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mp_persondet.py
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import numpy as np
import cv2 as cv
class MPPersonDet:
def __init__(self, modelPath, nmsThreshold=0.3, scoreThreshold=0.5, topK=5000, backendId=0, targetId=0):
self.model_path = modelPath
self.nms_threshold = nmsThreshold
self.score_threshold = scoreThreshold
self.topK = topK
self.backend_id = backendId
self.target_id = targetId
self.input_size = np.array([224, 224]) # wh
self.model = cv.dnn.readNet(self.model_path)
self.model.setPreferableBackend(self.backend_id)
self.model.setPreferableTarget(self.target_id)
self.anchors = self._load_anchors()
@property
def name(self):
return self.__class__.__name__
def setBackendAndTarget(self, backendId, targetId):
self.backend_id = backendId
self.target_id = targetId
self.model.setPreferableBackend(self.backend_id)
self.model.setPreferableTarget(self.target_id)
def _preprocess(self, image):
pad_bias = np.array([0., 0.]) # left, top
image = cv.cvtColor(image, cv.COLOR_BGR2RGB)
image = image.astype(np.float32) / 255.0 # norm
image = (image - 0.5) * 2 # [0, 1] -> [-1, 1]
ratio = min(self.input_size / image.shape[:2])
if image.shape[0] != self.input_size[0] or image.shape[1] != self.input_size[1]:
# keep aspect ratio when resize
ratio_size = (np.array(image.shape[:2]) * ratio).astype(np.int32)
image = cv.resize(image, (ratio_size[1], ratio_size[0]))
pad_h = self.input_size[0] - ratio_size[0]
pad_w = self.input_size[1] - ratio_size[1]
pad_bias[0] = left = pad_w // 2
pad_bias[1] = top = pad_h // 2
right = pad_w - left
bottom = pad_h - top
image = cv.copyMakeBorder(image, top, bottom, left, right, cv.BORDER_CONSTANT, None, (0, 0, 0))
blob = np.transpose(image, [2, 0, 1])
pad_bias = (pad_bias / ratio).astype(np.int32)
return blob[np.newaxis, :, :, :], pad_bias # chw -> nchw
def infer(self, image):
h, w, _ = image.shape
# Preprocess
input_blob, pad_bias = self._preprocess(image)
# Forward
self.model.setInput(input_blob)
output_blob = self.model.forward(self.model.getUnconnectedOutLayersNames())
# Postprocess
results = self._postprocess(output_blob, np.array([w, h]), pad_bias)
return results
def _postprocess(self, output_blob, original_shape, pad_bias):
score = output_blob[1][0, :, 0]
box_delta = output_blob[0][0, :, 0:4]
landmark_delta = output_blob[0][0, :, 4:]
scale = max(original_shape)
# get scores
score = score.astype(np.float64)
score = np.clip(score, -100, 100)
score = 1 / (1 + np.exp(-score))
# get boxes
cxy_delta = box_delta[:, :2] / self.input_size
wh_delta = box_delta[:, 2:] / self.input_size
xy1 = (cxy_delta - wh_delta / 2 + self.anchors) * scale
xy2 = (cxy_delta + wh_delta / 2 + self.anchors) * scale
boxes = np.concatenate([xy1, xy2], axis=1)
boxes -= [pad_bias[0], pad_bias[1], pad_bias[0], pad_bias[1]]
# NMS
keep_idx = cv.dnn.NMSBoxes(boxes, score, self.score_threshold, self.nms_threshold, top_k=self.topK)
if len(keep_idx) == 0:
return np.empty(shape=(0, 13))
selected_score = score[keep_idx]
selected_box = boxes[keep_idx]
# get landmarks
selected_landmarks = landmark_delta[keep_idx].reshape(-1, 4, 2)
selected_landmarks = selected_landmarks / self.input_size
selected_anchors = self.anchors[keep_idx]
for idx, landmark in enumerate(selected_landmarks):
landmark += selected_anchors[idx]
