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ultralytics 8.0.40 TensorRT metadata and Results visualizer (#1014)

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Glenn Jocher 2023-02-17 20:06:06 +01:00 • committed by GitHub
parent e799592718
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40 changed files with 576 additions and 280 deletions
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10
ultralytics/yolo/v8/segment/predict.py
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@ -10,7 +10,7 @@ from ultralytics.yolo.v8.detect.predict import DetectionPredictor
class SegmentationPredictor(DetectionPredictor):
def postprocess(self, preds, img, orig_img, classes=None):
def postprocess(self, preds, img, orig_img):
# TODO: filter by classes
p = ops.non_max_suppression(preds[0],
self.args.conf,
@ -22,9 +22,11 @@ class SegmentationPredictor(DetectionPredictor):
results = []
proto = preds[1][-1] if len(preds[1]) == 3 else preds[1] # second output is len 3 if pt, but only 1 if exported
for i, pred in enumerate(p):
shape = orig_img[i].shape if isinstance(orig_img, list) else orig_img.shape
orig_img = orig_img[i] if isinstance(orig_img, list) else orig_img
shape = orig_img.shape
if not len(pred):
results.append(Results(boxes=pred[:, :6], orig_shape=shape[:2])) # save empty boxes
results.append(Results(boxes=pred[:, :6], orig_img=orig_img,
names=self.model.names)) # save empty boxes
continue
if self.args.retina_masks:
pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], shape).round()
@ -32,7 +34,7 @@ class SegmentationPredictor(DetectionPredictor):
else:
masks = ops.process_mask(proto[i], pred[:, 6:], pred[:, :4], img.shape[2:], upsample=True) # HWC
pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], shape).round()
results.append(Results(boxes=pred[:, :6], masks=masks, orig_shape=shape[:2]))
results.append(Results(boxes=pred[:, :6], masks=masks, orig_img=orig_img, names=self.model.names))
return results
def write_results(self, idx, results, batch):