from flask import Flask, request, jsonify, send_from_directory import os import json import base64 import numpy as np import pickle from flask_cors import CORS from data_load import DataLoadUtil from reconstruction import ReconstructionUtil from pts import PtsUtil ROOT = os.path.join("./static") print(ROOT) app = Flask(__name__, static_folder="static") CORS(app) @app.route("/") def serve_index(): return send_from_directory(app.static_folder, "index.html") @app.route('/') def serve_static(filename): return send_from_directory(app.static_folder, filename) @app.route('/get_scene_list', methods=['POST']) def get_scene_list(): data = request.json dataset_name = data.get('dataset_name') dataset_path = os.path.join(ROOT, dataset_name) if not os.path.exists(dataset_path): print(f"Dataset not found: {dataset_path}") return jsonify({"error": "Dataset not found"}), 404 scene_list = [d for d in os.listdir(dataset_path) if os.path.isdir(os.path.join(dataset_path, d))] return jsonify({"scene_list": scene_list, "success": True}) @app.route('/get_scene_info', methods=['POST']) def get_scene_info(): data = request.json dataset_name = data.get('dataset_name') scene_name = data.get('scene_name') scene_path = os.path.join(ROOT, dataset_name, scene_name) camera_params_path = os.path.join(scene_path, 'camera_params') label_json_path = os.path.join(scene_path, 'label.json') if not os.path.exists(scene_path) or not os.path.exists(label_json_path): return jsonify({"error": "Scene or label.json not found"}), 404 with open(label_json_path, 'r') as f: label_data = json.load(f) sequence_length = len([f for f in os.listdir(camera_params_path) if os.path.isfile(os.path.join(camera_params_path, f))]) max_coverage_rate = label_data.get('max_coverage_rate') best_sequence = label_data.get('best_sequence') best_sequence_length = len(best_sequence) best_sequence_formatted = [] for i in range(best_sequence_length): best_sequence_formatted.append( { "frame": best_sequence[i][0], "coverage_rate": round(best_sequence[i][1]*100,1) } ) return jsonify({ "sequence_length": sequence_length, "max_coverage_rate": round(max_coverage_rate*100,2), "best_sequence_length": best_sequence_length, "best_sequence": best_sequence_formatted, "success": True }) def read_image_as_base64(file_path): try: with open(file_path, 'rb') as image_file: encoded_string = base64.b64encode(image_file.read()).decode('utf-8') return encoded_string except FileNotFoundError: return None @app.route('/get_frame_data', methods=['POST']) def get_frame_data(): data = request.json dataset_name = data.get('dataset_name') scene_name = data.get('scene_name') sequence = data.get('sequence') scene_path = os.path.join(ROOT, dataset_name, scene_name) root = os.path.join(ROOT, dataset_name) camera_params_path = os.path.join(scene_path, 'camera_params') depth_path = os.path.join(scene_path, 'depth') mask_path = os.path.join(scene_path, 'mask') model_points_normals = DataLoadUtil.load_points_normals(root, scene_name) model_points = model_points_normals[:, :3] obj_path = os.path.join(dataset_name, scene_name, 'mesh', 'world_target_mesh.obj') mtl_path = os.path.join(dataset_name, scene_name, 'mesh', 'material.mtl') if not all([os.path.exists(scene_path), os.path.exists(camera_params_path), os.path.exists(depth_path), os.path.exists(mask_path)]): return jsonify({"error": "Invalid paths or files not found"}), 404 result = [] combined_point_cloud = np.zeros((0, 3)) last_CR = 0 for frame_info in sequence: frame_id = frame_info.get('frame') frame_data = {} path = DataLoadUtil.get_path(root, scene_name, frame_id) cam_params = DataLoadUtil.load_cam_info(path, binocular=True) frame_data['cam_to_world'] = cam_params['cam_to_world'].tolist() depth_file = os.path.join(depth_path, f'{frame_id}_L.png') depth_base64 = read_image_as_base64(depth_file) frame_data['depth'] = depth_base64 if depth_base64 else None mask_file = os.path.join(mask_path, f'{frame_id}_L.png') mask_base64 = read_image_as_base64(mask_file) frame_data['mask'] = mask_base64 if mask_base64 else None point_cloud = DataLoadUtil.get_target_point_cloud_world_from_path(path, binocular=True) sampled_point_cloud = ReconstructionUtil.filter_points(point_cloud, model_points_normals, cam_params['cam_to_world'], theta=75) sampled_point_cloud = PtsUtil.voxel_downsample_point_cloud(sampled_point_cloud, 0.01) frame_data['new_point_cloud'] = sampled_point_cloud.tolist() frame_data['combined_point_cloud'] = combined_point_cloud.tolist() combined_point_cloud = np.concatenate([combined_point_cloud, sampled_point_cloud], axis=0) combined_point_cloud = PtsUtil.voxel_downsample_point_cloud(combined_point_cloud, 0.01) frame_data["coverage_rate"] = frame_info.get('coverage_rate') delta_CR = frame_data["coverage_rate"] - last_CR frame_data["delta_CR"] = round(delta_CR,2) last_CR = frame_data["coverage_rate"] result.append({ "frame_id": frame_id, "data": frame_data }) return jsonify({"seq_frame_data": result,"model_pts":model_points.tolist(), "obj_path": obj_path, "mtl_path":mtl_path, "success": True}) @app.route('/analysis_inference_result', methods=['POST']) def analysis_inference_result(): res = {"success": True} if 'file' not in request.files: res["success"] = False res["message"] = "No file part" return jsonify(res) file = request.files['file'] if file.filename == '': res["success"] = False res["message"] = "No selected file" return jsonify(res) try: data = pickle.load(file) except Exception as e: res["success"] = False res["message"] = f"File processing error: {e}" return jsonify(res) print(data) return jsonify(res) if __name__ == '__main__': app.run(debug=True, port=13333)