selected_landmarks *= scale
selected_landmarks -= pad_bias
# TODO: still don't know the meaning of face bbox
# each landmark: hip center point; full body point; shoulder center point; upper body point;
#
# [
# [face_bbox, landmarks, score]
# ...
# [face_bbox, landmarks, score]
# ]
return np.c_[selected_box.reshape(-1, 4), selected_landmarks.reshape(-1, 8), selected_score.reshape(-1, 1)]
def _load_anchors(self):
return np.array([[0.017857142857142856, 0.017857142857142856],
[0.017857142857142856, 0.017857142857142856],
[0.05357142857142857, 0.017857142857142856],
[0.05357142857142857, 0.017857142857142856],
[0.08928571428571429, 0.017857142857142856],
[0.08928571428571429, 0.017857142857142856],
[0.125, 0.017857142857142856],
[0.125, 0.017857142857142856],
[0.16071428571428573, 0.017857142857142856],
[0.16071428571428573, 0.017857142857142856],
[0.19642857142857142, 0.017857142857142856],
[0.19642857142857142, 0.017857142857142856],
[0.23214285714285715, 0.017857142857142856],
[0.23214285714285715, 0.017857142857142856],
[0.26785714285714285, 0.017857142857142856],
[0.26785714285714285, 0.017857142857142856],
[0.30357142857142855, 0.017857142857142856],
[0.30357142857142855, 0.017857142857142856],
[0.3392857142857143, 0.017857142857142856],
[0.3392857142857143, 0.017857142857142856],
[0.375, 0.017857142857142856],
[0.375, 0.017857142857142856],
[0.4107142857142857, 0.017857142857142856],
[0.4107142857142857, 0.017857142857142856],
[0.44642857142857145, 0.017857142857142856],
[0.44642857142857145, 0.017857142857142856],
[0.48214285714285715, 0.017857142857142856],
[0.48214285714285715, 0.017857142857142856],
[0.5178571428571429, 0.017857142857142856],
[0.5178571428571429, 0.017857142857142856],
[0.5535714285714286, 0.017857142857142856],
[0.5535714285714286, 0.017857142857142856],
[0.5892857142857143, 0.017857142857142856],
[0.5892857142857143, 0.017857142857142856],
[0.625, 0.017857142857142856],
[0.625, 0.017857142857142856],
[0.6607142857142857, 0.017857142857142856],
[0.6607142857142857, 0.017857142857142856],
[0.6964285714285714, 0.017857142857142856],
[0.6964285714285714, 0.017857142857142856],
[0.7321428571428571, 0.017857142857142856],
[0.7321428571428571, 0.017857142857142856],
[0.7678571428571429, 0.017857142857142856],
[0.7678571428571429, 0.017857142857142856],
[0.8035714285714286, 0.017857142857142856],
[0.8035714285714286, 0.017857142857142856],
[0.8392857142857143, 0.017857142857142856],
[0.8392857142857143, 0.017857142857142856],
[0.875, 0.017857142857142856],
[0.875, 0.017857142857142856],
[0.9107142857142857, 0.017857142857142856],
[0.9107142857142857, 0.017857142857142856],
[0.9464285714285714, 0.017857142857142856],
[0.9464285714285714, 0.017857142857142856],
[0.9821428571428571, 0.017857142857142856],
[0.9821428571428571, 0.017857142857142856],
[0.017857142857142856, 0.05357142857142857],
[0.017857142857142856, 0.05357142857142857],
[0.05357142857142857, 0.05357142857142857],
[0.05357142857142857, 0.05357142857142857],
[0.08928571428571429, 0.05357142857142857],
[0.08928571428571429, 0.05357142857142857],
[0.125, 0.05357142857142857],
[0.125, 0.05357142857142857],
[0.16071428571428573, 0.05357142857142857],
[0.16071428571428573, 0.05357142857142857],
[0.19642857142857142, 0.05357142857142857],
[0.19642857142857142, 0.05357142857142857],
[0.23214285714285715, 0.05357142857142857],
