8000 add overlap images for saveImage by jnwnlee · Pull Request #76 · yfeng95/DECA · GitHub
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add overlap images for saveImage #76

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add overlap images for saveImage #76

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4 changes: 3 additions & 1 deletion decalib/datasets/datasets.py
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Original file line number Diff line number Diff line change
Expand Up @@ -139,5 +139,7 @@ def __getitem__(self, index):
return {'image': torch.tensor(dst_image).float(),
'imagename': imagename,
# 'tform': tform,
# 'original_image': torch.tensor(image.transpose(2,0,1)).float(),
'center': center,
'size': size,
'original_image': torch.tensor(image.transpose(2,0,1)).float(),
}
18 changes: 17 additions & 1 deletion decalib/deca.py
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Expand Up @@ -224,12 +224,28 @@ def decode(self, codedict, rendering=True, iddict=None, vis_lmk=True, return_vis
uv_texture_gt = uv_gt[:,:3,:,:]*self.uv_face_eye_mask + (torch.ones_like(uv_gt[:,:3,:,:])*(1-self.uv_face_eye_mask)*0.7)

opdict['uv_texture_gt'] = uv_texture_gt

offsets = [1, -1]
shifted_shapes = []
for offset1 in offsets:
for offset2 in offsets:
shift_offset = torch.zeros_like(verts)
shift_offset[:,:,0] = shift_offset[:,:,0] + offset2
shift_offset[:,:,1] = shift_offset[:,:,1] + offset1
shifted_shapes.append(self.render.render_shape(verts, trans_verts+shift_offset))

shifted_shape_lt, shifted_shape_rt, shifted_shape_lb, shifted_shape_rb = shifted_shapes

visdict = {
'inputs': images,
'landmarks2d': util.tensor_vis_landmarks(images, landmarks2d),
'landmarks3d': util.tensor_vis_landmarks(images, landmarks3d),
'shape_images': shape_images,
'shape_detail_images': shape_detail_images
'shape_detail_images': shape_detail_images,
'shifted_shape_lt': shifted_shape_lt,
'shifted_shape_rt': shifted_shape_rt,
'shifted_shape_lb': shifted_shape_lb,
'shifted_shape_rb': shifted_shape_rb
}
if self.cfg.model.use_tex:
visdict['rendered_images'] = ops['images']
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17 changes: 17 additions & 0 deletions decalib/utils/util.py
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Expand Up @@ -575,6 +575,23 @@ def dict_tensor2npy(tensor_dict):
return npy_dict

# ---------------------------------- visualization
def render_overlap(src_img, trg_img_data):
'''
warp source image to match with target background image
'''
center = trg_img_data['center']
size = trg_img_data['size']
src_pts = np.array([[0,0], [0,src_img.shape[2] - 1],
[src_img.shape[3] - 1, 0], [src_img.shape[3] - 1, src_img.shape[2] - 1]])
dst_pts = np.array([[center[0]-size, center[1]-size], [center[0]-size, center[1]+size],
[center[0]+size, center[1]-size], [center[0]+size, center[1]+size]])

tform = estimate_transform('similarity', src_pts, dst_pts)
dst_image = warp(src_img[0].detach().cpu().numpy().transpose(1,2,0), tform.inverse,
output_shape=(trg_img_data['original_image'].shape[1], trg_img_data['original_image'].shape[2]))
dst_image = torch.tensor(dst_image.transpose(2,0,1)).float()
return dst_image

end_list = np.array([17, 22, 27, 42, 48, 31, 36, 68], dtype = np.int32) - 1
def plot_kpts(image, kpts, color = 'r'):
''' Draw 68 key points
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19 changes: 19 additions & 0 deletions demos/demo_reconstruct.py
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Expand Up @@ -70,6 +70,25 @@ def main(args):
continue
image =util.tensor2image(visdict[vis_name][0])
cv2.imwrite(os.path.join(savefolder, name, name + '_' + vis_name +'.jpg'), util.tensor2image(visdict[vis_name][0]))
# overlap on input (face segmented) image
wfp = os.path.join(savefolder, name, name + '_' + 'overlap' +'.jpg')
alpha = 0.6
deca.render.render_shape(opdict['verts'], opdict['trans_verts'])
res = cv2.addWeighted(util.tensor2image(visdict['inputs'][0]), 1 - alpha, util.tensor2image(visdict['shape_detail_images'][0]), alpha, 0)
cv2.imwrite(wfp, res)
# save original image
cv2.imwrite(os.path.join(savefolder, name, name + '_' + 'original_image' +'.jpg'), util.tensor2image(testdata[i]['original_image']))
# get full shape image
shape_full = torch.cat((torch.cat((visdict['shifted_shape_lt'], visdict['shifted_shape_rt']), 3),
torch.cat((visdict['shifted_shape_lb'], visdict['shifted_shape_rb']), 3)),2)
cv2.imwrite(os.path.join(savefolder, name, name + '_' + 'shape_full' +'.jpg'), util.tensor2image(shape_full[0]))
# overlap full shape image to original image
wfp = os.path.join(savefolder, name, name + '_' + 'original_overlap' +'.jpg')
dst_image = util.render_overlap(shape_full, testdata[i])
#cv2.imwrite(os.path.join(savefolder, name, name + '_' + 'shape_trans' +'.jpg'), util.tensor2image(dst_image)) #.transpose(2,0,1).float()
alpha = 0.6
res = cv2.addWeighted(util.tensor2image(testdata[i]['original_image']), 1 - alpha, util.tensor2image(dst_image), alpha, 0)
cv2.imwrite(wfp, res)
print(f'-- please check the results in {savefolder}')

if __name__ == '__main__':
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