boxes, masks, seg_scores = im_detect(im, net)
#print 'boxes{},masks{},seg_scores{}'.format(boxes.shape,masks.shape,seg_scores.shape)
print 'boxes{}'.format(boxes.shape)
end = time.time()
print 'forward time %f' % (end - start)
result_mask, result_box = gpu_mask_voting(masks, boxes, seg_scores,
len(CLASSES) + 1, 300,
im.shape[1], im.shape[0])
#print 'res_box{},res_mask{}'.format(result_box.shape,result_mask.shape)
#pred_dict = get_vis_dict(result_box, result_mask, 'data/demo/' + im_name, CLASSES)
pred_dict = get_vis_dict(result_box, result_mask, data_path + im_name,
CLASSES)
img_width = im.shape[1]
img_height = im.shape[0]
inst_img, cls_img = _convert_pred_to_image(img_width, img_height,
pred_dict)
color_map = _get_voc_color_map()
target_cls_file = os.path.join(res_path,
'cls_maskSeg_' + img_name + '.jpg')
cls_out_img = np.zeros((img_height, img_width, 3))
for i in xrange(img_height):
for j in xrange(img_width):
cls_out_img[i][j] = color_map[cls_img[i][j]][::-1]
cv2.imwrite(target_cls_file, cls_out_img)
background = Image.open(gt_image)
#boxx = (0,0,200,200)
#background = background.crop(boxx)
mask = Image.open(target_cls_file)
background = background.convert('RGBA')
mask = mask.convert('RGBA')
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print 'Demo for data/demo/{}'.format(im_name)
gt_image = os.path.join(demo_dir, im_name)
im = cv2.imread(gt_image)
start = time.time()
boxes, masks, seg_scores = im_detect(im, net)
end = time.time()
print 'forward time %f' % (end-start)
result_mask, result_box = gpu_mask_voting(masks, boxes, seg_scores, len(CLASSES) + 1,
100, im.shape[1], im.shape[0])
pred_dict = get_vis_dict(result_box, result_mask, 'data/demo/' + im_name, CLASSES)
img_width = im.shape[1]
img_height = im.shape[0]
inst_img, cls_img = _convert_pred_to_image(img_width, img_height, pred_dict)
color_map = _get_voc_color_map()
target_cls_file = os.path.join(demo_dir, 'cls_' + im_name)
cls_out_img = np.zeros((img_height, img_width, 3))
for i in xrange(img_height):
for j in xrange(img_width):
cls_out_img[i][j] = color_map[cls_img[i][j]][::-1]
cv2.imwrite(target_cls_file, cls_out_img)
background = Image.open(gt_image)
mask = Image.open(target_cls_file)
background = background.convert('RGBA')
mask = mask.convert('RGBA')
superimpose_image = Image.blend(background, mask, 0.8)
superimpose_name = os.path.join(demo_dir, 'final_' + im_name)
superimpose_image.save(superimpose_name, 'JPEG')