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更换文档检测模型

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liuyebo
2024-08-27 14:42:45 +08:00
parent aea6f19951
commit 1514e09c40
2072 changed files with 254336 additions and 4967 deletions
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paddle_detection/deploy/serving/python/postprocess_ops.py Normal file
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import cv2
import math
import numpy as np
from preprocess_ops import get_affine_transform
class HRNetPostProcess(object):
def __init__(self, use_dark=True):
self.use_dark = use_dark
def flip_back(self, output_flipped, matched_parts):
assert output_flipped.ndim == 4,\
'output_flipped should be [batch_size, num_joints, height, width]'
output_flipped = output_flipped[:, :, :, ::-1]
for pair in matched_parts:
tmp = output_flipped[:, pair[0], :, :].copy()
output_flipped[:, pair[0], :, :] = output_flipped[:, pair[1], :, :]
output_flipped[:, pair[1], :, :] = tmp
return output_flipped
def get_max_preds(self, heatmaps):
"""get predictions from score maps
Args:
heatmaps: numpy.ndarray([batch_size, num_joints, height, width])
Returns:
preds: numpy.ndarray([batch_size, num_joints, 2]), keypoints coords
maxvals: numpy.ndarray([batch_size, num_joints, 2]), the maximum confidence of the keypoints
"""
assert isinstance(heatmaps,
np.ndarray), 'heatmaps should be numpy.ndarray'
assert heatmaps.ndim == 4, 'batch_images should be 4-ndim'
batch_size = heatmaps.shape[0]
num_joints = heatmaps.shape[1]
width = heatmaps.shape[3]
heatmaps_reshaped = heatmaps.reshape((batch_size, num_joints, -1))
idx = np.argmax(heatmaps_reshaped, 2)
maxvals = np.amax(heatmaps_reshaped, 2)
maxvals = maxvals.reshape((batch_size, num_joints, 1))
idx = idx.reshape((batch_size, num_joints, 1))
preds = np.tile(idx, (1, 1, 2)).astype(np.float32)
preds[:, :, 0] = (preds[:, :, 0]) % width
preds[:, :, 1] = np.floor((preds[:, :, 1]) / width)
pred_mask = np.tile(np.greater(maxvals, 0.0), (1, 1, 2))
pred_mask = pred_mask.astype(np.float32)
preds *= pred_mask
return preds, maxvals
def gaussian_blur(self, heatmap, kernel):
border = (kernel - 1) // 2
batch_size = heatmap.shape[0]
num_joints = heatmap.shape[1]
height = heatmap.shape[2]
width = heatmap.shape[3]
for i in range(batch_size):
for j in range(num_joints):
origin_max = np.max(heatmap[i, j])
dr = np.zeros((height + 2 * border, width + 2 * border))
dr[border:-border, border:-border] = heatmap[i, j].copy()
dr = cv2.GaussianBlur(dr, (kernel, kernel), 0)
heatmap[i, j] = dr[border:-border, border:-border].copy()
heatmap[i, j] *= origin_max / np.max(heatmap[i, j])
return heatmap
def dark_parse(self, hm, coord):
heatmap_height = hm.shape[0]
heatmap_width = hm.shape[1]
px = int(coord[0])
py = int(coord[1])
if 1 < px < heatmap_width - 2 and 1 < py < heatmap_height - 2:
dx = 0.5 * (hm[py][px + 1] - hm[py][px - 1])
dy = 0.5 * (hm[py + 1][px] - hm[py - 1][px])
dxx = 0.25 * (hm[py][px + 2] - 2 * hm[py][px] + hm[py][px - 2])
dxy = 0.25 * (hm[py+1][px+1] - hm[py-1][px+1] - hm[py+1][px-1] \
+ hm[py-1][px-1])
dyy = 0.25 * (
hm[py + 2 * 1][px] - 2 * hm[py][px] + hm[py - 2 * 1][px])
derivative = np.matrix([[dx], [dy]])
hessian = np.matrix([[dxx, dxy], [dxy, dyy]])
if dxx * dyy - dxy**2 != 0:
hessianinv = hessian.I
offset = -hessianinv * derivative
offset = np.squeeze(np.array(offset.T), axis=0)
coord += offset
return coord
def dark_postprocess(self, hm, coords, kernelsize):
"""
refer to https://github.com/ilovepose/DarkPose/lib/core/inference.py
"""
hm = self.gaussian_blur(hm, kernelsize)
hm = np.maximum(hm, 1e-10)
hm = np.log(hm)
for n in range(coords.shape[0]):
for p in range(coords.shape[1]):
coords[n, p] = self.dark_parse(hm[n][p], coords[n][p])
return coords
def get_final_preds(self, heatmaps, center, scale, kernelsize=3):
"""the highest heatvalue location with a quarter offset in the
direction from the highest response to the second highest response.
Args:
heatmaps (numpy.ndarray): The predicted heatmaps
center (numpy.ndarray): The boxes center
scale (numpy.ndarray): The scale factor
Returns:
preds: numpy.ndarray([batch_size, num_joints, 2]), keypoints coords
maxvals: numpy.ndarray([batch_size, num_joints, 1]), the maximum confidence of the keypoints
"""
coords, maxvals = self.get_max_preds(heatmaps)
heatmap_height = heatmaps.shape[2]
heatmap_width = heatmaps.shape[3]
if self.use_dark:
coords = self.dark_postprocess(heatmaps, coords, kernelsize)
else:
for n in range(coords.shape[0]):
for p in range(coords.shape[1]):
hm = heatmaps[n][p]
px = int(math.floor(coords[n][p][0] + 0.5))
py = int(math.floor(coords[n][p][1] + 0.5))
if 1 < px < heatmap_width - 1 and 1 < py < heatmap_height - 1:
diff = np.array([
hm[py][px + 1] - hm[py][px - 1],
hm[py + 1][px] - hm[py - 1][px]
])
coords[n][p] += np.sign(diff) * .25
preds = coords.copy()
# Transform back
for i in range(coords.shape[0]):
preds[i] = transform_preds(coords[i], center[i], scale[i],
[heatmap_width, heatmap_height])
return preds, maxvals
def __call__(self, output, center, scale):
preds, maxvals = self.get_final_preds(output, center, scale)
return np.concatenate(
(preds, maxvals), axis=-1), np.mean(
maxvals, axis=1)
def transform_preds(coords, center, scale, output_size):
target_coords = np.zeros(coords.shape)
trans = get_affine_transform(center, scale * 200, 0, output_size, inv=1)
for p in range(coords.shape[0]):
target_coords[p, 0:2] = affine_transform(coords[p, 0:2], trans)
return target_coords
def affine_transform(pt, t):
new_pt = np.array([pt[0], pt[1], 1.]).T
new_pt = np.dot(t, new_pt)
return new_pt[:2]