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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-11-30 16:58:56 -08:00
parent ff41a15a2b
commit 1ff01f0973
1 changed files with 22 additions and 26 deletions
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48
utils/datasets.py
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@ -424,9 +424,9 @@ class LoadImagesAndLabels(Dataset): # for training/testing
# Letterbox
h, w = img.shape[:2]
if self.rect:
img, ratio, padw, padh = letterbox(img, self.batch_shapes[self.batch[index]], mode='rect')
img, ratio, pad = letterbox(img, self.batch_shapes[self.batch[index]], mode='rect')
else:
img, ratio, padw, padh = letterbox(img, self.img_size, mode='square')
img, ratio, pad = letterbox(img, self.img_size, mode='square')
# Load labels
labels = []
@ -439,10 +439,10 @@ class LoadImagesAndLabels(Dataset): # for training/testing
if x.size > 0:
# Normalized xywh to pixel xyxy format
labels = x.copy()
labels[:, 1] = ratio[0] * w * (x[:, 1] - x[:, 3] / 2) + padw
labels[:, 2] = ratio[1] * h * (x[:, 2] - x[:, 4] / 2) + padh
labels[:, 3] = ratio[0] * w * (x[:, 1] + x[:, 3] / 2) + padw
labels[:, 4] = ratio[1] * h * (x[:, 2] + x[:, 4] / 2) + padh
labels[:, 1] = ratio[0] * w * (x[:, 1] - x[:, 3] / 2) + pad[0] # pad width
labels[:, 2] = ratio[1] * h * (x[:, 2] - x[:, 4] / 2) + pad[1]
labels[:, 3] = ratio[0] * w * (x[:, 1] + x[:, 3] / 2) + pad[0]
labels[:, 4] = ratio[1] * h * (x[:, 2] + x[:, 4] / 2) + pad[1]
if self.augment:
# Augment colorspace
@ -610,39 +610,35 @@ def load_mosaic(self, index):
return img4, labels4
def letterbox(img, new_shape=416, color=(128, 128, 128), mode='auto', interp=cv2.INTER_AREA):
def letterbox(img, new_shape=(416, 416), color=(128, 128, 128), mode='auto', interp=cv2.INTER_AREA):
# Resize a rectangular image to a 32 pixel multiple rectangle
# https://github.com/ultralytics/yolov3/issues/232
shape = img.shape[:2] # current shape [height, width]
if isinstance(new_shape, int):
r = float(new_shape) / max(shape) # ratio = new / old
else:
r = max(new_shape) / max(shape)
ratio = r, r # width, height ratios
new_unpad = (int(round(shape[1] * r)), int(round(shape[0] * r)))
new_shape = (new_shape, new_shape)
# Compute padding https://github.com/ultralytics/yolov3/issues/232
r = max(new_shape) / max(shape) # ratio = new / old
ratio = r, r # width, height ratios
new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
# Compute padding
dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
if mode is 'auto': # minimum rectangle
dw = np.mod(new_shape - new_unpad[0], 32) / 2 # width padding
dh = np.mod(new_shape - new_unpad[1], 32) / 2 # height padding
elif mode is 'square': # square
dw = (new_shape - new_unpad[0]) / 2 # width padding
dh = (new_shape - new_unpad[1]) / 2 # height padding
elif mode is 'rect': # square
dw = (new_shape[1] - new_unpad[0]) / 2 # width padding
dh = (new_shape[0] - new_unpad[1]) / 2 # height padding
elif mode is 'scaleFill':
dw, dh = np.mod(dw, 32), np.mod(dh, 32) # wh padding
elif mode is 'scaleFill': # stretch
dw, dh = 0.0, 0.0
new_unpad = (new_shape, new_shape)
ratio = new_shape / shape[1], new_shape / shape[0] # width, height ratios
new_unpad = new_shape
ratio = new_shape[0] / shape[1], new_shape[1] / shape[0] # width, height ratios
dw /= 2 # divide padding into 2 sides
dh /= 2
if shape[::-1] != new_unpad: # resize
img = cv2.resize(img, new_unpad, interpolation=interp) # INTER_AREA is better, INTER_LINEAR is faster
top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border
return img, ratio, dw, dh
return img, ratio, (dw, dh)
def random_affine(img, targets=(), degrees=10, translate=.1, scale=.1, shear=10):