def predict(self, img): rgb_origin = img img_numpy = img img = torch.from_numpy(img.copy()).float() img = img.cuda() img_h, img_w = img.shape[0], img.shape[1] img_trans = FastBaseTransform()(img.unsqueeze(0)) net_outs = self.net(img_trans) nms_outs = NMS(net_outs, 0) results = after_nms(nms_outs, img_h, img_w, crop_masks=not self.args.no_crop, visual_thre=self.args.visual_thre) torch.cuda.synchronize() temp = self.time_here self.time_here = time.time() self.frame_times.add(self.time_here - temp) fps = 1 / self.frame_times.get_avg() frame_numpy = draw_img(results, img, self.args, class_color=True, fps=fps) return frame_numpy
timer.start() img_h, img_w = img_origin.shape[0:2] with timer.counter('forward'): class_p, box_p, coef_p, proto_p, anchors = sess.run(None, {input_name: img}) with timer.counter('nms'): ids_p, class_p, box_p, coef_p, proto_p = nms_numpy(class_p, box_p, coef_p, proto_p, anchors, cfg) with timer.counter('after_nms'): ids_p, class_p, boxes_p, masks_p = after_nms_numpy(ids_p, class_p, box_p, coef_p, proto_p, img_h, img_w, cfg) with timer.counter('save_img'): img_numpy = draw_img(ids_p, class_p, boxes_p, masks_p, img_origin, cfg, img_name=img_name) cv2.imwrite(f'results/images/{img_name}', img_numpy) aa = time.perf_counter() if i > 0: batch_time = aa - temp timer.add_batch_time(batch_time) temp = aa if i > 0: t_t, t_d, t_f, t_nms, t_an, t_si = timer.get_times(['batch', 'data', 'forward', 'nms', 'after_nms', 'save_img']) fps, t_fps = 1 / (t_d + t_f + t_nms + t_an), 1 / t_t bar_str = progress_bar.get_bar(i + 1) print(f'\rTesting: {bar_str} {i + 1}/{ds}, fps: {fps:.2f} | total fps: {t_fps:.2f} | ' f't_t: {t_t:.3f} | t_d: {t_d:.3f} | t_f: {t_f:.3f} | t_nms: {t_nms:.3f} | '
net_outs = net(img_trans) nms_outs = NMS(net_outs, args.traditional_nms) show_lincomb = bool(args.show_lincomb and args.image_path) with timer.env('after nms'): results = after_nms(nms_outs, img_h, img_w, show_lincomb=show_lincomb, crop_masks=not args.no_crop, visual_thre=args.visual_thre, img_name=img_name) torch.cuda.synchronize() img_numpy = draw_img(results, img_origin, args) cv2.imwrite(f'results/images/{img_name}', img_numpy) print(f'\r{i + 1}/{num}', end='') print('\nDone.') # detect videos elif args.video is not None: vid = cv2.VideoCapture('videos/' + args.video) target_fps = round(vid.get(cv2.CAP_PROP_FPS)) frame_width = round(vid.get(cv2.CAP_PROP_FRAME_WIDTH)) frame_height = round(vid.get(cv2.CAP_PROP_FRAME_HEIGHT)) num_frames = round(vid.get(cv2.CAP_PROP_FRAME_COUNT))
def process(): try: destFile = "" if request.method == 'POST': file = request.files['file'] if file and allowed_file(file.filename): filename = secure_filename(file.filename) destFile = os.path.join(app.config['UPLOAD_FOLDER'], filename) file.save(destFile) app.logger.warning('filename=(%s)', filename) else: app.logger.warning("Request dictionary data: {}".format(request.data)) app.logger.warning("Request dictionary form: {}".format(request.form)) url = request.form["url"] print("url:", url) # download file destFile = download_file(url) # app.logger.error('An error occurred') app.logger.warning('destFile=(%s)', destFile) img_name = destFile.split('/')[-1] app.logger.warning('img_name=(%s)', img_name) # img_origin = cv2.imread(one_img) # img_tensor = torch.from_numpy(img_origin).float() img_origin = cv2.imread(destFile) # torch.from_numpy(img_origin).float() img_tensor = torch.from_numpy(img_origin).float() if cuda: # img_origin = img_origin.cuda() img_tensor = img_tensor.cuda() img_h, img_w = img_tensor.shape[0], img_tensor.shape[1] img_trans = FastBaseTransform()(img_tensor.unsqueeze(0)) net_outs = net(img_trans) nms_outs = NMS(net_outs, args.traditional_nms) show_lincomb = bool(args.show_lincomb) results = after_nms(nms_outs, img_h, img_w, show_lincomb=show_lincomb, crop_masks=not args.no_crop, visual_thre=args.visual_thre, img_name=img_name) # img_h, img_w = img_origin.shape[0], img_origin.shape[1] # img_trans = FastBaseTransform()(img_origin.unsqueeze(0)) # net_outs = net(img_trans) # nms_outs = NMS(net_outs, args.traditional_nms) app.logger.warning('img_h=(%s)', img_h) app.logger.warning('img_w=(%s)', img_w) app.logger.warning('cuda=(%s)', cuda) app.logger.warning('args.show_lincomb=(%s)', args.show_lincomb) app.logger.warning('args.no_crop=(%s)', args.no_crop) app.logger.warning('args.visual_thre=(%s)', args.visual_thre) app.logger.warning('args=(%s)', args) # show_lincomb = bool(args.show_lincomb) with timer.env('after nms'): results = after_nms(nms_outs, img_h, img_w, show_lincomb=show_lincomb, crop_masks=not args.no_crop, visual_thre=args.visual_thre, img_name=img_name) if cuda: torch.cuda.synchronize() # app.logger.warning('results=(%s)', results) # img_numpy = draw_img(results, img_origin, args) img_numpy = draw_img(results, img_origin, img_name, args) cv2.imwrite(f'results/images/{img_name}', img_numpy) # print(f'\r{i + 1}/{num}', end='') try: im = Image.open(f'results/images/{img_name}') # im = Image.open(destFile) io = BytesIO() im.save(io, format='JPEG') return Response(io.getvalue(), mimetype='image/jpeg') except IOError: abort(404) # return send_from_directory('.', filename), 200 callback = json.dumps({"results": results}) return callback, 200 except: traceback.print_exc() return {'message': 'input error'}, 400
with timer.counter('forward'): net_outs = net(img) with timer.counter('nms'): nms_outs = nms(cfg, net_outs) with timer.counter('after_nms'): results = after_nms(nms_outs, img_h, img_w, cfg, img_name=img_name) with timer.counter('save_img'): img_numpy = draw_img(results, img_origin, cfg, img_name=img_name) cv2.imwrite(f'results/images/{img_name}', img_numpy) aa = time.perf_counter() if i > 0: batch_time = aa - temp timer.add_batch_time(batch_time) temp = aa if i > 0: t_t, t_d, t_f, t_nms, t_an, t_si = timer.get_times([ 'batch', 'data', 'forward', 'nms', 'after_nms', 'save_img' ]) fps, t_fps = 1 / (t_d + t_f + t_nms + t_an), 1 / t_t bar_str = progress_bar.get_bar(i + 1)
img_name = one_img.split('/')[-1] img_origin = cv2.imread(one_img) img_tensor = torch.from_numpy(img_origin).float() if cuda: img_tensor = img_tensor.cuda() img_h, img_w = img_tensor.shape[0], img_tensor.shape[1] img_trans = FastBaseTransform()(img_tensor.unsqueeze(0)) tensor_outs=tf_rep.run(img_trans.cpu().numpy())._0 net_outs = net(img_trans) nms_outs = NMS(net_outs, args.traditional_nms) show_lincomb = bool(args.show_lincomb and args.image_path) results = after_nms(nms_outs, img_h, img_w, show_lincomb=show_lincomb, crop_masks=not args.no_crop, visual_thre=args.visual_thre, img_name=img_name) img_numpy = draw_img(results, img_origin, img_name, args) cv2.imwrite(f'results/images/{img_name}', img_numpy) print(f'\r{i + 1}/{len(images)}', end='') print('\nDone.') # detect videos elif args.video is not None: vid = cv2.VideoCapture('videos/' + args.video) target_fps = round(vid.get(cv2.CAP_PROP_FPS)) frame_width = round(vid.get(cv2.CAP_PROP_FRAME_WIDTH)) frame_height = round(vid.get(cv2.CAP_PROP_FRAME_HEIGHT)) num_frames = round(vid.get(cv2.CAP_PROP_FRAME_COUNT)) name = args.video.split('/')[-1]
