def preprocess_image(anno, H):
image = imread(anno.imageName)
# skip greyscale images
if len(image.shape) < 3:
return []
# 4 channels images check
if image.shape[2] == 4:
image = image[:, :, :3]
if 'rotate90' in H['data'] and H['data']['rotate90']:
image, anno = Rotate90.do(image, anno)
if image.shape[0] != H["image_height"] or image.shape[1] != H[
"image_width"]:
anno = rescale_boxes(image.shape, anno, H["image_height"],
H["image_width"])
image = imresize(image, (H["image_height"], H["image_width"]),
interp='cubic')
result = [(image, anno)]
if 'augmentations' in H['data']:
new_res = []
for i, a in result:
augmented = Augmentations(H['data']).process(i, a)
new_res.append(augmented)
result = new_res
return result
def rect_predict(image_path, parameters, to_json, H, options):
orig_img = imread(image_path)[:, :, :3]
img = Rotate90.do(
orig_img
)[0] if 'rotate90' in H['data'] and H['data']['rotate90'] else orig_img
img = imresize(img, (H['image_height'], H['image_width']), interp='cubic')
np_pred_boxes, np_pred_confidences = parameters['sess']. \
run([parameters['pred_boxes'], parameters['pred_confidences']], feed_dict={parameters['x_in']: img})
image_info = {
'path': image_path,
'original_shape': img.shape[:2],
'transformed': img
}
pred_anno = postprocess_regular(image_info, np_pred_boxes,
np_pred_confidences, H, options)
ret_list = [{
'x1': r.x1,
'x2': r.x2,
'y1': r.y1,
'y2': r.y2,
} for r in pred_anno]
return ret_list
def preprocess_image(anno, H):
image = imread(anno.imageName)
# skip greyscale images
if len(image.shape) < 3:
return []
if 'rotate90' in H['data'] and H['data']['rotate90']:
image, anno = Rotate90.do(image, anno)
result = [(image, anno)]
if 'augmentations' in H['data']:
new_res = []
for i, a in result:
augmented = Augmentations(H['data']).process(i, a)
new_res.append(augmented)
result = new_res
return result
def sliding_predict(image_path, parameters, to_json, H, options):
orig_img = imread(image_path)[:, :, :3]
height, width, _ = orig_img.shape
if options.get('verbose', False):
print(width, height)
sl_win_options = H['sliding_predict']
assert (sl_win_options['window_height'] > sl_win_options['overlap'])
slides = propose_slides(height, sl_win_options['window_height'],
sl_win_options['overlap'])
result = []
for top, bottom in slides:
bottom = min(height, top + sl_win_options['window_height'])
if options.get('verbose', False):
print('Slide: ', 0, top, width, bottom)
img = orig_img[top:bottom, 0:width]
img = Rotate90.do(
img
)[0] if 'rotate90' in H['data'] and H['data']['rotate90'] else img
img = imresize(img, (H['image_height'], H['image_width']),
interp='cubic')
np_pred_boxes, np_pred_confidences = parameters['sess']. \
run([parameters['pred_boxes'], parameters['pred_confidences']], feed_dict={parameters['x_in']: img})
image_info = {
'path': image_path,
'original_shape': (bottom - top, width),
'transformed': img,
'a': orig_img[top:bottom, 0:width]
}
pred_boxes = postprocess_regular(image_info, np_pred_boxes,
np_pred_confidences, H, options)
shift_boxes(pred_boxes, top)
result.extend(pred_boxes)
result = combine_boxes(result, sl_win_options['iou_min'],
sl_win_options['nms'])
result = [r.writeJSON() for r in result] if to_json else result
return result