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swap cv2.INTER_AREA for cv2.INTER_LINEAR

This commit is contained in:
Glenn Jocher 2020-04-10 12:47:07 -07:00
parent b98ce11d3a
commit 6736d7d125
1 changed files with 6 additions and 9 deletions
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15
utils/datasets.py
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@ -240,7 +240,7 @@ class LoadStreams: # multiple IP or RTSP cameras
raise StopIteration
# Letterbox
img = [letterbox(x, new_shape=self.img_size, auto=self.rect, interp=cv2.INTER_LINEAR)[0] for x in img0]
img = [letterbox(x, new_shape=self.img_size, auto=self.rect)[0] for x in img0]
# Stack
img = np.stack(img, 0)
@ -508,8 +508,7 @@ def load_image(self, index):
h0, w0 = img.shape[:2] # orig hw
r = self.img_size / max(h0, w0) # resize image to img_size
if r < 1 or (self.augment and (r != 1)): # always resize down, only resize up if training with augmentation
interp = cv2.INTER_LINEAR if self.augment else cv2.INTER_AREA # LINEAR for training, AREA for testing
img = cv2.resize(img, (int(w0 * r), int(h0 * r)), interpolation=interp)
img = cv2.resize(img, (int(w0 * r), int(h0 * r)), interpolation=cv2.INTER_LINEAR)
return img, (h0, w0), img.shape[:2] # img, hw_original, hw_resized
else:
return self.imgs[index], self.img_hw0[index], self.img_hw[index] # img, hw_original, hw_resized
@ -589,8 +588,7 @@ def load_mosaic(self, index):
return img4, labels4
def letterbox(img, new_shape=(416, 416), color=(114, 114, 114),
auto=True, scaleFill=False, scaleup=True, interp=cv2.INTER_AREA):
def letterbox(img, new_shape=(416, 416), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True):
# Resize 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):
@ -616,7 +614,7 @@ def letterbox(img, new_shape=(416, 416), color=(114, 114, 114),
dh /= 2
if shape[::-1] != new_unpad: # resize
img = cv2.resize(img, new_unpad, interpolation=interp) # INTER_AREA is better, INTER_LINEAR is faster
img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
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
@ -651,9 +649,8 @@ def random_affine(img, targets=(), degrees=10, translate=.1, scale=.1, shear=10,
# Combined rotation matrix
M = S @ T @ R # ORDER IS IMPORTANT HERE!!
changed = (border != 0) or (M != np.eye(3)).any()
if changed:
img = cv2.warpAffine(img, M[:2], dsize=(width, height), flags=cv2.INTER_AREA, borderValue=(114, 114, 114))
if (border != 0) or (M != np.eye(3)).any(): # image changed
img = cv2.warpAffine(img, M[:2], dsize=(width, height), flags=cv2.INTER_LINEAR, borderValue=(114, 114, 114))
# Transform label coordinates
n = len(targets)