(scene_images, marker, estimator, source=None, blend_type='D', warp='grid_sample', use_colormap=True)
| 110 | return blends, masks, blends_colormap |
| 111 | |
| 112 | def blend_life(scene_images, marker, estimator, source=None, blend_type='D', warp='grid_sample', use_colormap=True): |
| 113 | print('blend with our model') |
| 114 | blends = [] |
| 115 | masks = [] |
| 116 | blends_colormap = [] |
| 117 | for id, scene in tqdm(enumerate(scene_images), total=len(scene_images)): |
| 118 | marker = cv2.resize(marker, (scene.shape[1], scene.shape[0])) |
| 119 | if blend_type == "mix": |
| 120 | source_id = 3 * int(id / 3) |
| 121 | source = scene_images[source_id] |
| 122 | |
| 123 | flow = estimator.estimate(scene, marker) |
| 124 | |
| 125 | if use_colormap: |
| 126 | colormap = get_colormap(flow, scene.shape[0], scene.shape[1]) |
| 127 | |
| 128 | mask = None |
| 129 | if warp == 'grid_sample': |
| 130 | out = image_flow_warp(marker, flow[0].permute([1,2,0])) |
| 131 | mask_origin = np.ones(shape=(marker.shape[0], marker.shape[1], 1)).astype(np.float64) |
| 132 | mask_origin = image_flow_warp(mask_origin, flow[0].permute([1,2,0]),padding_mode='zeros') |
| 133 | mask = (1 - mask_origin) |
| 134 | if use_colormap: |
| 135 | out_colormap = image_flow_warp(colormap, flow[0].permute([1,2,0])) |
| 136 | out_colormap = ((out_colormap + 256) / 2) |
| 137 | mask_colormap = np.ones_like(colormap).astype(np.float32) / 2 |
| 138 | mask_colormap = image_flow_warp(mask_colormap, flow[0].permute([1,2,0])) |
| 139 | mask_colormap = (1 - mask_colormap) |
| 140 | |
| 141 | elif warp == 'homography': |
| 142 | # RANSAC homography |
| 143 | flow = flow[0].permute([1,2,0]) |
| 144 | image = marker |
| 145 | image = torch.from_numpy(image) |
| 146 | if image.ndim == 2: |
| 147 | image = image[None].permute([1,2,0]) |
| 148 | H, W, _ = image.shape |
| 149 | coords = coords_grid(1, H, W).cuda().float().contiguous() |
| 150 | flow = flow[None].repeat(1, 1, 1, 1).permute([0, 3, 1, 2]).float().contiguous() |
| 151 | grid = (flow + coords).permute([0, 2, 3, 1]).contiguous() # (1, H, W, 2) |
| 152 | grid = grid[0].cpu() |
| 153 | src_pts = [] |
| 154 | dst_pts = [] |
| 155 | for y in range(H): |
| 156 | for x in range(W): |
| 157 | if grid[y,x,0]>=0 and grid[y,x,0]<W and grid[y,x,1]>=0 and grid[y,x,1]<H: |
| 158 | src_pts.append((grid[y,x,0], grid[y,x,1])) |
| 159 | dst_pts.append((x, y)) |
| 160 | src_pts = np.float32(src_pts) |
| 161 | dst_pts = np.float32(dst_pts) |
| 162 | |
| 163 | M, _ = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0) |
| 164 | if M is None: |
| 165 | blends.append(None) |
| 166 | continue |
| 167 | out = cv2.warpPerspective(marker, M, (scene.shape[1], scene.shape[0])) |
| 168 | |
| 169 | blend_i, mask_i = blend(out, source, scene, blend_type, mask=mask) |
no test coverage detected