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| import cv2 | |
| import numpy as np | |
| import torch | |
| from torch import nn | |
| from maskrcnn_benchmark.structures.bounding_box import BoxList | |
| from maskrcnn_benchmark.structures.keypoint import PersonKeypoints | |
| class KeypointPostProcessor(nn.Module): | |
| def __init__(self, keypointer=None): | |
| super(KeypointPostProcessor, self).__init__() | |
| self.keypointer = keypointer | |
| def forward(self, x, boxes): | |
| mask_prob = x | |
| scores = None | |
| if self.keypointer: | |
| mask_prob, scores = self.keypointer(x, boxes) | |
| assert len(boxes) == 1, "Only non-batched inference supported for now" | |
| boxes_per_image = [box.bbox.size(0) for box in boxes] | |
| mask_prob = mask_prob.split(boxes_per_image, dim=0) | |
| scores = scores.split(boxes_per_image, dim=0) | |
| results = [] | |
| for prob, box, score in zip(mask_prob, boxes, scores): | |
| bbox = BoxList(box.bbox, box.size, mode="xyxy") | |
| for field in box.fields(): | |
| bbox.add_field(field, box.get_field(field)) | |
| prob = PersonKeypoints(prob, box.size) | |
| prob.add_field("logits", score) | |
| bbox.add_field("keypoints", prob) | |
| results.append(bbox) | |
| return results | |
| def heatmaps_to_keypoints(maps, rois): | |
| """Extract predicted keypoint locations from heatmaps. Output has shape | |
| (#rois, 4, #keypoints) with the 4 rows corresponding to (x, y, logit, prob) | |
| for each keypoint. | |
| """ | |
| # This function converts a discrete image coordinate in a HEATMAP_SIZE x | |
| # HEATMAP_SIZE image to a continuous keypoint coordinate. We maintain | |
| # consistency with keypoints_to_heatmap_labels by using the conversion from | |
| # Heckbert 1990: c = d + 0.5, where d is a discrete coordinate and c is a | |
| # continuous coordinate. | |
| offset_x = rois[:, 0] | |
| offset_y = rois[:, 1] | |
| widths = rois[:, 2] - rois[:, 0] | |
| heights = rois[:, 3] - rois[:, 1] | |
| widths = np.maximum(widths, 1) | |
| heights = np.maximum(heights, 1) | |
| widths_ceil = np.ceil(widths) | |
| heights_ceil = np.ceil(heights) | |
| # NCHW to NHWC for use with OpenCV | |
| maps = np.transpose(maps, [0, 2, 3, 1]) | |
| min_size = 0 # cfg.KRCNN.INFERENCE_MIN_SIZE | |
| num_keypoints = maps.shape[3] | |
| xy_preds = np.zeros((len(rois), 3, num_keypoints), dtype=np.float32) | |
| end_scores = np.zeros((len(rois), num_keypoints), dtype=np.float32) | |
| for i in range(len(rois)): | |
| if min_size > 0: | |
| roi_map_width = int(np.maximum(widths_ceil[i], min_size)) | |
| roi_map_height = int(np.maximum(heights_ceil[i], min_size)) | |
| else: | |
| roi_map_width = widths_ceil[i] | |
| roi_map_height = heights_ceil[i] | |
| width_correction = widths[i] / roi_map_width | |
| height_correction = heights[i] / roi_map_height | |
| roi_map = cv2.resize( | |
| maps[i], (roi_map_width, roi_map_height), interpolation=cv2.INTER_CUBIC | |
| ) | |
| # Bring back to CHW | |
| roi_map = np.transpose(roi_map, [2, 0, 1]) | |
| # roi_map_probs = scores_to_probs(roi_map.copy()) | |
| w = roi_map.shape[2] | |
| pos = roi_map.reshape(num_keypoints, -1).argmax(axis=1) | |
| x_int = pos % w | |
| y_int = (pos - x_int) // w | |
| # assert (roi_map_probs[k, y_int, x_int] == | |
| # roi_map_probs[k, :, :].max()) | |
| x = (x_int + 0.5) * width_correction | |
| y = (y_int + 0.5) * height_correction | |
| xy_preds[i, 0, :] = x + offset_x[i] | |
| xy_preds[i, 1, :] = y + offset_y[i] | |
| xy_preds[i, 2, :] = 1 | |
| end_scores[i, :] = roi_map[np.arange(num_keypoints), y_int, x_int] | |
| return np.transpose(xy_preds, [0, 2, 1]), end_scores | |
| class Keypointer(object): | |
| """ | |
| Projects a set of masks in an image on the locations | |
| specified by the bounding boxes | |
| """ | |
| def __init__(self, padding=0): | |
| self.padding = padding | |
| def __call__(self, masks, boxes): | |
| # TODO do this properly | |
| if isinstance(boxes, BoxList): | |
| boxes = [boxes] | |
| assert len(boxes) == 1 | |
| result, scores = heatmaps_to_keypoints( | |
| masks.detach().cpu().numpy(), boxes[0].bbox.cpu().numpy() | |
| ) | |
| return torch.from_numpy(result).to(masks.device), torch.as_tensor(scores, device=masks.device) | |
| def make_roi_keypoint_post_processor(cfg): | |
| keypointer = Keypointer() | |
| keypoint_post_processor = KeypointPostProcessor(keypointer) | |
| return keypoint_post_processor |