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116 lines
4.6 KiB
116 lines
4.6 KiB
from pathlib import Path
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import hydra
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import torch
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from ultralytics.yolo.engine.trainer import DEFAULT_CONFIG
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from ultralytics.yolo.utils import ROOT, ops
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from ultralytics.yolo.utils.plotting import Annotator, colors, save_one_box
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from ..detect.predict import DetectionPredictor
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class SegmentationPredictor(DetectionPredictor):
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def postprocess(self, preds, img, orig_img):
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masks = []
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if len(preds) == 2: # eval
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p, proto, = preds
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else: # len(3) train
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p, proto, _ = preds
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# TODO: filter by classes
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p = ops.non_max_suppression(p,
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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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nm=32)
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for i, pred in enumerate(p):
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shape = orig_img[i].shape if self.webcam else orig_img.shape
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if not len(pred):
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continue
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if self.args.retina_masks:
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pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], shape).round()
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masks.append(ops.process_mask_native(proto[i], pred[:, 6:], pred[:, :4], shape[:2])) # HWC
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else:
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masks.append(ops.process_mask(proto[i], pred[:, 6:], pred[:, :4], img.shape[2:], upsample=True)) # HWC
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pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], shape).round()
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return (p, masks)
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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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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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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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preds, masks = preds
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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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# Segments
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mask = masks[idx]
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if self.args.save_txt:
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segments = [
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ops.scale_segments(im0.shape if self.arg.retina_masks else im.shape[2:], x, im0.shape, normalize=True)
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for x in reversed(ops.masks2segments(mask))]
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# Print results
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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)}, " # add to string
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# Mask plotting
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self.annotator.masks(
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mask,
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colors=[colors(x, True) for x in det[:, 5]],
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im_gpu=torch.as_tensor(im0, dtype=torch.float16).to(self.device).permute(2, 0, 1).flip(0).contiguous() /
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255 if self.args.retina_masks else im[idx])
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# Write results
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for j, (*xyxy, conf, cls) in enumerate(reversed(det[:, :6])):
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if self.args.save_txt: # Write to file
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seg = segments[j].reshape(-1) # (n,2) to (n*2)
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line = (cls, *seg, conf) if self.args.save_conf else (cls, *seg) # 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:
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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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# annotator.draw.polygon(segments[j], outline=colors(c, True), width=3)
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if self.args.save_crop:
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imc = im0.copy()
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save_one_box(xyxy, imc, file=self.save_dir / 'crops' / self.model.names[c] / f'{p.stem}.jpg', 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 = SegmentationPredictor(cfg)
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predictor()
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if __name__ == "__main__":
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predict()
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