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import cv2
import numpy as np
import torch
from dataset.range_transform import inv_im_trans
from collections import defaultdict
def tensor_to_numpy(image):
image_np = (image.numpy() * 255).astype('uint8')
return image_np
def tensor_to_np_float(image):
image_np = image.numpy().astype('float32')
return image_np
def detach_to_cpu(x):
return x.detach().cpu()
def transpose_np(x):
return np.transpose(x, [1,2,0])
def tensor_to_gray_im(x):
x = detach_to_cpu(x)
x = tensor_to_numpy(x)
x = transpose_np(x)
return x
def tensor_to_im(x):
x = detach_to_cpu(x)
x = inv_im_trans(x).clamp(0, 1)
x = tensor_to_numpy(x)
x = transpose_np(x)
return x
# Predefined key <-> caption dict
key_captions = {
'im': 'Image',
'gt': 'GT',
}
"""
Return an image array with captions
keys in dictionary will be used as caption if not provided
values should contain lists of cv2 images
"""
def get_image_array(images, grid_shape, captions={}):
h, w = grid_shape
cate_counts = len(images)
rows_counts = len(next(iter(images.values())))
font = cv2.FONT_HERSHEY_SIMPLEX
output_image = np.zeros([w*cate_counts, h*(rows_counts+1), 3], dtype=np.uint8)
col_cnt = 0
for k, v in images.items():
# Default as key value itself
caption = captions.get(k, k)
# Handles new line character
dy = 40
for i, line in enumerate(caption.split('\n')):
cv2.putText(output_image, line, (10, col_cnt*w+100+i*dy),
font, 0.8, (255,255,255), 2, cv2.LINE_AA)
# Put images
for row_cnt, img in enumerate(v):
im_shape = img.shape
if len(im_shape) == 2:
img = img[..., np.newaxis]
img = (img * 255).astype('uint8')
output_image[(col_cnt+0)*w:(col_cnt+1)*w,
(row_cnt+1)*h:(row_cnt+2)*h, :] = img
col_cnt += 1
return output_image
def base_transform(im, size):
im = tensor_to_np_float(im)
if len(im.shape) == 3:
im = im.transpose((1, 2, 0))
else:
im = im[:, :, None]
# Resize
if im.shape[1] != size:
im = cv2.resize(im, size, interpolation=cv2.INTER_NEAREST)
return im.clip(0, 1)
def im_transform(im, size):
return base_transform(inv_im_trans(detach_to_cpu(im)), size=size)
def mask_transform(mask, size):
return base_transform(detach_to_cpu(mask), size=size)
def out_transform(mask, size):
return base_transform(detach_to_cpu(torch.sigmoid(mask)), size=size)
def pool_pairs(images, size, num_objects):
req_images = defaultdict(list)
b, t = images['rgb'].shape[:2]
# limit the number of images saved
b = min(2, b)
# find max num objects
max_num_objects = max(num_objects[:b])
GT_suffix = ''
for bi in range(b):
GT_suffix += ' \n%s' % images['info']['name'][bi][-25:-4]
for bi in range(b):
for ti in range(t):
req_images['RGB'].append(im_transform(images['rgb'][bi,ti], size))
for oi in range(max_num_objects):
if ti == 0 or oi >= num_objects[bi]:
req_images['Mask_%d'%oi].append(mask_transform(images['first_frame_gt'][bi][0,oi], size))
# req_images['Mask_X8_%d'%oi].append(mask_transform(images['first_frame_gt'][bi][0,oi], size))
# req_images['Mask_X16_%d'%oi].append(mask_transform(images['first_frame_gt'][bi][0,oi], size))
else:
req_images['Mask_%d'%oi].append(mask_transform(images['masks_%d'%ti][bi][oi], size))
# req_images['Mask_%d'%oi].append(mask_transform(images['masks_%d'%ti][bi][oi][2], size))
# req_images['Mask_X8_%d'%oi].append(mask_transform(images['masks_%d'%ti][bi][oi][1], size))
# req_images['Mask_X16_%d'%oi].append(mask_transform(images['masks_%d'%ti][bi][oi][0], size))
req_images['GT_%d_%s'%(oi, GT_suffix)].append(mask_transform(images['cls_gt'][bi,ti,0]==(oi+1), size))
# print((images['cls_gt'][bi,ti,0]==(oi+1)).shape)
# print(mask_transform(images['cls_gt'][bi,ti,0]==(oi+1), size).shape)
return get_image_array(req_images, size, key_captions) |