import time import os import sys sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) import time import glob import numpy as np np.random.seed(42) from datetime import datetime import json from scipy.io import savemat, loadmat from src.utils import get_meshgrid, get_world_points, get_camera_points, get_screen_points, write_point_cloud, get_white_mask, get_meshgrid_contour, post_process from src.phase import extract_phase, unwrap_phase from src.recons import reconstruction_cumsum import matplotlib.pyplot as plt from src.calibration import calibrate_world, calibrate_screen, map_screen_to_world import argparse from src.vis import plot_coords import cv2 from src.eval import get_eval_result, find_notch import pickle def pmdstart(config_path, img_folder): start_time = time.time() print(f"config_path: {config_path}") #time.sleep(15) main(config_path, img_folder) print(f"img_folder: {img_folder}") print('test pass') end_time = time.time() print(f"Time taken: {end_time - start_time} seconds") return True def main(config_path, img_folder): cfg = json.load(open(config_path, 'r')) n_cam = 4 num_freq = cfg['num_freq'] save_path = None debug = False grid_spacing = cfg['grid_spacing'] num_freq = cfg['num_freq'] smooth = True align = True denoise = True print(f"开始执行时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}") print("\n1. 相机标定") preprocess_start = time.time() with open(os.path.join('config', cfg['cam_params']), 'rb') as pkl_file: cam_params = pickle.load(pkl_file) with open(os.path.join('config', cfg['screen_params']), 'rb') as pkl_file: screen_params = pickle.load(pkl_file)[0] preprocess_end = time.time() print(f" 完成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}") print(f" 耗时: {preprocess_end - preprocess_start:.2f} 秒") # import pdb; pdb.set_trace() print("\n2. 屏幕标定") screen_cal_start = time.time() screen_to_world = map_screen_to_world(screen_params, cam_params[0]) screen_cal_end = time.time() print(f" 完成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}") print(f" 耗时: {screen_cal_end - screen_cal_start:.2f} 秒") print("\n3. 相位提取，相位展开") phase_start = time.time() x_uns, y_uns = [], [] binary_masks = [] for cam_id in range(n_cam): white_path = os.path.join(img_folder, f'{cam_id}_frame_24.bmp') binary = get_white_mask(white_path) binary_masks.append(binary) phases = extract_phase(img_folder, cam_id, binary, cam_params[cam_id]['camera_matrix'], cam_params[cam_id]['distortion_coefficients'], num_freq=num_freq) x_un, y_un = unwrap_phase(phases, save_path, num_freq, debug=False) x_uns.append(x_un) y_uns.append(y_un) phase_end = time.time() print(f" 完成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}") print(f" 耗时: {phase_end - phase_start:.2f} 秒") print("\n4. 获得不同坐标系下点的位置") get_point_start = time.time() total_cloud_point = np.empty((0, 3)) for i in range(n_cam): if i > 5: continue contours_point = get_meshgrid_contour(binary_masks[i], grid_spacing, save_path, debug=False) world_points = get_world_points(contours_point, cam_params[i], grid_spacing, cfg['d'], save_path, debug=debug) camera_points, u_p, v_p = get_camera_points(world_points, cam_params[i], save_path, i, debug=debug) point_data = {'x_w': world_points[:, 0], 'y_w': world_points[:, 1], 'z_w': world_points[:, 2], 'x_c': camera_points[:, 0], 'y_c': camera_points[:, 1], 'z_c': camera_points[:, 2], 'u_p': u_p, 'v_p': v_p} screen_points = get_screen_points(point_data, x_uns[i], y_uns[i], screen_params, screen_to_world, cfg, save_path, i, debug=debug) #plot_coords(world_points, camera_points, screen_points) z, smoothed, aligned, denoised = reconstruction_cumsum(world_points, camera_points, screen_points, save_path, i, debug=debug, smooth=smooth, align=align, denoise=denoise) write_point_cloud(os.path.join(img_folder, str(i) + '_cloudpoint.txt'), world_points[:, 0], world_points[:, 1], 1000 * denoised[:,2]) total_cloud_point = np.vstack([total_cloud_point, np.column_stack((denoised[:, 0], denoised[:, 1], 1000 * denoised[:,2]))]) write_point_cloud(os.path.join(img_folder, 'cloudpoint.txt'), total_cloud_point[:, 0], total_cloud_point[:, 1], total_cloud_point[:,2]) if debug: fig = plt.figure() ax = fig.add_subplot(111, projection='3d') # 提取 x, y, z 坐标 x_vals = total_cloud_point[:, 0] y_vals = total_cloud_point[:, 1] z_vals = total_cloud_point[:, 2] # 绘制3D点云 ax.scatter(x_vals, y_vals, z_vals, c=z_vals, cmap='viridis', marker='o') # 设置轴标签和标题 ax.set_xlabel('X (mm)') ax.set_ylabel('Y (mm)') ax.set_zlabel('Z (mm)') ax.set_title('3D Point Cloud Visualization') plt.show() get_point_end = time.time() print(f" 完成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}") print(f" 耗时: {get_point_end - get_point_start:.2f} 秒") print("\n5. 后处理") post_process_start = time.time() post_process(img_folder, debug) post_process_end = time.time() print(f" 完成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}") print(f" 耗时: {post_process_end - post_process_start:.2f} 秒") print("\n6. 评估") eval_start = time.time() point_cloud_path = os.path.join(img_folder, str(i) + '_cloudpoint.txt') json_path = os.path.join(img_folder, str(i) + 'result.json') theta_notch = 0 get_eval_result(point_cloud_path, json_path, theta_notch) eval_end = time.time() print(f" 完成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}") print(f" 耗时: {eval_end - eval_start:.2f} 秒") return True if __name__ == '__main__': config_path = 'D:\\code\\pmdrecons-fourcam\\config\\cfg_3freq_wafer.json' img_folder = 'D:\\file\\20240913-data\\20240913105234292' pmdstart(config_path, img_folder)