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97 lines
3.9 KiB
97 lines
3.9 KiB
import hydra |
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import torch |
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from ultralytics.yolo.engine.predictor import BasePredictor |
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from ultralytics.yolo.engine.trainer import DEFAULT_CONFIG |
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from ultralytics.yolo.utils import ops |
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from ultralytics.yolo.utils.plotting import Annotator, colors, save_one_box |
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class DetectionPredictor(BasePredictor): |
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def get_annotator(self, img): |
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return Annotator(img, line_width=self.args.line_thickness, example=str(self.model.names)) |
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def preprocess(self, img): |
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img = torch.from_numpy(img).to(self.model.device) |
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img = img.half() if self.model.fp16 else img.float() # uint8 to fp16/32 |
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img /= 255 # 0 - 255 to 0.0 - 1.0 |
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return img |
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def postprocess(self, preds, img, orig_img): |
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preds = ops.non_max_suppression(preds, |
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self.args.conf_thres, |
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self.args.iou_thres, |
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agnostic=self.args.agnostic_nms, |
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max_det=self.args.max_det) |
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for i, pred in enumerate(preds): |
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shape = orig_img[i].shape if self.webcam else orig_img.shape |
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pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], shape).round() |
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return preds |
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def write_results(self, idx, preds, batch): |
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p, im, im0 = batch |
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log_string = "" |
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if len(im.shape) == 3: |
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im = im[None] # expand for batch dim |
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self.seen += 1 |
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im0 = im0.copy() |
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if self.webcam: # batch_size >= 1 |
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log_string += f'{idx}: ' |
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frame = self.dataset.count |
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else: |
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frame = getattr(self.dataset, 'frame', 0) |
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self.data_path = p |
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# save_path = str(self.save_dir / p.name) # im.jpg |
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self.txt_path = str(self.save_dir / 'labels' / p.stem) + ('' if self.dataset.mode == 'image' else f'_{frame}') |
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log_string += '%gx%g ' % im.shape[2:] # print string |
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self.annotator = self.get_annotator(im0) |
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det = preds[idx] |
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if len(det) == 0: |
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return log_string |
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for c in det[:, 5].unique(): |
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n = (det[:, 5] == c).sum() # detections per class |
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log_string += f"{n} {self.model.names[int(c)]}{'s' * (n > 1)}, " |
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# write |
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gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh |
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for *xyxy, conf, cls in reversed(det): |
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if self.args.save_txt: # Write to file |
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xywh = (ops.xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh |
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line = (cls, *xywh, conf) if self.args.save_conf else (cls, *xywh) # label format |
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with open(f'{self.txt_path}.txt', 'a') as f: |
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f.write(('%g ' * len(line)).rstrip() % line + '\n') |
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if self.save_img or self.args.save_crop or self.args.view_img: # Add bbox to image |
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c = int(cls) # integer class |
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label = None if self.args.hide_labels else ( |
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self.model.names[c] if self.args.hide_conf else f'{self.model.names[c]} {conf:.2f}') |
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self.annotator.box_label(xyxy, label, color=colors(c, True)) |
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if self.args.save_crop: |
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imc = im0.copy() |
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save_one_box(xyxy, |
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imc, |
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file=self.save_dir / 'crops' / self.model.model.names[c] / f'{self.data_path.stem}.jpg', |
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BGR=True) |
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return log_string |
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@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name) |
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def predict(cfg): |
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cfg.model = cfg.model or "n.pt" |
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sz = cfg.imgsz |
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if type(sz) != int: # recieved listConfig |
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cfg.imgsz = [sz[0], sz[0]] if len(cfg.imgsz) == 1 else [sz[0], sz[1]] # expand |
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else: |
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cfg.imgsz = [sz, sz] |
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predictor = DetectionPredictor(cfg) |
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predictor() |
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if __name__ == "__main__": |
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predict()
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