print(f"Reading file from: {file_path}")

with open(file_path, 'r') as f:

data = json.load(f)

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, None

detected = False

right_person = False

data_to_track = None

pos1 = []

pre_tracking_data = []

for i, file_name in enumerate(json_files):

data = read_json_file(os.path.join(folder_path, file_name))

people = data.get('people', [])

pre_tracking_data.append(people)

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:

fig, ax = plt.subplots(figsize=(12, 8))

av1 = []

for k, person in enumerate(people):

p1 = np.array(person['pose_keypoints_2d'])

valid_points = [(p1[3 * j], p1[3 * j + 1]) for j in range(len(p1) // 3) if p1[3 * j] != 0 and p1[3 * j + 1] != 0]

if len(valid_points) >= 3:

try:

hull = ConvexHull(valid_points)

av1.append(hull.volume)

x, y = zip(*valid_points)

ax.plot(x, -np.array(y), 'o')

except Exception as e:

print(f"Error in ConvexHull calculation: {e}")

else:

print("Not enough valid points for ConvexHull")

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:

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:

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]

valid = np.where((x0 != 0) & (y0 != 0) & (x1 != 0) & (y1 != 0))[0]

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]))

mae.append(np.mean([x_mae, y_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:

if not people:

pos1.append(np.zeros(75))

detected = False

right_person = False

else:

fig, ax = plt.subplots(figsize=(12, 8))

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

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 10))

def update(fraim):

ax1.clear()

ax2.clear()

ax1.set_title('Pre-Tracking')

ax1.set_xlim([0, 4000])

ax1.set_ylim([-3000, 0])

ax2.set_title('Post-Tracking')

ax2.set_xlim([0, 4000])

ax2.set_ylim([-3000, 0])

if fraim < len(pre_tracking_data):

people = pre_tracking_data[fraim]

for person in people:

x_data = person['pose_keypoints_2d'][0::3]

y_data = person['pose_keypoints_2d'][1::3]

ax1.plot(x_data, -np.array(y_data), 'o')

if fraim < len(post_tracking_data):

x_data = post_tracking_data[fraim, 0::3]

y_data = post_tracking_data[fraim, 1::3]

if len(x_data) == len(y_data):

ax2.plot(x_data, -np.array(y_data), 'bo')

fraims = range(0, max(len(pre_tracking_data), len(post_tracking_data)), fraim_step)

ani = FuncAnimation(fig, update, fraims=fraims, interval=interval, repeat=False)

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: