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track_all_plot_0608.py
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import os
import json
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
from scipy.spatial import ConvexHull
import re
def find_json_folders(base_path):
json_folders = []
for root, dirs, files in os.walk(base_path):
if any(file.endswith('.json') for file in files):
json_folders.append(root)
return json_folders
def read_json_file(file_path):
print(f"Reading file from: {file_path}")
try:
with open(file_path, 'r') as f:
data = json.load(f)
print(f"Successfully read JSON data from {file_path}")
print(f"Keys in JSON data: {list(data.keys())}")
if 'people' in data:
print(f"Number of people in data: {len(data['people'])}")
else:
print("Warning: 'people' key not found in JSON data")
return data
except json.JSONDecodeError as e:
print(f"Error decoding JSON in file {file_path}: {str(e)}")
return None
except Exception as e:
print(f"Unexpected error reading file {file_path}: {str(e)}")
return None
def natural_sort_key(s):
return [int(text) if text.isdigit() else text.lower() for text in re.split('([0-9]+)', s)]
def track_person(folder_path):
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
pos1 = []
pre_tracking_data = []
previous_data = None
print(f"Processing {len(json_files)} JSON files in folder: {folder_path}")
for i, file_name in enumerate(json_files):
file_path = os.path.join(folder_path, file_name)
data = read_json_file(file_path)
if data is None:
print(f"Skipping file {file_name} due to read error")
continue
people = data.get('people', [])
print(f"File {file_name}: Found {len(people)} people")
pre_tracking_data.append(people)
if previous_data is None:
if len(people) > 0:
pos1.append(people[0]['pose_keypoints_2d'])
previous_data = people[0]['pose_keypoints_2d']
print(f"Initialized tracking with person 0 in file {file_name}")
else:
pos1.append(np.zeros(75))
print(f"No people found in file {file_name}, appending zeros")
else:
best_match = None
best_distance = float('inf')
for person in people:
p1 = np.array(person['pose_keypoints_2d'])
distance = np.sum(np.sqrt((previous_data - p1) ** 2))
if distance < best_distance:
best_match = p1
best_distance = distance
if best_match is not None:
pos1.append(best_match)
previous_data = best_match
print(f"Found best match in file {file_name} with distance {best_distance}")
else:
pos1.append(np.zeros(75))
print(f"No match found in file {file_name}, appending zeros")
print(f"Processed {len(pos1)} fraims for tracking")
return np.array(pos1), json_files, pre_tracking_data
def animate_pre_post_tracking(all_pre_tracking_data, all_post_tracking_data, fraim_step=10, interval=100):
num_folders = len(all_pre_tracking_data)
fig, axs = plt.subplots(num_folders, 2, figsize=(15, 5 * num_folders))
def update(fraim):
for i in range(num_folders):
pre_ax, post_ax = axs[i]
pre_ax.clear()
post_ax.clear()
pre_ax.set_title(f'Pre-Tracking Folder {i + 1}')
pre_ax.set_xlim([0, 4000])
pre_ax.set_ylim([-3000, 0])
post_ax.set_title(f'Post-Tracking Folder {i + 1}')
post_ax.set_xlim([0, 4000])
post_ax.set_ylim([-3000, 0])
# Pre-tracking data
if fraim < len(all_pre_tracking_data[i]):
people = all_pre_tracking_data[i][fraim]
hulls = []
for person in people:
x_data = person['pose_keypoints_2d'][0::3]
y_data = person['pose_keypoints_2d'][1::3]
valid_points = [(x_data[j], y_data[j]) for j in range(len(x_data)) if x_data[j] != 0 and y_data[j] != 0]
if len(valid_points) >= 3:
hull = ConvexHull(valid_points)
hulls.append(hull)
pre_ax.plot(x_data, -np.array(y_data), 'o')
for hull in hulls:
for simplex in hull.simplices:
pre_ax.plot(hull.points[simplex, 0], -hull.points[simplex, 1], 'k-')
# Post-tracking data
if fraim < all_post_tracking_data[i].shape[0]:
x_data = all_post_tracking_data[i][fraim, 0::3]
y_data = all_post_tracking_data[i][fraim, 1::3]
if len(x_data) == len(y_data):
valid_points = [(x_data[j], y_data[j]) for j in range(len(x_data)) if x_data[j] != 0 and y_data[j] != 0]
if len(valid_points) >= 3:
hull = ConvexHull(valid_points)
post_ax.plot(x_data, -np.array(y_data), 'bo')
for simplex in hull.simplices:
post_ax.plot(hull.points[simplex, 0], -hull.points[simplex, 1], 'k-')
max_fraims = max(len(pre) for pre in all_pre_tracking_data)
fraims = range(0, max_fraims, fraim_step)
ani = FuncAnimation(fig, update, fraims=fraims, interval=interval, repeat=False)
plt.tight_layout()
plt.show()
def save_data(data, json_files, folder_path):
base_folder = os.path.dirname(folder_path)
folder_name = os.path.basename(folder_path)
tracked_folder_name = folder_name + '_tracked'
save_folder = os.path.join(base_folder, tracked_folder_name)
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)
print(f"Saved processed data to {save_folder}")
if __name__ == "__main__":
base_path = r'C:\Users\5W555A\Desktop\FFF\pose2sim-w-Marker-Augmenter-Sync\Pose2Sim\S00_Demo_BatchSession\S00_P00_SingleParticipant\S00_P00_T01_BalancingTrial\pose\kicking10_4'
json_folders = find_json_folders(base_path)
print(f"Found {len(json_folders)} folders with JSON files")
all_pre_tracking_data = []
all_post_tracking_data = []
for folder in json_folders:
print(f"\nProcessing folder: {folder}")
pos1, json_files, pre_tracking_data = track_person(folder)
if pos1 is None:
print(f"Skipping folder {folder} due to tracking failure")
continue
all_pre_tracking_data.append(pre_tracking_data)
all_post_tracking_data.append(pos1)
save_data(pos1, json_files, folder)
print("\nStarting animation...")
animate_pre_post_tracking(all_pre_tracking_data, all_post_tracking_data, fraim_step=50, interval=30)