def main(): parser = argparse.ArgumentParser(description='YOLACT Detection.') parser.add_argument('--weight', default='weights/best_30.5_res101_coco_392000.pth', type=str) parser.add_argument('--image', default=None, type=str, help='The folder of images for detecting.') parser.add_argument('--video', default=None, type=str, help='The path of the video to evaluate.') parser.add_argument('--img_size', type=int, default=544, help='The image size for validation.') parser.add_argument('--traditional_nms', default=False, action='store_true', help='Whether to use traditional nms.') parser.add_argument('--hide_mask', default=False, action='store_true', help='Hide masks in results.') parser.add_argument('--hide_bbox', default=False, action='store_true', help='Hide boxes in results.') parser.add_argument('--hide_score', default=False, action='store_true', help='Hide scores in results.') parser.add_argument('--cutout', default=False, action='store_true', help='Cut out each object and save.') parser.add_argument('--save_lincomb', default=False, action='store_true', help='Show the generating process of masks.') parser.add_argument('--no_crop', default=False, action='store_true', help='Do not crop the output masks with the predicted bounding box.') parser.add_argument('--real_time', default=False, action='store_true', help='Show the detection results real-timely.') parser.add_argument('--visual_thre', default=0.3, type=float, help='Detections with a score under this threshold will be removed.') args = parser.parse_args() prefix = re.findall(r'best_\d+\.\d+_', args.weight)[0] suffix = re.findall(r'_\d+\.pth', args.weight)[0] args.cfg = args.weight.split(prefix)[-1].split(suffix)[0] cfg = get_config(args, mode='detect') net = Yolact(cfg) net.load_weights(cfg.weight, cfg.cuda) net.eval() if cfg.cuda: cudnn.benchmark = True cudnn.fastest = True net = net.cuda() # detect images if cfg.image is not None: dataset = COCODetection(cfg, mode='detect') data_loader = data.DataLoader(dataset, 1, num_workers=2, shuffle=False, pin_memory=True, collate_fn=detect_collate) ds = len(data_loader) assert ds > 0, 'No .jpg images found.' progress_bar = ProgressBar(40, ds) timer.reset() for i, (img, img_origin, img_name) in enumerate(data_loader): if i == 1: timer.start() if cfg.cuda: img = img.cuda() img_h, img_w = img_origin.shape[0:2] with torch.no_grad(), timer.counter('forward'): class_p, box_p, coef_p, proto_p = net(img) with timer.counter('nms'): ids_p, class_p, box_p, coef_p, proto_p = nms(class_p, box_p, coef_p, proto_p, net.anchors, cfg) with timer.counter('after_nms'): ids_p, class_p, boxes_p, masks_p = after_nms(ids_p, class_p, box_p, coef_p, proto_p, img_h, img_w, cfg, img_name=img_name) with timer.counter('save_img'): img_numpy = draw_img(ids_p, class_p, boxes_p, masks_p, img_origin, cfg, img_name=img_name) cv2.imwrite(f'results/images/{img_name}', img_numpy) aa = time.perf_counter() if i > 0: batch_time = aa - temp timer.add_batch_time(batch_time) temp = aa if i > 0: t_t, t_d, t_f, t_nms, t_an, t_si = timer.get_times(['batch', 'data', 'forward', 'nms', 'after_nms', 'save_img']) fps, t_fps = 1 / (t_d + t_f + t_nms + t_an), 1 / t_t bar_str = progress_bar.get_bar(i + 1) print(f'\rTesting: {bar_str} {i + 