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torzdf
165 lines
5.7 KiB
Python
165 lines
5.7 KiB
Python
#!/usr/bin/env python3
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"""DARK heatmap decoding for heatmap based aligners."""
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import logging
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import cv2
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import numpy as np
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from lib.logger import parse_class_init
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from lib.utils import get_module_objects
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logger = logging.getLogger(__name__)
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class Dark:
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"""Dark heatmap decoding
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https://github.com/ilovepose/DarkPose
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Parameters
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----------
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num_points
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The number of landmarks output from the model
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size
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The size of the heatmap
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"""
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def __init__(self, num_points: int, size: int, blur_kernel: int = 11):
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logger.debug(parse_class_init(locals()))
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self._num_points = num_points
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self._size = size
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self._blur_kernel = blur_kernel
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self._border = (blur_kernel - 1) // 2
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def get_max_preds(self, batch_heatmaps: np.ndarray) -> np.ndarray:
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""" get predictions from score maps
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Parameters
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----------
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heatmaps
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Heatmap to derive points from ([batch_size, num_joints, height, width])
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Returns
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-------
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coords
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The derived points from the heatmaps (B, N, 2)
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"""
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assert isinstance(batch_heatmaps, np.ndarray), "batch_heatmaps should be numpy.ndarray"
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assert batch_heatmaps.ndim == 4, "batch_images should be 4-ndim"
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batch = batch_heatmaps.shape[0]
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heatmaps_reshaped = batch_heatmaps.reshape((batch, self._num_points, -1))
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idx = np.argmax(heatmaps_reshaped, 2)
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max_vals = np.amax(heatmaps_reshaped, 2)
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max_vals = max_vals.reshape((batch, self._num_points, 1))
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idx = idx.reshape((batch, self._num_points, 1))
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preds = np.zeros((batch, self._num_points, 2), dtype=np.float32)
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preds[:, :, 0] = idx[..., 0] % self._size
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preds[:, :, 1] = idx[..., 0] // self._size
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pred_mask = np.repeat(np.greater(max_vals, 0.0), 2, axis=2)
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pred_mask = pred_mask.astype(np.float32)
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preds *= pred_mask
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return preds
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def gaussian_blur(self, heatmap: np.ndarray) -> np.ndarray:
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"""Perform gaussian blurring on the heatmaps
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Parameters
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----------
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heatmap
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Batch of heatmaps to blur (N, points, size, size)
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Returns
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-------
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The blurred heatmaps
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"""
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batch_size = heatmap.shape[0]
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origin_max = heatmap.reshape(batch_size, self._num_points, -1).max(axis=2)
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padded = np.pad(heatmap,
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((0, 0),
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(0, 0),
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(self._border, self._border),
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(self._border, self._border)),
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mode="constant")
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reshaped = padded.reshape( # pylint:disable=too-many-function-args)
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batch_size * self._num_points,
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self._size + 2 * self._border,
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self._size + 2 * self._border
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)
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blurred = np.stack([cv2.GaussianBlur(img, (self._blur_kernel, self._blur_kernel), 0)
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for img in reshaped])
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blurred = blurred.reshape(batch_size,
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self._num_points,
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self._size + 2 * self._border,
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self._size + 2 * self._border)
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cropped = blurred[:, :, self._border:-self._border, self._border:-self._border]
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new_max = cropped.reshape(batch_size, self._num_points, -1).max(axis=2)
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scale = origin_max / (new_max + 1e-8) # avoid division by zero
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scale = scale[:, :, None, None]
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return cropped * scale
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def taylor(self, heatmap: np.ndarray, coords: np.ndarray # pylint:disable=too-many-locals
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) -> np.ndarray:
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"""Sub-pixel refine the predictions
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Parameters
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----------
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heatmap
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The processed heatmaps for refinement
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coords
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The coordinates to be refined
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Returns
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-------
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The refined coordinates
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"""
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batch = heatmap.shape[0]
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px = np.clip(coords[..., 0], 2, self._size - 3).astype(np.int32)
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py = np.clip(coords[..., 1], 2, self._size - 3).astype(np.int32)
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flat_idx = np.arange(batch * self._num_points)
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hm = heatmap.reshape(batch * self._num_points, self._size, self._size)
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px_f = px.reshape(-1)
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py_f = py.reshape(-1)
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dx = 0.5 * (hm[flat_idx, py_f, px_f + 1] - hm[flat_idx, py_f, px_f - 1])
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dy = 0.5 * (hm[flat_idx, py_f + 1, px_f] - hm[flat_idx, py_f - 1, px_f])
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dxx = 0.25 * (hm[flat_idx, py_f, px_f + 2] - 2 *
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hm[flat_idx, py_f, px_f] + hm[flat_idx, py_f, px_f - 2])
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dyy = 0.25 * (hm[flat_idx, py_f + 2, px_f] - 2 *
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hm[flat_idx, py_f, px_f] + hm[flat_idx, py_f - 2, px_f])
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dxy = 0.25 * (hm[flat_idx, py_f + 1, px_f + 1] - hm[flat_idx, py_f - 1, px_f + 1] -
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hm[flat_idx, py_f + 1, px_f - 1] + hm[flat_idx, py_f - 1, px_f - 1])
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dx = dx.reshape(batch, self._num_points)
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dy = dy.reshape(batch, self._num_points)
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dxx = dxx.reshape(batch, self._num_points)
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dyy = dyy.reshape(batch, self._num_points)
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dxy = dxy.reshape(batch, self._num_points)
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det = dxx * dyy - dxy ** 2
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inv_det = 1.0 / (det + 1e-8)
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offset_x = -inv_det * (dyy * dx - dxy * dy)
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offset_y = -inv_det * (-dxy * dx + dxx * dy)
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coords[..., 0] += offset_x
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coords[..., 1] += offset_y
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return coords
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def __call__(self, heatmap: np.ndarray):
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coords = self.get_max_preds(heatmap)
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# post-processing
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heatmap = self.gaussian_blur(heatmap)
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heatmap = np.maximum(heatmap, 1e-10)
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heatmap = np.log(heatmap)
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coords = self.taylor(heatmap, coords)
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return coords
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get_module_objects(__name__)
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