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import os
import tqdm
import cv2
import numpy as np
import pickle
# root="./cv"
root="/home/chentingwei/LoFi/lofi"
# 加载 YOLO 模型
net = cv2.dnn.readNet("./model/yolov3.weights", "./model/yolov3.cfg")
# 获取输出层的名称
layer_names = net.getLayerNames()
output_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()]
src_points = np.array([[0, 0], [180, 0], [0, 480], [180, 480]], dtype="float32") # real world
dst_points = np.array([[222, 210], [374, 209], [65, 458], [495, 451]], dtype="float32") # image world
M = cv2.getPerspectiveTransform(src_points, dst_points)
data=[]
def get_gt(img_path,net):
image = cv2.imread(img_path)
# 加载图像
height, width, channels = image.shape
# 准备输入图像
blob = cv2.dnn.blobFromImage(image, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
net.setInput(blob)
outs = net.forward(output_layers)
# 解析 YOLO 输出，找到人体边界框
class_ids = []
confidences = []
boxes = []
for out in outs:
for detection in out:
scores = detection[5:]
class_id = np.argmax(scores)
confidence = scores[class_id]
if confidence > 0.5: # 置信度阈值
center_x = int(detection[0] * width)
center_y = int(detection[1] * height)
w = int(detection[2] * width)
h = int(detection[3] * height)
x = int(center_x - w / 2)
y = int(center_y - h / 2)
boxes.append([x, y, w, h])
confidences.append(float(confidence))
class_ids.append(class_id)
# 如果检测到了多个框，只保留置信度最高的那个框
if len(boxes) > 0:
max_confidence_idx = np.argmax(confidences)
boxes = [boxes[max_confidence_idx]]
x, y, w, h = boxes[0]
foot_position_image = (x + w // 2, y + h)
person_img_coords = np.array([[foot_position_image[0], foot_position_image[1]]],
dtype="float32")
actual_coords = cv2.perspectiveTransform(np.array([person_img_coords]), np.linalg.inv(M))
return actual_coords[0,0,0],actual_coords[0,0,1]
people_id=0
for people in os.listdir(root):
print(people)
path=os.path.join(root,people)
pbar = tqdm.tqdm(os.listdir(path))
x_list = []
y_list = []
img_path_list = []
time_list = []
for pic in pbar:
if "color" not in pic:
continue
# print(pic)
timestamp = pic.split("_")
timestamp = timestamp[-1].split(".")
timestamp = timestamp[0]
timestamp = timestamp.split("-")
# print(timestamp)
timestamp = float(timestamp[0]) * 60 * 60 * 100 + float(timestamp[1]) * 60 * 100 + float(timestamp[2]) * 100 + float(timestamp[3])
img_path = os.path.join(path, pic)
x, y = get_gt(img_path, net)
x_list.append(x)
y_list.append(y)
img_path_list.append(img_path)
time_list.append(timestamp)
data.append({
'timestamp': np.array(time_list),
'people_name': people,
'people': people_id,
'x': np.array(x_list),
'y': np.array(y_list),
'img_path': img_path_list
})
people_id += 1
output_file = './gt_data.pkl'
with open(output_file, 'wb') as f:
pickle.dump(data, f)
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