Bienqq
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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-03-20 19:30:10 +02:00
parent e0eb62706d
commit e7075f2b23
1 changed files with 8 additions and 8 deletions
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16
utils/datasets.py
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@ -43,9 +43,9 @@ class LoadImages: # for inference
img, _, _, _ = letterbox(img0, height=self.height) img, _, _, _ = letterbox(img0, height=self.height)
# Normalize RGB # Normalize RGB
img = img[:, :, ::-1].transpose(2, 0, 1) img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB
img = np.ascontiguousarray(img, dtype=np.float32) img = np.ascontiguousarray(img, dtype=np.float32) # uint8 to float32
img /= 255.0 img /= 255.0 # 0 - 255 to 0.0 - 1.0
# cv2.imwrite(img_path + '.letterbox.jpg', 255 * img.transpose((1, 2, 0))[:, :, ::-1]) # save letterbox image # cv2.imwrite(img_path + '.letterbox.jpg', 255 * img.transpose((1, 2, 0))[:, :, ::-1]) # save letterbox image
return img_path, img, img0 return img_path, img, img0
@ -79,9 +79,9 @@ class LoadWebcam: # for inference
img, _, _, _ = letterbox(img0, height=self.height) img, _, _, _ = letterbox(img0, height=self.height)
# Normalize RGB # Normalize RGB
img = img[:, :, ::-1].transpose(2, 0, 1) img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB
img = np.ascontiguousarray(img, dtype=np.float32) img = np.ascontiguousarray(img, dtype=np.float32) # uint8 to float32
img /= 255.0 img /= 255.0 # 0 - 255 to 0.0 - 1.0
return img_path, img, img0 return img_path, img, img0
@ -207,8 +207,8 @@ class LoadImagesAndLabels: # for training
img_shapes.append((h, w)) img_shapes.append((h, w))
# Normalize # Normalize
img_all = np.stack(img_all)[:, :, :, ::-1].transpose(0, 3, 1, 2) # BGR to RGB and cv2 to pytorch img_all = np.stack(img_all)[:, :, :, ::-1].transpose(0, 3, 1, 2) # list to np.array and BGR to RGB
img_all = np.ascontiguousarray(img_all, dtype=np.float32) # int8 to float32 img_all = np.ascontiguousarray(img_all, dtype=np.float32) # uint8 to float32
img_all /= 255.0 # 0 - 255 to 0.0 - 1.0 img_all /= 255.0 # 0 - 255 to 0.0 - 1.0
labels_all = torch.from_numpy(np.concatenate(labels_all, 0)) labels_all = torch.from_numpy(np.concatenate(labels_all, 0))