Bienqq
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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-10-13 17:40:45 +02:00
parent 0985dc91d5
commit 2f46e7d765
2 changed files with 14 additions and 14 deletions
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2
train.py
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@ -65,7 +65,7 @@ def train():
multi_scale = opt.multi_scale multi_scale = opt.multi_scale
if multi_scale: if multi_scale:
img_sz_min = 7 # round(img_size / 32 / 1.5) + 1 img_sz_min = 14 # round(img_size / 32 / 1.5) + 1
img_sz_max = 27 # round(img_size / 32 * 1.5) - 1 img_sz_max = 27 # round(img_size / 32 * 1.5) - 1
img_size = img_sz_max * 32 # initiate with maximum multi_scale size img_size = img_sz_max * 32 # initiate with maximum multi_scale size
print('Using multi-scale %g - %g' % (img_sz_min * 32, img_size)) print('Using multi-scale %g - %g' % (img_sz_min * 32, img_size))

26
utils/datasets.py
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@ -511,7 +511,7 @@ def load_image(self, index):
img = cv2.imread(img_path) # BGR img = cv2.imread(img_path) # BGR
assert img is not None, 'Image Not Found ' + img_path assert img is not None, 'Image Not Found ' + img_path
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 < 1.0: # 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_LINEAR) # _LINEAR fastest img = cv2.resize(img, (int(w * r), int(h * r)), interpolation=cv2.INTER_LINEAR) # _LINEAR fastest
return img return img
@ -644,7 +644,7 @@ def random_affine(img, targets=(), degrees=10, translate=.1, scale=.1, shear=10)
# torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10)) # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
# https://medium.com/uruvideo/dataset-augmentation-with-random-homographies-a8f4b44830d4 # https://medium.com/uruvideo/dataset-augmentation-with-random-homographies-a8f4b44830d4
if targets is None: if targets is None: # targets = [cls, xyxy]
targets = [] targets = []
border = 0 # width of added border (optional) border = 0 # width of added border (optional)
height = img.shape[0] + border * 2 height = img.shape[0] + border * 2
@ -667,19 +667,18 @@ def random_affine(img, targets=(), degrees=10, translate=.1, scale=.1, shear=10)
S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg) S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg)
S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg) S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg)
M = S @ T @ R # Combined rotation matrix. ORDER IS IMPORTANT HERE!! # Combined rotation matrix
imw = cv2.warpAffine(img, M[:2], dsize=(width, height), flags=cv2.INTER_AREA, M = S @ T @ R # ORDER IS IMPORTANT HERE!!
borderValue=(128, 128, 128)) # BGR order borderValue changed = (border != 0) or (M != np.eye(3)).any()
if changed:
# Return warped points also img = cv2.warpAffine(img, M[:2], dsize=(width, height), flags=cv2.INTER_AREA, borderValue=(128, 128, 128))
if len(targets) > 0:
n = targets.shape[0]
points = targets[:, 1:5].copy()
area0 = (points[:, 2] - points[:, 0]) * (points[:, 3] - points[:, 1])
# Transform label coordinates
n = len(targets)
if n:
# warp points # warp points
xy = np.ones((n * 4, 3)) xy = np.ones((n * 4, 3))
xy[:, :2] = points[:, [0, 1, 2, 3, 0, 3, 2, 1]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1 xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1
xy = (xy @ M.T)[:, :2].reshape(n, 8) xy = (xy @ M.T)[:, :2].reshape(n, 8)
# create new boxes # create new boxes
@ -702,13 +701,14 @@ def random_affine(img, targets=(), degrees=10, translate=.1, scale=.1, shear=10)
w = xy[:, 2] - xy[:, 0] w = xy[:, 2] - xy[:, 0]
h = xy[:, 3] - xy[:, 1] h = xy[:, 3] - xy[:, 1]
area = w * h area = w * h
area0 = (targets[:, 3] - targets[:, 1]) * (targets[:, 4] - targets[:, 2])
ar = np.maximum(w / (h + 1e-16), h / (w + 1e-16)) ar = np.maximum(w / (h + 1e-16), h / (w + 1e-16))
i = (w > 4) & (h > 4) & (area / (area0 + 1e-16) > 0.1) & (ar < 10) i = (w > 4) & (h > 4) & (area / (area0 + 1e-16) > 0.1) & (ar < 10)
targets = targets[i] targets = targets[i]
targets[:, 1:5] = xy[i] targets[:, 1:5] = xy[i]
return imw, targets return img, targets
def cutout(image, labels): def cutout(image, labels):