1}/{ds}, fps: {fps:.2f} | total fps: {t_fps:.2f} | ' f't_t: {t_t:.3f} | t_d: {t_d:.3f} | t_f: {t_f:.3f} | t_nms: {t_nms:.3f} | ' f't_after_nms: {t_an:.3f} | t_save_img: {t_si:.3f}', end='') print('\nFinished, saved in: results/images.') # detect videos elif cfg.video is not None: vid = cv2.VideoCapture(cfg.video) target_fps = round(vid.get(cv2.CAP_PROP_FPS)) frame_width = round(vid.get(cv2.CAP_PROP_FRAME_WIDTH)) frame_height = round(vid.get(cv2.CAP_PROP_FRAME_HEIGHT)) num_frames = round(vid.get(cv2.CAP_PROP_FRAME_COUNT)) name = cfg.video.split('/')[-1] video_writer = cv2.VideoWriter(f'results/videos/{name}', cv2.VideoWriter_fourcc(*"mp4v"), target_fps, (frame_width, frame_height)) progress_bar = ProgressBar(40, num_frames) timer.reset() t_fps = 0 for i in range(num_frames): if i == 1: timer.start() frame_origin = vid.read()[1] img_h, img_w = frame_origin.shape[0:2] frame_trans = val_aug(frame_origin, cfg.img_size) frame_tensor = torch.tensor(frame_trans).float() if cfg.cuda: frame_tensor = frame_tensor.cuda() with torch.no_grad(), timer.counter('forward'): class_p, box_p, coef_p, proto_p = net(frame_tensor.unsqueeze(0)) with timer.counter('nms'): ids_p, class_p, box_p, coef_p, proto_p = nms(class_p, box_p, coef_p, proto_p, net.anchors, cfg) with timer.counter('after_nms'): ids_p, class_p, boxes_p, masks_p = after_nms(ids_p, class_p, box_p, coef_p, proto_p, img_h, img_w, cfg) with timer.counter('save_img'): frame_numpy = draw_img(ids_p, class_p, boxes_p, masks_p, frame_origin, cfg, fps=t_fps) if cfg.real_time: cv2.imshow('Detection', frame_numpy) cv2.waitKey(1) else: video_writer.write(frame_numpy) aa = time.perf_counter() if i > 0: batch_time = aa - temp timer.add_batch_time(batch_time) temp = aa if i > 0: t_t, t_d, t_f, t_nms, t_an, t_si = timer.get_times(['batch', 'data', 'forward', 'nms', 'after_nms', 'save_img']) fps, t_fps = 1 / (t_d + t_f + t_nms + t_an), 1 / t_t bar_str = progress_bar.get_bar(i + 1) print(f'\rDetecting: {bar_str} {i + 1}/{num_frames}, fps: {fps:.2f} | total fps: {t_fps:.2f} | ' f't_t: {t_t:.3f} | t_d: {t_d:.3f} | t_f: {t_f:.3f} | t_nms: {t_nms:.3f} | ' f't_after_nms: {t_an:.3f} | t_save_img: {t_si:.3f}', end='') if not cfg.real_time: print(f'\n\nFinished, saved in: results/videos/{name}') vid.release() video_writer.release()
img_name = img_name.split('.')[0] # only save the filename print("the {} image : {}".format(i, img_name)) print("img size:", img.shape) img_h, img_w = img_origin.shape[0:2] class_p, box_p, coef_p, proto_p, anchors = net(img) ids_p, class_p, box_p, coef_p, proto_p = nms(class_p, box_p, coef_p, proto_p, anchors, cfg) ids_p, class_p, boxes_p, masks_p = after_nms(ids_p, class_p, box_p, coef_p, proto_p, img_h, img_w, cfg, img_name=img_name) # 种类id, 置信度,bbox[n, 4],mask[n, img_h, img_w] print(ids_p.shape, class_p.shape, boxes_p.shape, masks_p.shape) if args.background: saveBackground(ids_p, class_p, boxes_p, img_name) cfg.cutout = False else: save(ids_p, class_p, boxes_p, masks_p, img_name) cfg.cutout = False # output the image with masks and bounding boxes # if --cutout set to true, the cutout objects also be saved # cutout images save to results/image img_numpy = draw_img(ids_p, class_p, boxes_p, masks_p, img_origin, cfg, img_name=img_name) cv2.imwrite(f'{args.image}/detect/{img_name}_detect.jpg', img_numpy) endTime = time.perf_counter() print(f'Time cost:{endTime-startTime:.3f}s') labRecord.cutoutSummary() else: print("cfg.image is None!")