Bienqq
/
car-detection-bayes
2
1
Fork 0
Code Issues Pull Requests Releases Wiki Activity

dataloader default color to imagenet mean 114

This commit is contained in:
Glenn Jocher 2020-04-05 18:02:41 -07:00
parent 2baf4e3f93
commit 26fc4fb018
1 changed files with 3 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
utils/datasets.py
View File

@ -536,7 +536,7 @@ def load_mosaic(self, index):
labels4 = [] labels4 = []
s = self.img_size s = self.img_size
xc, yc = [int(random.uniform(s * 0.5, s * 1.5)) for _ in range(2)] # mosaic center x, y xc, yc = [int(random.uniform(s * 0.5, s * 1.5)) for _ in range(2)] # mosaic center x, y
img4 = np.zeros((s * 2, s * 2, 3), dtype=np.uint8) + 128 # base image with 4 tiles img4 = np.full((s * 2, s * 2, 3), 114, dtype=np.uint8) # base image with 4 tiles
indices = [index] + [random.randint(0, len(self.labels) - 1) for _ in range(3)] # 3 additional image indices indices = [index] + [random.randint(0, len(self.labels) - 1) for _ in range(3)] # 3 additional image indices
for i, index in enumerate(indices): for i, index in enumerate(indices):
# Load image # Load image
@ -597,7 +597,7 @@ def load_mosaic(self, index):
return img4, labels4 return img4, labels4
def letterbox(img, new_shape=(416, 416), color=(128, 128, 128), def letterbox(img, new_shape=(416, 416), color=(114, 114, 114),
auto=True, scaleFill=False, scaleup=True, interp=cv2.INTER_AREA): auto=True, scaleFill=False, scaleup=True, interp=cv2.INTER_AREA):
# Resize image to a 32-pixel-multiple rectangle https://github.com/ultralytics/yolov3/issues/232 # Resize image to a 32-pixel-multiple rectangle https://github.com/ultralytics/yolov3/issues/232
shape = img.shape[:2] # current shape [height, width] shape = img.shape[:2] # current shape [height, width]
@ -661,7 +661,7 @@ def random_affine(img, targets=(), degrees=10, translate=.1, scale=.1, shear=10,
M = S @ T @ R # ORDER IS IMPORTANT HERE!! M = S @ T @ R # ORDER IS IMPORTANT HERE!!
changed = (border != 0) or (M != np.eye(3)).any() changed = (border != 0) or (M != np.eye(3)).any()
if changed: if changed:
img = cv2.warpAffine(img, M[:2], dsize=(width, height), flags=cv2.INTER_AREA, borderValue=(128, 128, 128)) img = cv2.warpAffine(img, M[:2], dsize=(width, height), flags=cv2.INTER_AREA, borderValue=(114, 114, 114))
# Transform label coordinates # Transform label coordinates
n = len(targets) n = len(targets)