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# https://github.com/IDEA-Research/DWPose
import cv2
import numpy as np
import onnxruntime
def nms(boxes, scores, nms_thr):
"""Single class NMS implemented in Numpy."""
x1 = boxes[:, 0]
y1 = boxes[:, 1]
x2 = boxes[:, 2]
y2 = boxes[:, 3]
areas = (x2 - x1 + 1) * (y2 - y1 + 1)
order = scores.argsort()[::-1]
keep = []
while order.size > 0:
i = order[0]
keep.append(i)
xx1 = np.maximum(x1[i], x1[order[1:]])
yy1 = np.maximum(y1[i], y1[order[1:]])
xx2 = np.minimum(x2[i], x2[order[1:]])
yy2 = np.minimum(y2[i], y2[order[1:]])
w = np.maximum(0.0, xx2 - xx1 + 1)
h = np.maximum(0.0, yy2 - yy1 + 1)
inter = w * h
ovr = inter / (areas[i] + areas[order[1:]] - inter)
inds = np.where(ovr <= nms_thr)[0]
order = order[inds + 1]
return keep
def multiclass_nms(boxes, scores, nms_thr, score_thr):
"""Multiclass NMS implemented in Numpy. Class-aware version."""
final_dets = []
num_classes = scores.shape[1]
for cls_ind in range(num_classes):
cls_scores = scores[:, cls_ind]
valid_score_mask = cls_scores > score_thr
if valid_score_mask.sum() == 0:
continue
else:
valid_scores = cls_scores[valid_score_mask]
valid_boxes = boxes[valid_score_mask]
keep = nms(valid_boxes, valid_scores, nms_thr)
if len(keep) > 0:
cls_inds = np.ones((len(keep), 1)) * cls_ind
dets = np.concatenate(
[valid_boxes[keep], valid_scores[keep, None], cls_inds], 1
)
final_dets.append(dets)
if len(final_dets) == 0:
return None
return np.concatenate(final_dets, 0)
def demo_postprocess(outputs, img_size, p6=False):
grids = []
expanded_strides = []
strides = [8, 16, 32] if not p6 else [8, 16, 32, 64]
hsizes = [img_size[0] // stride for stride in strides]
wsizes = [img_size[1] // stride for stride in strides]
for hsize, wsize, stride in zip(hsizes, wsizes, strides):
xv, yv = np.meshgrid(np.arange(wsize), np.arange(hsize))
grid = np.stack((xv, yv), 2).reshape(1, -1, 2)
grids.append(grid)
shape = grid.shape[:2]
expanded_strides.append(np.full((*shape, 1), stride))
grids = np.concatenate(grids, 1)
expanded_strides = np.concatenate(expanded_strides, 1)
outputs[..., :2] = (outputs[..., :2] + grids) * expanded_strides
outputs[..., 2:4] = np.exp(outputs[..., 2:4]) * expanded_strides
return outputs
def preprocess(img, input_size, swap=(2, 0, 1)):
if len(img.shape) == 3:
padded_img = np.ones((input_size[0], input_size[1], 3), dtype=np.uint8) * 114
else:
padded_img = np.ones(input_size, dtype=np.uint8) * 114
r = min(input_size[0] / img.shape[0], input_size[1] / img.shape[1])
resized_img = cv2.resize(
img,
(int(img.shape[1] * r), int(img.shape[0] * r)),
interpolation=cv2.INTER_LINEAR,
).astype(np.uint8)
padded_img[: int(img.shape[0] * r), : int(img.shape[1] * r)] = resized_img
padded_img = padded_img.transpose(swap)
padded_img = np.ascontiguousarray(padded_img, dtype=np.float32)
return padded_img, r
def inference_detector(session, oriImg):
input_shape = (640, 640)
img, ratio = preprocess(oriImg, input_shape)
ort_inputs = {session.get_inputs()[0].name: img[None, :, :, :]}
output = session.run(None, ort_inputs)
predictions = demo_postprocess(output[0], input_shape)[0]
boxes = predictions[:, :4]
scores = predictions[:, 4:5] * predictions[:, 5:]
boxes_xyxy = np.ones_like(boxes)
boxes_xyxy[:, 0] = boxes[:, 0] - boxes[:, 2] / 2.0
boxes_xyxy[:, 1] = boxes[:, 1] - boxes[:, 3] / 2.0
boxes_xyxy[:, 2] = boxes[:, 0] + boxes[:, 2] / 2.0
boxes_xyxy[:, 3] = boxes[:, 1] + boxes[:, 3] / 2.0
boxes_xyxy /= ratio
dets = multiclass_nms(boxes_xyxy, scores, nms_thr=0.45, score_thr=0.1)
if dets is not None:
final_boxes, final_scores, final_cls_inds = dets[:, :4], dets[:, 4], dets[:, 5]
isscore = final_scores > 0.3
iscat = final_cls_inds == 0
isbbox = [i and j for (i, j) in zip(isscore, iscat)]
final_boxes = final_boxes[isbbox]
else:
return []
return final_boxes
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