[0.23214285714285715, 0.05357142857142857],
[0.26785714285714285, 0.05357142857142857],
[0.26785714285714285, 0.05357142857142857],
[0.30357142857142855, 0.05357142857142857],
[0.30357142857142855, 0.05357142857142857],
[0.3392857142857143, 0.05357142857142857],
[0.3392857142857143, 0.05357142857142857],
[0.375, 0.05357142857142857],
[0.375, 0.05357142857142857],
[0.4107142857142857, 0.05357142857142857],
[0.4107142857142857, 0.05357142857142857],
[0.44642857142857145, 0.05357142857142857],
[0.44642857142857145, 0.05357142857142857],
[0.48214285714285715, 0.05357142857142857],
[0.48214285714285715, 0.05357142857142857],
[0.5178571428571429, 0.05357142857142857],
[0.5178571428571429, 0.05357142857142857],
[0.5535714285714286, 0.05357142857142857],
[0.5535714285714286, 0.05357142857142857],
[0.5892857142857143, 0.05357142857142857],
[0.5892857142857143, 0.05357142857142857],
[0.625, 0.05357142857142857],
[0.625, 0.05357142857142857],
[0.6607142857142857, 0.05357142857142857],
[0.6607142857142857, 0.05357142857142857],
[0.6964285714285714, 0.05357142857142857],
[0.6964285714285714, 0.05357142857142857],
[0.7321428571428571, 0.05357142857142857],
[0.7321428571428571, 0.05357142857142857],
[0.7678571428571429, 0.05357142857142857],
[0.7678571428571429, 0.05357142857142857],
[0.8035714285714286, 0.05357142857142857],
[0.8035714285714286, 0.05357142857142857],
[0.8392857142857143, 0.05357142857142857],
[0.8392857142857143, 0.05357142857142857],
[0.875, 0.05357142857142857],
[0.875, 0.05357142857142857],
[0.9107142857142857, 0.05357142857142857],
[0.9107142857142857, 0.05357142857142857],
[0.9464285714285714, 0.05357142857142857],
[0.9464285714285714, 0.05357142857142857],
[0.9821428571428571, 0.05357142857142857],
[0.9821428571428571, 0.05357142857142857],
[0.017857142857142856, 0.08928571428571429],
[0.017857142857142856, 0.08928571428571429],
[0.05357142857142857, 0.08928571428571429],
[0.05357142857142857, 0.08928571428571429],
[0.08928571428571429, 0.08928571428571429],
[0.08928571428571429, 0.08928571428571429],
[0.125, 0.08928571428571429],
[0.125, 0.08928571428571429],
[0.16071428571428573, 0.08928571428571429],
[0.16071428571428573, 0.08928571428571429],
[0.19642857142857142, 0.08928571428571429],
[0.19642857142857142, 0.08928571428571429],
[0.23214285714285715, 0.08928571428571429],
[0.23214285714285715, 0.08928571428571429],
[0.26785714285714285, 0.08928571428571429],
[0.26785714285714285, 0.08928571428571429],
[0.30357142857142855, 0.08928571428571429],
[0.30357142857142855, 0.08928571428571429],
[0.3392857142857143, 0.08928571428571429],
[0.3392857142857143, 0.08928571428571429],
[0.375, 0.08928571428571429],
[0.375, 0.08928571428571429],
[0.4107142857142857, 0.08928571428571429],
[0.4107142857142857, 0.08928571428571429],
[0.44642857142857145, 0.08928571428571429],
[0.44642857142857145, 0.08928571428571429],
[0.48214285714285715, 0.08928571428571429],
[0.48214285714285715, 0.08928571428571429],
[0.5178571428571429, 0.08928571428571429],
[0.5178571428571429, 0.08928571428571429],
[0.5535714285714286, 0.08928571428571429],
[0.5535714285714286, 0.08928571428571429],
[0.5892857142857143, 0.08928571428571429],
[0.5892857142857143, 0.08928571428571429],
[0.625, 0.08928571428571429],
[0.625, 0.08928571428571429],
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