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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-10-05 13:47:06 +02:00
parent 8610026e2c
commit 563dad3b53
1 changed files with 19 additions and 17 deletions
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36
utils/datasets.py
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@ -643,26 +643,28 @@ def cutout(image, labels):
# Intersection over box2 area # Intersection over box2 area
return inter_area / box2_area return inter_area / box2_area
# random mask_size up to 50% image size # create random masks
s = 0.5 # scale scales = [0.5] * 1 # + [0.25] * 4 + [0.125] * 16 # image size fraction
mask_h = random.randint(1, int(h * s)) for s in scales:
mask_w = random.randint(1, int(w * s)) mask_h = random.randint(1, int(h * s))
mask_w = random.randint(1, int(w * s))
# box # box
xmin = max(0, random.randint(0, w) - mask_w // 2) xmin = max(0, random.randint(0, w) - mask_w // 2)
ymin = max(0, random.randint(0, h) - mask_h // 2) ymin = max(0, random.randint(0, h) - mask_h // 2)
xmax = min(w, xmin + mask_w) xmax = min(w, xmin + mask_w)
ymax = min(h, ymin + mask_h) ymax = min(h, ymin + mask_h)
# apply random color mask # apply random color mask
mask_color = [random.randint(0, 255) for _ in range(3)] mask_color = [random.randint(0, 255) for _ in range(3)]
image[ymin:ymax, xmin:xmax] = mask_color image[ymin:ymax, xmin:xmax] = mask_color
# return unobscured labels
if len(labels):
box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32)
ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area
labels = labels[ioa < 0.90] # remove >90% obscured labels
# return unobscured labels
if len(labels):
box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32)
ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area
labels = labels[ioa < 0.90] # remove >90% obscured labels
return labels return labels