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car-detection-bayes
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Merge remote-tracking branch 'origin/master'

This commit is contained in:
Glenn Jocher 2019-08-01 03:31:21 +02:00
parent f8de211f77 fa6322a15b
commit 7f1f738b74
1 changed files with 2 additions and 2 deletions
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4
utils/datasets.py
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@ -283,7 +283,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
r = self.img_size / max(img.shape) # size ratio r = self.img_size / max(img.shape) # size ratio
if self.augment and r < 1: # if training (NOT testing), downsize to inference shape if self.augment and r < 1: # if training (NOT testing), downsize to inference shape
h, w, _ = img.shape h, w, _ = img.shape
img = cv2.resize(img, (int(w * r), int(h * r)), interpolation=cv2.INTER_AREA) img = cv2.resize(img, (int(w * r), int(h * r)), interpolation=cv2.INTER_LINEAR) # INTER_LINEAR fastest
if self.n < 3000: # cache into memory if image count < 3000 if self.n < 3000: # cache into memory if image count < 3000
self.imgs[index] = img self.imgs[index] = img
@ -410,7 +410,7 @@ def letterbox(img, new_shape=416, color=(128, 128, 128), mode='auto'):
ratiow, ratioh = new_shape / shape[1], new_shape / shape[0] ratiow, ratioh = new_shape / shape[1], new_shape / shape[0]
if shape[::-1] != new_unpad: # resize if shape[::-1] != new_unpad: # resize
img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_AREA) # INTER_AREA for quality, INTER_LINEAR for speed img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR) # INTER_AREA is better, INTER_LINEAR is faster
top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
left, right = int(round(dw - 0.1)), int(round(dw + 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 img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border