for extra in range(3):
center = np.array([
int(image.shape[0] / 2),
int(image.shape[1] / 2)
])
deg = np.random.randint(-10, 10)
#deg = np.random.normal()*30
newimg = aug.salt_and_pepper(
aug.gaussian_blur(image))
#.3 probability for each of shifting vs rotating vs shift(rotate(image))
p = np.random.randint(0, 3)
if p == 0:
newimg, nb = aug.shift_image(newimg, box[idx])
elif p == 1:
newimg, nb = aug.rotate_image_and_boxes(
newimg, deg, center, box[idx])
elif p == 2:
newimg, nb = aug.rotate_image_and_boxes(
newimg, deg, center, box[idx])
newimg, nb = aug.shift_image(newimg, nb)
newimg = (newimg).astype(np.uint8)
if idx % 1000 == 0 and SAVE_IMAGES:
Image.fromarray(newimg).save(
'process/img_%s_%s_%s.png' %
(name, extra, it[0]))
if len(nb) > 0:
tf_example = tfr.to_tf_example(
newimg, nb, classes_final[idx])
# remove and gaussian blur
newimg = aug.gaussian_blur(image)
#newimg = image
#.3 probability for each of shifting vs rotating vs shift(rotate(image))
p = np.random.randint(0, 3)
# debug
# modified to use the removed cloud version of bboxes
# image, new_coords, new_classes
if p == 0:
newimg, nb = aug.shift_image(
newimg, new_coords)
#newimg,nb = aug.shift_image(newimg,box[idx])
elif p == 1:
newimg, nb = aug.rotate_image_and_boxes(
newimg, deg, center, new_coords)
#newimg,nb = aug.rotate_image_and_boxes(newimg,deg,center,box[idx])
elif p == 2:
newimg, nb = aug.rotate_image_and_boxes(
newimg, deg, center, new_coords)
#newimg,nb = aug.rotate_image_and_boxes(newimg,deg,center,box[idx])
newimg, nb = aug.shift_image(newimg, nb)
newimg = (newimg).astype(np.uint8)
if idx % 100 == 0 and SAVE_IMAGES:
#debug
# changed save dir
Image.fromarray(newimg).save(
'./augmented_img_60/img_%s_%s_%s.png' %
(name, extra, it[0]))
tot_box += len(box[idx])
if name in train_fs:
img_dict, annos, ann_id = output_img(
image, box[idx], classes_final[idx], out_train_dir,
fname, idx, ind_chips, ann_id, cat_list)
train_ann['annotations'].extend(annos)
train_ann['images'].append(img_dict)
train_chips += 1
if AUGMENT:
degs = [10, 90, 180, 270]
center = (int(image.shape[0] / 2),
int(image.shape[1] / 2))
for deg in degs:
ind_chips += 1
nimage, nbox = aug.rotate_image_and_boxes(
image, deg, center, box[idx])
img_dict, annos, ann_id = output_img(
nimage,
nbox,
classes_final[idx],
out_train_dir,
fname,
idx,
ind_chips,
ann_id,
cat_list,
aug=True)
train_ann['annotations'].extend(annos)
train_ann['images'].append(img_dict)
train_chips += 1
elif name in val_fs:
num_chips = len(c_img)
print("\tNum Chips: ", num_chips)
# For each image chip (i in range(num_chips))
for i in range(num_chips):
print("\t\tChip #: ", i)
# Calculate the center of the chip
center = (int(c_img[i].shape[0] / 2), int(c_img[i].shape[1] / 2))
# For each of the desired rotation degrees
for deg in DEGREES:
# Rotate the original chip and get the updated image/boxes/classes
tmp_img, tmp_box, tmp_cls = aug.rotate_image_and_boxes(
c_img[i], deg, center, c_box[i], c_cls[i])
# Git rid of very small boxes that are artifacts of chipping
final_boxes = []
final_classes = []
final_classes_simple = []
# Clip boxes correctly!!
clipped_boxes = []
for box in tmp_box:
xMin, yMin, xMax, yMax = box
xMin = clip(xMin, 0, CHIP_SHAPE[0] - 1)
yMin = clip(yMin, 0, CHIP_SHAPE[1] - 1)
xMax = clip(xMax, 0, CHIP_SHAPE[0] - 1)
yMax = clip(yMax, 0, CHIP_SHAPE[1] - 1)
clipped_boxes.append([xMin, yMin, xMax, yMax])
# ...
# ])
# 1: array([ [...], [...], ... ])
# ...
# }
#
# c_cls = dictionary keyed by integer and values are arrays of ints which are class labels.
# i.e. c_cls = {
# 0: [10, 15, 21, 29, 71, 20]
# 1: [30, 55, 22, 19, 61, 30]
# ...
# }
# The keys of the dictionaries match up with len(c_img). The details for chip
# c_img[0] are stored in c_box[0] and c_cls[0]. So, len(c_img) == len(c_box) == len(c_cls)
c_img, c_box, c_cls = wv.chip_image(img=arr,
coords=coords,
classes=classes,
shape=(700, 700))
# Augment the data
ind = np.random.choice(range(c_img.shape[0]))
center = (int(c_img[ind].shape[0] / 2), int(c_img[ind].shape[1] / 2))
for deg in range(0, 360, 45):
rot_im, rot_boxes = aug.rotate_image_and_boxes(c_img[ind], deg, center,
c_box[ind])
pim = aug.draw_bboxes(rot_im, rot_boxes)
plt.imshow(pim)
plt.title("deg: {}".format(deg))
plt.show()
