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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-04-22 16:17:01 +02:00
parent e5d11c68ac
commit 23cd4ecfa7
1 changed files with 10 additions and 21 deletions
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31
utils/datasets.py
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@ -74,7 +74,7 @@ class LoadImages: # for inference
print('image %g/%g %s: ' % (self.count, self.nF, path), end='')
# Padded resize
img, _, _, _ = letterbox_rect(img0, height=self.height)
img, _, _, _ = letterbox(img0, height=self.height)
print('%gx%g ' % img.shape[:2], end='') # print image size
# Normalize RGB
@ -176,7 +176,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img)
h, w, _ = img.shape
img, ratio, padw, padh = letterbox(img, height=self.img_size)
img, ratio, padw, padh = letterbox(img, height=self.img_size, mode='square')
# Load labels
labels = []
@ -236,30 +236,19 @@ class LoadImagesAndLabels(Dataset): # for training/testing
return torch.stack(img, 0), torch.cat(label, 0), path, hw
def letterbox(img, height=416, color=(127.5, 127.5, 127.5)):
# Resize a rectangular image to a padded square
shape = img.shape[:2] # shape = [height, width]
ratio = float(height) / max(shape) # ratio = old / new
new_shape = (round(shape[1] * ratio), round(shape[0] * ratio)) # new_shape = [width, height]
dw = (height - new_shape[0]) / 2 # width padding
dh = (height - new_shape[1]) / 2 # height padding
top, bottom = round(dh - 0.1), round(dh + 0.1)
left, right = round(dw - 0.1), round(dw + 0.1)
img = cv2.resize(img, new_shape, interpolation=cv2.INTER_AREA) # resized, no border
img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # padded square
return img, ratio, dw, dh
def letterbox_rect(img, height=416, color=(127.5, 127.5, 127.5)):
def letterbox(img, height=416, color=(127.5, 127.5, 127.5), mode='rect'):
# Resize a rectangular image to a 32 pixel multiple rectangle
shape = img.shape[:2] # shape = [height, width]
ratio = float(height) / max(shape) # ratio = old / new
new_shape = (round(shape[1] * ratio), round(shape[0] * ratio)) # new_shape = [width, height]
dw = np.mod(height - new_shape[0], 32) / 2 # width padding
dh = np.mod(height - new_shape[1], 32) / 2 # height padding
# Select padding https://github.com/ultralytics/yolov3/issues/232
if mode is 'rect': # rectangle
dw = np.mod(height - new_shape[0], 32) / 2 # width padding
dh = np.mod(height - new_shape[1], 32) / 2 # height padding
else: # square
dw = (height - new_shape[0]) / 2 # width padding
dh = (height - new_shape[1]) / 2 # height padding
top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
left, right = int(round(dw - 0.1)), int(round(dw + 0.1))