def data_noshift(mission, img_path, json_path, lamda_shift,lamda_scale,min_scale,max_scale):
json_file = open(json_path)
str = json_file.read()
data = json.loads(str,encoding="GB2312")
x_value_curr = [data[u'plate_0'][u'PosPt0X'],data[u'plate_0'][u'PosPt1X'],data[u'plate_0'][u'PosPt2X'],data[u'plate_0'][u'PosPt3X']]
y_value_curr = [data[u'plate_0'][u'PosPt0Y'],data[u'plate_0'][u'PosPt1Y'],data[u'plate_0'][u'PosPt2Y'],data[u'plate_0'][u'PosPt3Y']]
x1 = (min(x_value_curr))/10#x bottom left
y1 = (min(y_value_curr))/10#y bottom left
x2 = (max(x_value_curr))/10#x top right
y2 = (max(y_value_curr))/10#y top right
print num_img, ' bbox:',x1,y1,x2,y2
bbox = BoundingBox(x1,y1,x2,y2)
image = cv2.imread(img_path)
bbox_curr_gt = bbox
rand_search_region, rand_search_location , edge_spacing_x, edge_spacing_y = cropPadImage(bbox_curr_gt, image)
bbox_gt_recentered = BoundingBox(0, 0, 0, 0)
bbox_gt_recentered = bbox_curr_gt.recenter(rand_search_location, edge_spacing_x, edge_spacing_y, bbox_gt_recentered)
bbox_gt_recentered.scale(rand_search_region)
bbox_gt_scaled = bbox_gt_recentered
bbox_curr_y = [bbox_gt_scaled.x1, bbox_gt_scaled.y1, bbox_gt_scaled.x2, bbox_gt_scaled.y2]
cv2.imwrite('/home/huaxin/data_3T/license_dataset/license_track/Dataset_0/' + mission + '/image_unprocess_'+format(num_img, '05d')+'.jpg', rand_search_region)
# train_y_bbox_curr.append(bbox_curr_y)
return bbox_curr_y
image_index = num_img
img_path = train_x_path_curr[image_index]
image = cv2.imread(img_path)
# if num_img == 0:
# cv2.imshow("first", image)
# cv2.waitKey(0)
bbox = train_y_bbox_curr[num_img]
bbox_curr_gt = bbox
bbox_curr_shift = BoundingBox(0, 0, 0, 0)
bbox_curr_shift = bbox_curr_gt.shift(image, lamda_scale, lamda_shift,
min_scale, max_scale, True,
bbox_curr_shift)
rand_search_region, rand_search_location, edge_spacing_x, edge_spacing_y = cropPadImage(
bbox_curr_shift, image)
rand_search_x = preprocess(rand_search_region)
bbox_estimate = sess.run(logits,
feed_dict={
x_cur: [rand_search_x],
y: [[1, 1, 1, 1]],
keep_prob: 1
})
bbox_estimate = BoundingBox(bbox_estimate[0, 0], bbox_estimate[0, 1],
bbox_estimate[0, 2], bbox_estimate[0, 3])
# Inplace correction of bounding box
print 'height', rand_search_region.shape[
# Capture frame-by-frame
frame = cam.get_image()
frame_numpy = pygame.surfarray.array3d(frame)
frame_numpy = np.fliplr(frame_numpy)
frame_numpy_90 = np.rot90(frame_numpy, 1)
b, g, r = cv2.split(frame_numpy_90) # get b, g, r
frame_input = cv2.merge([r, g, b]) # switch it to r, g, b
image_curr_input = frame_input
# cur_search_region, search_location, edge_spacing_x, edge_spacing_y = cropPadImage(bbox_curr_prior_tight, image_curr_input)
bbox_curr_gt = bbox_curr_prior_tight
# bbox_curr_shift = BoundingBox(0, 0, 0, 0)
# bbox_curr_shift = bbox_curr_gt.shift(image_curr_input, lamda_scale, lamda_shift, min_scale, max_scale, True, bbox_curr_shift)
cur_search_region, search_location, edge_spacing_x, edge_spacing_y = cropPadImage(
bbox_curr_gt, image_curr_input)
curr_search_x = preprocess(cur_search_region)
bbox_estimate = sess.run(logits,
feed_dict={
x_cur: [curr_search_x],
y: [[1, 1, 1, 1]],
keep_prob: 1
})
bbox_estimate = BoundingBox(bbox_estimate[0, 0], bbox_estimate[0, 1],
bbox_estimate[0, 2], bbox_estimate[0, 3])
# Inplace correction of bounding box
print 'height', cur_search_region.shape[
0], 'width', cur_search_region.shape[1]