From 956298128d6880bac10d44d19e30ca3009109d18 Mon Sep 17 00:00:00 2001 From: Blake Blackshear Date: Sat, 22 Feb 2020 09:15:29 -0600 Subject: [PATCH] cleanup --- start_no_thread.py | 508 --------------------------------------------- 1 file changed, 508 deletions(-) delete mode 100755 start_no_thread.py diff --git a/start_no_thread.py b/start_no_thread.py deleted file mode 100755 index 7f20ade93..000000000 --- a/start_no_thread.py +++ /dev/null @@ -1,508 +0,0 @@ -import datetime -import time -import threading -import queue -import itertools -from collections import defaultdict -from statistics import mean -import cv2 -import imutils -import numpy as np -import subprocess as sp -import multiprocessing as mp -import SharedArray as sa -from scipy.spatial import distance as dist -import tflite_runtime.interpreter as tflite -from tflite_runtime.interpreter import load_delegate -from frigate.edgetpu import ObjectDetector, EdgeTPUProcess, RemoteObjectDetector, load_labels -from frigate.motion import MotionDetector - -def draw_box_with_label(frame, x_min, y_min, x_max, y_max, label, info, thickness=2, color=None, position='ul'): - if color is None: - color = (0,0,255) - display_text = "{}: {}".format(label, info) - cv2.rectangle(frame, (x_min, y_min), (x_max, y_max), color, thickness) - font_scale = 0.5 - font = cv2.FONT_HERSHEY_SIMPLEX - # get the width and height of the text box - size = cv2.getTextSize(display_text, font, fontScale=font_scale, thickness=2) - text_width = size[0][0] - text_height = size[0][1] - line_height = text_height + size[1] - # set the text start position - if position == 'ul': - text_offset_x = x_min - text_offset_y = 0 if y_min < line_height else y_min - (line_height+8) - elif position == 'ur': - text_offset_x = x_max - (text_width+8) - text_offset_y = 0 if y_min < line_height else y_min - (line_height+8) - elif position == 'bl': - text_offset_x = x_min - text_offset_y = y_max - elif position == 'br': - text_offset_x = x_max - (text_width+8) - text_offset_y = y_max - # make the coords of the box with a small padding of two pixels - textbox_coords = ((text_offset_x, text_offset_y), (text_offset_x + text_width + 2, text_offset_y + line_height)) - cv2.rectangle(frame, textbox_coords[0], textbox_coords[1], color, cv2.FILLED) - cv2.putText(frame, display_text, (text_offset_x, text_offset_y + line_height - 3), font, fontScale=font_scale, color=(0, 0, 0), thickness=2) - -def calculate_region(frame_shape, xmin, ymin, xmax, ymax, multiplier=2): - # size is larger than longest edge - size = int(max(xmax-xmin, ymax-ymin)*multiplier) - # if the size is too big to fit in the frame - if size > min(frame_shape[0], frame_shape[1]): - size = min(frame_shape[0], frame_shape[1]) - - # x_offset is midpoint of bounding box minus half the size - x_offset = int((xmax-xmin)/2.0+xmin-size/2.0) - # if outside the image - if x_offset < 0: - x_offset = 0 - elif x_offset > (frame_shape[1]-size): - x_offset = (frame_shape[1]-size) - - # y_offset is midpoint of bounding box minus half the size - y_offset = int((ymax-ymin)/2.0+ymin-size/2.0) - # if outside the image - if y_offset < 0: - y_offset = 0 - elif y_offset > (frame_shape[0]-size): - y_offset = (frame_shape[0]-size) - - return (x_offset, y_offset, x_offset+size, y_offset+size) - -def intersection(box_a, box_b): - return ( - max(box_a[0], box_b[0]), - max(box_a[1], box_b[1]), - min(box_a[2], box_b[2]), - min(box_a[3], box_b[3]) - ) - -def area(box): - return (box[2]-box[0] + 1)*(box[3]-box[1] + 1) - -def intersection_over_union(box_a, box_b): - # determine the (x, y)-coordinates of the intersection rectangle - intersect = intersection(box_a, box_b) - - # compute the area of intersection rectangle - inter_area = max(0, intersect[2] - intersect[0] + 1) * max(0, intersect[3] - intersect[1] + 1) - - if inter_area == 0: - return 0.0 - - # compute the area of both the prediction and