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