'],
            'box': obj['box'],
            'region': obj['region'],
            'centroid': obj['centroid'],
            'frame_time': obj['frame_time']
        }
        if 'history' in obj:
            obj['history'].append(entry)
        else:
            obj['history'] = [entry]

    def match_and_update(self, new_objects):
        if len(new_objects) == 0:
            return
            
        # group by name
        new_object_groups = defaultdict(lambda: [])
        for obj in new_objects:
            new_object_groups[obj['name']].append(obj)
        
        # track objects for each label type
        for label, group in new_object_groups.items():
            current_objects = [o for o in self.tracked_objects.values() if o['name'] == label]
            current_ids = [o['id'] for o in current_objects]
            current_centroids = np.array([o['centroid'] for o in current_objects])

            # compute centroids of new objects
            for obj in group:
                centroid_x = int((obj['box']['xmin']+obj['box']['xmax']) / 2.0)
                centroid_y = int((obj['box']['ymin']+obj['box']['ymax']) / 2.0)
                obj['centroid'] = (centroid_x, centroid_y)

            if len(current_objects) == 0:
                for index, obj in enumerate(group):
                    self.register(index, obj)
                return
            
            new_centroids = np.array([o['centroid'] for o in group])

            # compute the distance between each pair of tracked
            # centroids and new centroids, respectively -- our
            # goal will be to match each new centroid to an existing
            # object centroid
            D = dist.cdist(current_centroids, new_centroids)

            # in order to perform this matching we must (1) find the
            # smallest value in each row and then (2) sort the row
            # indexes based on their minimum values so that the row
            # with the smallest value is at the *front* of the index
            # list
            rows = D.min(axis=1).argsort()

            # next, we perform a similar process on the columns by
            # finding the smallest value in each column and then
            # sorting using the previously computed row index list
            cols = D.argmin(axis=1)[rows]

            # in order to determine if we need to update, register,
            # or deregister an object we need to keep track of which
            # of the rows and column indexes we have already examined
            usedRows = set()
            usedCols = set()

            # loop over the combination of the (row, column) index
            # tuples
            for (row, col) in zip(rows, cols):
                # if we have already examined either the row or
                # column value before, ignore it
                if row in usedRows or col in usedCols:
                    continue

                # otherwise, grab the object ID for the current row,
                # set its new centroid, and reset the disappeared
                # counter
                objectID = current_ids[row]
                self.update(objectID, group[col])

                # indicate that we have examined each of the row and
                # column indexes, respectively
                usedRows.add(row)
                usedCols.add(col)

            # compute the column index we have NOT yet examined
            unusedCols = set(range(0, D.shape[1])).difference(usedCols)

            # if the number of input centroids is greater
            # than the number of existing object centroids we need to
            # register each new input centroid as a trackable object
            # if D.shape[0] < D.shape[1]:
            for col in unusedCols:
                self.register(col, group[col])

# Maintains the frame and object with the highest score
class BestFrames(threading.Thread):
    def __init__(self, camera):
        threading.Thread.__init__(self)
        self.camera = camera
        self.best_objects = {}
        self.best_frames = {}

    def run(self):
        prctl.set_name(self.__class__.__name__)
        while True:
            # wait until objects have been tracked
            with self.camera.objects_tracked:
                self.camera.objects_tracked.wait()

            # make a copy of tracked objects
            tracked_objects = list(self.camera.object_tracker.tracked_objects.values())

            for obj in tracked_objects:
                if obj['name'] in self.best_objects:
                    now = datetime.datetime.now().timestamp()
                    # if the object is a higher score than the current best score 
                    # or the current object is more than 1 minute old, use the new object
                    if obj['score'] > self.best_objects[obj['name']]['score'] or (now - self.best_objects[obj['name']]['frame_time']) > 60:
                        self.best_objects[obj['name']] = copy.deepcopy(obj)
                else:
                    self.best_objects[obj['name']] = copy.deepcopy(obj)
            
            for name, obj in self.best_objects.items():
                if obj['frame_time'] in self.camera.frame_cache:
                    best_frame = self.camera.frame_cache[obj['frame_time']]

                    draw_box_with_label(best_frame, obj['box']['xmin'], obj['box']['ymin'], 
                        obj['box']['xmax'], obj['box']['ymax'], obj['name'], f"{int(obj['score']*100)}% {obj['area']}")
                    
                    # print a timestamp
                    time_to_show = datetime.datetime.fromtimestamp(obj['frame_time']).strftime("%m/%d/%Y %H:%M:%S")
                    cv2.putText(best_frame, time_to_show, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, fontScale=.8, color=(255, 255, 255), thickness=2)
                    
                    self.best_frames[name] = best_frame