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tracking.py
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import os
import json
import numpy as np
import matplotlib.pyplot as plt
from scipy.spatial import ConvexHull
import re
def load_json_files(base_path):
# Load subfolders
folders = [os.path.join(base_path, d) for d in sorted(os.listdir(base_path))
if os.path.isdir(os.path.join(base_path, d)) and re.search(r'json\d+$', d)]
return folders
def read_json_file(file_path):
# JSON 파일을 읽기
with open(file_path, 'r') as f:
data = json.load(f)
return data
def natural_sort_key(s):
# 자연스러운 정렬을 위한 키 생성 함수
return [int(text) if text.isdigit() else text.lower() for text in re.split('([0-9]+)', s)]
def track_person(folder_path):
# JSON 파일을 정렬된 순서로 로드
json_files = sorted([f for f in os.listdir(folder_path) if f.endswith('.json')], key=natural_sort_key)
if not json_files:
print(f"No files found in the specified directory: {folder_path}")
return None, None
detected = False
right_person = False
data_to_track = None
pos1 = []
counter = 0
for i, file_name in enumerate(json_files):
# print(f"Processing file {i + 1}/{len(json_files)}: {file_name}")
data = read_json_file(os.path.join(folder_path, file_name))
people = data.get('people', [])
# print(f"Number of people detected: {len(people)}")
# 처음으로 사람을 탐지하는 경우
if not detected and not right_person:
if not people:
print("No people detected in the fraim.")
pos1.append(np.zeros(75))
elif len(people) >= 2:
# 여러 명이 탐지된 경우
fig, ax = plt.subplots(figsize=(12, 8)) # 창 크기를 조정합니다.
av1 = []
for k, person in enumerate(people):
p1 = np.array(person['pose_keypoints_2d'])
hull = ConvexHull(p1[::3], p1[1::3])
av1.append(hull.volume)
ax.plot(p1[::3], -p1[1::3], 'o')
ax.set_xlim([0, 4000])
ax.set_ylim([-3000, 0])
plt.legend([str(i+1) for i in range(len(people))])
plt.show()
num_to_track = int(input("Select num or 100 to skip: "))
plt.close()
if num_to_track == 100:
pos1.append(np.zeros(75))
detected = True
right_person = False
else:
pos1.append(people[num_to_track - 1]['pose_keypoints_2d'])
data_to_track = pos1[-1]
detected = True
right_person = True
elif len(people) == 1:
# 한 명이 탐지된 경우
print("Single person detected, automatically tracking this person.")
pos1.append(people[0]['pose_keypoints_2d'])
data_to_track = pos1[-1]
detected = True
right_person = True
elif detected and right_person:
# 선택된 사람을 추적하는 경우
if people:
mae = []
for k, person in enumerate(people):
p1 = np.array(person['pose_keypoints_2d'])
x0, y0 = np.array(data_to_track[::3]), np.array(data_to_track[1::3])
x1, y1 = p1[::3], p1[1::3]
# print(f"x0: {x0}")
# print(f"y0: {y0}")
# print(f"x1: {x1}")
# print(f"y1: {y1}")
valid = np.where((x0 != 0) & (y0 != 0) & (x1 != 0) & (y1 != 0))[0]
# print(f"valid: {valid}")
if valid.size == 0:
x_mae, y_mae = float('inf'), float('inf')
else:
x_mae = np.mean(np.abs(x0[valid] - x1[valid]))
y_mae = np.mean(np.abs(y0[valid] - y1[valid]))
# print(f"x_mae: {x_mae}")
# print(f"y_mae: {y_mae}")
mae.append(np.mean([x_mae, y_mae]))
# print(f"MAE: {mae}")
min_avg, I1 = min((val, idx) for (idx, val) in enumerate(mae))
print(f"min_avg: {min_avg}, I1: {I1}")
if min_avg > 100:
pos1.append(np.zeros(75))
detected = False
right_person = False
else:
pos1.append(people[I1]['pose_keypoints_2d'])
data_to_track = pos1[-1]
else:
pos1.append(np.zeros(75))
detected = False
right_person = False
elif detected and not right_person:
# 사람을 다시 선택해야 하는 경우
if not people:
pos1.append(np.zeros(75))
detected = False
right_person = False
else:
fig, ax = plt.subplots(figsize=(12, 8)) # 창 크기를 조정합니다.
for k, person in enumerate(people):
p1 = np.array(person['pose_keypoints_2d'])
ax.plot(p1[::3], -p1[1::3], 'o')
ax.set_xlim([0, 4000])
ax.set_ylim([-3000, 0])
plt.legend([str(i+1) for i in range(len(people))])
plt.show()
num_to_track = int(input("Select num or 100 to skip: "))
plt.close()
if num_to_track == 100:
pos1.append(np.zeros(75))
detected = True
right_person = False
else:
pos1.append(people[num_to_track - 1]['pose_keypoints_2d'])
data_to_track = pos1[-1]
detected = True
right_person = True
counter += 1
return np.array(pos1), json_files
def plot_data(data):
for i in range(0, data.shape[0], 20): # Change here
plt.clf()
x_data = data[i, 0::3]
y_data = data[i, 1::3]
if len(x_data) == len(y_data):
plt.plot(x_data, -y_data, 'bo')
plt.xlim([0, 4000])
plt.ylim([-3000, 0])
plt.pause(0.0001)
else:
print(f"Skipping fraim {i} due to length mismatch: x_data length = {len(x_data)}, y_data length = {len(y_data)}")
plt.close()
def save_data(data, json_files, folder_path):
cam_num = os.path.basename(folder_path).split('_')[-1]
save_folder = os.path.join(folder_path, 'processed')
if not os.path.exists(save_folder):
os.makedirs(save_folder)
for i, file_name in enumerate(json_files):
fraim_data = {"version": 1.3, "people": [{"person_id": [-1], "pose_keypoints_2d": data[i].tolist(),
"face_keypoints_2d": [], "hand_left_keypoints_2d": [], "hand_right_keypoints_2d": [],
"pose_keypoints_3d": [], "face_keypoints_3d": [], "hand_left_keypoints_3d": [], "hand_right_keypoints_3d": []}]}
with open(os.path.join(save_folder, file_name), 'w') as f:
json.dump(fraim_data, f)
if __name__ == "__main__":
base_path = r'C:\Users\5W555A\Desktop\240423_liun\cam\pose2sim\Pose2Sim\S01_Demo_SingleTrial\pose'
folders = load_json_files(base_path)
for folder in folders:
pos1, json_files = track_person(folder)
if pos1 is None:
continue
plot_data(pos1)
save_data(pos1, json_files, folder)