ground-truth - # rectangles - box_a_area = (box_a[2] - box_a[0] + 1) * (box_a[3] - box_a[1] + 1) - box_b_area = (box_b[2] - box_b[0] + 1) * (box_b[3] - box_b[1] + 1) - - # compute the intersection over union by taking the intersection - # area and dividing it by the sum of prediction + ground-truth - # areas - the interesection area - iou = inter_area / float(box_a_area + box_b_area - inter_area) - - # return the intersection over union value - return iou - -def clipped(obj, frame_shape): - # 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 - box = obj[2] - region = obj[3] - if ((region[0] > 5 and box[0]-region[0] <= 5) or - (region[1] > 5 and box[1]-region[1] <= 5) or - (frame_shape[1]-region[2] > 5 and region[2]-box[2] <= 5) or - (frame_shape[0]-region[3] > 5 and region[3]-box[3] <= 5)): - return True - else: - return False - -def filtered(obj): - if obj[0] != 'person': - return True - return False - -def create_tensor_input(frame, region): - cropped_frame = frame[region[1]:region[3], region[0]:region[2]] - - # Resize to 300x300 if needed - if cropped_frame.shape != (300, 300, 3): - # TODO: use Pillow-SIMD? - cropped_frame = cv2.resize(cropped_frame, dsize=(300, 300), interpolation=cv2.INTER_LINEAR) - - # Expand dimensions since the model expects images to have shape: [1, 300, 300, 3] - return np.expand_dims(cropped_frame, axis=0) - -class ObjectTracker(): - def __init__(self, max_disappeared): - self.tracked_objects = {} - self.disappeared = {} - self.max_disappeared = max_disappeared - - def register(self, index, frame_time, obj): - id = f"{frame_time}-{index}" - obj['id'] = id - obj['frame_time'] = frame_time - obj['top_score'] = obj['score'] - self.add_history(obj) - self.tracked_objects[id] = obj - self.disappeared[id] = 0 - - def deregister(self, id): - del self.tracked_objects[id] - del self.disappeared[id] - - def update(self, id, new_obj): - self.disappeared[id] = 0 - 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, frame_time, new_objects): - if len(new_objects) == 0: - for id in list(self.tracked_objects.keys()): - if self.disappeared[id] >= self.max_disappeared: - self.deregister(id) - else: - self.disappeared[id] += 1 - return - - # group by name - new_object_groups = defaultdict(lambda: []) - for obj in new_objects: - new_object_groups[obj[0]].append({ - 'label': obj[0], - 'score': obj[1], - 'box': obj[2], - 'region': obj[3] - }) - - # 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['label'] == 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'][0]+obj['box'][2]) / 2.0) - centroid_y = int((obj['box'][1]+obj['box'][3]) / 2.0) - obj['centroid'] = (centroid_x, centroid_y) - - if len(current_objects) == 0: - for index, obj in enumerate(group): - self.register(index, frame_time, 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 - unusedRows = set(range(0, D.shape[0])).difference(usedRows) - unusedCols = set(range(0, D.shape[1])).difference(usedCols) - - # in the event that the number of object centroids is - # equal or greater than the number of input centroids - # we need to check and see if some of these objects have - # potentially disappeared - if D.shape[0] >= D.shape[1]: - for row in unusedRows: - id = current_ids[row] - - if self.disappeared[id] >= self.max_disappeared: - self.deregister(id) - else: - self.disappeared[id] += 1 - # 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 - else: - for col in unusedCols: - self.register(col, frame_time, group[col]) - -def main(): - frames = 0 - # frame_queue = queue.Queue(maxsize=5) - # frame_cache = {} - frame_shape = (1080,1920,3) - # frame_shape = (720,1280,3) - frame_size = frame_shape[0]*frame_shape[1]*frame_shape[2] - frame = np.zeros(frame_shape, np.uint8) - motion_detector = MotionDetector(frame_shape, resize_factor=6) - # object_detector = ObjectDetector('/lab/mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite', '/lab/labelmap.txt') - # object_detector = RemoteObjectDetector('/lab/mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite', '/lab/labelmap.txt') - # object_detector = ObjectDetector('/lab/detect.tflite', '/lab/labelmap.txt') - object_detector = RemoteObjectDetector('/lab/detect.tflite', '/lab/labelmap.txt') - object_tracker = ObjectTracker(10) - - # f = open('/debug/input/back.rgb24', 'rb') - # f = open('/debug/back.raw_video', 'rb') - # f = open('/debug/ali-jake.raw_video', 'rb') - - # -hwaccel vaapi -hwaccel_device /dev/dri/renderD128 -hwaccel_output_format yuv420p -i output.mp4 -f rawvideo -pix_fmt rgb24 pipe: - ffmpeg_cmd = (['ffmpeg'] + - ['-hide_banner','-loglevel','panic'] + - # ['-hwaccel','vaapi','-hwaccel_device','/dev/dri/renderD129','-hwaccel_output_format','yuv420p'] + - # ['-i', '/debug/input/output.mp4'] + - ['-i', '/lab/debug/back-night.mp4'] + - ['-f','rawvideo','-pix_fmt','rgb24'] + - ['pipe:']) - - print(" ".join(ffmpeg_cmd)) - - ffmpeg_process = sp.Popen(ffmpeg_cmd, stdout = sp.PIPE, bufsize=frame_size) - - total_detections = 0 - start = datetime.datetime.now().timestamp() - frame_times = [] - while True: - - start_frame = datetime.datetime.now().timestamp() - frame_detections = 0 - frame_bytes = ffmpeg_process.stdout.read(frame_size) - if not frame_bytes: - break - frame_time = datetime.datetime.now().timestamp() - - # Store frame in numpy array - frame[:] = (np - .frombuffer(frame_bytes, np.uint8) - .reshape(frame_shape)) - frames += 1 - - # look for motion - motion_boxes = motion_detector.detect(frame) - - tracked_objects = object_tracker.tracked_objects.values() - - # merge areas of motion that intersect with a known tracked object into a single area to look at - areas_of_interest = [] - used_motion_boxes = [] - for obj in tracked_objects: - x_min, y_min, x_max, y_max = obj['box'] - for m_index, motion_box in enumerate(motion_boxes): - if area(intersection(obj['box'], motion_box))/area(motion_box) > .5: - used_motion_boxes.append(m_index) - x_min = min(obj['box'][0], motion_box[0]) - y_min = min(obj['box'][1], motion_box[1]) - x_max = max(obj['box'][2], motion_box[2]) - y_max = max(obj['box'][3], motion_box[3]) - areas_of_interest.append((x_min, y_min, x_max, y_max)) - unused_motion_boxes = set(range(0, len(motion_boxes))).difference(used_motion_boxes) - - # compute motion regions - motion_regions = [calculate_region(frame_shape, motion_boxes[i][0], motion_boxes[i][1], motion_boxes[i][2], motion_boxes[i][3], 1.2) - for i in unused_motion_boxes] - - # compute tracked object regions - object_regions = [calculate_region(frame_shape, a[0], a[1], a[2], a[3], 1.2) - for a in areas_of_interest] - - # merge regions with high IOU - merged_regions = motion_regions+object_regions - while True: - max_iou = 0.0 - max_indices = None - region_indices = range(len(merged_regions)) - for a, b in itertools.combinations(region_indices, 2): - iou = intersection_over_union(merged_regions[a], merged_regions[b]) - if iou > max_iou: - max_iou = iou - max_indices = (a, b) - if max_iou > 0.1: - a = merged_regions[max_indices[0]] - b = merged_regions[max_indices[1]] - merged_regions.append(calculate_region(frame_shape, - min(a[0], b[0]), - min(a[1], b[1]), - max(a[2], b[2]), - max(a[3], b[3]), - 1 - )) - del merged_regions[max(max_indices[0], max_indices[1])] - del merged_regions[min(max_indices[0], max_indices[1])] - else: - break - - # resize regions and detect - detections = [] - for region in merged_regions: - - tensor_input = create_tensor_input(frame, region) - - region_detections = object_detector.detect(tensor_input) - frame_detections += 1 - - for d in region_detections: - if filtered(d): - continue - box = d[2] - size = region[2]-region[0] - x_min = int((box[1] * size) + region[0]) - y_min = int((box[0] * size) + region[1]) - x_max = int((box[3] * size) + region[0]) - y_max = int((box[2] * size) + region[1]) - detections.append(( - d[0], - d[1], - (x_min, y_min, x_max, y_max), - region)) - - ######### - # merge objects, check for clipped objects and look again up to N times - ######### - refining = True - refine_count = 0 - while refining and refine_count < 4: - refining = False - - # group by name - detected_object_groups = defaultdict(lambda: []) - for detection in detections: - detected_object_groups[detection[0]].append(detection) - - selected_objects = [] - for group in detected_object_groups.values(): - - # apply non-maxima suppression to suppress weak, overlapping bounding boxes - boxes = [(o[2][0], o[2][1], o[2][2]-o[2][0], o[2][3]-o[2][1]) - for o in group] - confidences = [o[1] for o in group] - idxs = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4) - - for index in idxs: - obj = group[index[0]] - if clipped(obj, frame_shape): #obj['clipped']: - box = obj[2] - # calculate a new region that will hopefully get the entire object - region = calculate_region(frame_shape, - box[0], box[1], - box[2], box[3]) - - tensor_input = create_tensor_input(frame, region) - # run detection on new region - refined_detections = object_detector.detect(tensor_input) - frame_detections += 1 - for d in refined_detections: - if filtered(d): - continue - box = d[2] - size = region[2]-region[0] - x_min = int((box[1] * size) + region[0]) - y_min = int((box[0] * size) + region[1]) - x_max = int((box[3] * size) + region[0]) - y_max = int((box[2] * size) + region[1]) - selected_objects.append(( - d[0], - d[1], - (x_min, y_min, x_max, y_max), - region)) - - refining = True - else: - selected_objects.append(obj) - - # set the detections list to only include top, complete objects - # and new detections - detections = selected_objects - - if refining: - refine_count += 1 - - # now that we have refined our detections, we need to track objects - object_tracker.match_and_update(frame_time, detections) - - total_detections += frame_detections - frame_times.append(datetime.datetime.now().timestamp()-start_frame) - - # if (frames >= 700 and frames <= 1635) or (frames >= 2500): - # if (frames >= 300 and frames <= 600): - if (frames >= 0): - # row1 = cv2.hconcat([gray, cv2.convertScaleAbs(avg_frame)]) - # row2 = cv2.hconcat([frameDelta, thresh]) - # cv2.imwrite(f"/lab/debug/output/{frames}.jpg", cv2.vconcat([row1, row2])) - # # cv2.imwrite(f"/lab/debug/output/resized-frame-{frames}.jpg", resized_frame) - # for region in motion_regions: - # cv2.rectangle(frame, (region[0], region[1]), (region[2], region[3]), (255,128,0), 2) - # for region in object_regions: - # cv2.rectangle(frame, (region[0], region[1]), (region[2], region[3]), (0,128,255), 2) - for region in merged_regions: - cv2.rectangle(frame, (region[0], region[1]), (region[2], region[3]), (0,255,0), 2) - for box in motion_boxes: - cv2.rectangle(frame, (box[0], box[1]), (box[2], box[3]), (255,0,0), 2) - for detection in detections: - box = detection[2] - draw_box_with_label(frame, box[0], box[1], box[2], box[3], detection[0], f"{detection[1]*100}%") - for obj in object_tracker.tracked_objects.values(): - box = obj['box'] - draw_box_with_label(frame, box[0], box[1], box[2], box[3], obj['label'], obj['id'], thickness=1, color=(0,0,255), position='bl') - cv2.putText(frame, str(total_detections), (10, 10), cv2.FONT_HERSHEY_SIMPLEX, fontScale=0.5, color=(0, 0, 0), thickness=2) - cv2.putText(frame, str(frame_detections), (10, 30), cv2.FONT_HERSHEY_SIMPLEX, fontScale=0.5, color=(0, 0, 0), thickness=2) - cv2.imwrite(f"/lab/debug/output/frame-{frames}.jpg", frame) - # break - - duration = datetime.datetime.now().timestamp()-start - print(f"Processed {frames} frames for {duration:.2f} seconds and {(frames/duration):.2f} FPS.") - print(f"Total detections: {total_detections}") - print(f"Average frame processing time: {mean(frame_times)*1000:.2f}ms") - -if __name__ == '__main__': - main() \ No newline at end of file