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| import sys | |
| sys.path.append('.') | |
| import os | |
| import base64 | |
| import json | |
| from ctypes import * | |
| import cv2 | |
| import numpy as np | |
| from flask import Flask, request, jsonify | |
| from veinsdk import * | |
| from roi import * | |
| licensePath = "license.txt" | |
| license = "" | |
| machineCode = getMachineCode() | |
| print("\nmachineCode: ", machineCode.decode('utf-8')) | |
| # Get a specific environment variable by name | |
| license = os.environ.get("LICENSE") | |
| # Check if the variable exists | |
| if license is not None: | |
| print("Value of LICENSE:") | |
| else: | |
| license = "" | |
| try: | |
| with open(licensePath, 'r') as file: | |
| license = file.read().strip() | |
| except IOError as exc: | |
| print("failed to open license.txt: ", exc.errno) | |
| print("license: ", license) | |
| ret = setActivation(license.encode('utf-8')) | |
| print("\nactivation: ", ret) | |
| ret = initSDK() | |
| print("init: ", ret) | |
| app = Flask(__name__) | |
| def mat_to_bytes(mat): | |
| """ | |
| Convert cv::Mat image data (NumPy array in Python) to raw bytes. | |
| """ | |
| # Encode cv::Mat as PNG bytes | |
| is_success, buffer = cv2.imencode(".png", mat) | |
| if not is_success: | |
| raise ValueError("Failed to encode cv::Mat image") | |
| return buffer.tobytes() | |
| def palmvein(): | |
| result = None | |
| score = None | |
| file1 = request.files['file1'] | |
| file2 = request.files['file2'] | |
| try: | |
| image1 = cv2.imdecode(np.frombuffer(file1.read(), np.uint8), cv2.IMREAD_COLOR) | |
| except: | |
| result = "Failed to open file1" | |
| response = jsonify({"result": result, "score": float(score)}) | |
| response.status_code = 200 | |
| response.headers["Content-Type"] = "application/json; charset=utf-8" | |
| return response | |
| try: | |
| image2 = cv2.imdecode(np.frombuffer(file2.read(), np.uint8), cv2.IMREAD_COLOR) | |
| except: | |
| result = "Failed to open file2" | |
| response = jsonify({"result": result, "score": float(score)}) | |
| response.status_code = 200 | |
| response.headers["Content-Type"] = "application/json; charset=utf-8" | |
| return response | |
| roi1, label1 = get_roi_image(cv2.flip(image1, 1)) | |
| roi2, label2 = get_roi_image(cv2.flip(image2, 1)) | |
| if label1 != label2: | |
| result = "2 images are from the different hand" | |
| score = 0.0 | |
| response = jsonify({"result": result, "score": float(score)}) | |
| response.status_code = 200 | |
| response.headers["Content-Type"] = "application/json; charset=utf-8" | |
| return response | |
| if roi1 is None or roi2 is None: | |
| result = "\n hand detection failed !\n plesae make sure that input hand image is valid or not." | |
| response = jsonify({"result": result, "score": float(score)}) | |
| response.status_code = 200 | |
| response.headers["Content-Type"] = "application/json; charset=utf-8" | |
| return response | |
| roi_byte1 = mat_to_bytes(roi1) | |
| roi_byte2 = mat_to_bytes(roi2) | |
| feature_array1, feature_array2 = (c_float * 1024)(), (c_float * 1024)() # Assuming a maximum of 256 rectangles | |
| cnt1 = getFeature(roi_byte1, len(roi_byte1), feature_array1) | |
| cnt2 = getFeature(roi_byte2, len(roi_byte2), feature_array2) | |
| if cnt1 == 0 or cnt2 ==0: | |
| result = "feature extraction failed !" | |
| response = jsonify({"result": result, "score": float(score)}) | |
| response.status_code = 200 | |
| response.headers["Content-Type"] = "application/json; charset=utf-8" | |
| return response | |
| score = getScore(feature_array1, cnt1, feature_array2, cnt2) | |
| if score >= 0.65: | |
| result = "Same Hand !" | |
| # print(f"\n 2 images are from the same hand\n similarity: {score}") | |
| response = jsonify({"result": result, "score": float(score)}) | |
| response.status_code = 200 | |
| response.headers["Content-Type"] = "application/json; charset=utf-8" | |
| return response | |
| else: | |
| result = "Different Hand !" | |
| # print(f"\n 2 images are from the different hand\n similarity: {score}") | |
| response = jsonify({"result": result, "score": float(score)}) | |
| response.status_code = 200 | |
| response.headers["Content-Type"] = "application/json; charset=utf-8" | |
| return response | |
| def palmvein_base64(): | |
| result = None | |
| score = None | |
| content = request.get_json() | |
| try: | |
| imageBase64 = content['base64_1'] | |
| image_data = base64.b64decode(imageBase64) | |
| np_array = np.frombuffer(image_data, np.uint8) | |
| image1 = cv2.imdecode(np_array, cv2.IMREAD_COLOR) | |
| except: | |
| result = "Failed to open file1" | |
| response = jsonify({"result": result, "score": float(score)}) | |
| response.status_code = 200 | |
| response.headers["Content-Type"] = "application/json; charset=utf-8" | |
| return response | |
| try: | |
| imageBase64 = content['base64_2'] | |
| image_data = base64.b64decode(imageBase64) | |
| np_array = np.frombuffer(image_data, np.uint8) | |
| image2 = cv2.imdecode(np_array, cv2.IMREAD_COLOR) | |
| except: | |
| result = "Failed to open file2" | |
| response = jsonify({"result": result, "score": float(score)}) | |
| response.status_code = 200 | |
| response.headers["Content-Type"] = "application/json; charset=utf-8" | |
| return response | |
| roi1, label1 = get_roi_image(cv2.flip(image1, 1)) | |
| roi2, label2 = get_roi_image(cv2.flip(image2, 1)) | |
| if label1 != label2: | |
| result = "2 images are from the different hand" | |
| score = 0.0 | |
| response = jsonify({"result": result, "score": float(score)}) | |
| response.status_code = 200 | |
| response.headers["Content-Type"] = "application/json; charset=utf-8" | |
| return response | |
| if roi1 is None or roi2 is None: | |
| result = "\n hand detection failed !\n plesae make sure that input hand image is valid or not." | |
| response = jsonify({"result": result, "score": float(score)}) | |
| response.status_code = 200 | |
| response.headers["Content-Type"] = "application/json; charset=utf-8" | |
| return response | |
| roi_byte1 = mat_to_bytes(roi1) | |
| roi_byte2 = mat_to_bytes(roi2) | |
| feature_array1, feature_array2 = (c_float * 1024)(), (c_float * 1024)() # Assuming a maximum of 256 rectangles | |
| cnt1 = getFeature(roi_byte1, len(roi_byte1), feature_array1) | |
| cnt2 = getFeature(roi_byte2, len(roi_byte2), feature_array2) | |
| if cnt1 == 0 or cnt2 ==0: | |
| result = "feature extraction failed !" | |
| response = jsonify({"result": result, "score": float(score)}) | |
| response.status_code = 200 | |
| response.headers["Content-Type"] = "application/json; charset=utf-8" | |
| return response | |
| score = getScore(feature_array1, cnt1, feature_array2, cnt2) | |
| if score >= 0.65: | |
| result = "Same Hand !" | |
| # print(f"\n 2 images are from the same hand\n similarity: {score}") | |
| response = jsonify({"result": result, "score": float(score)}) | |
| response.status_code = 200 | |
| response.headers["Content-Type"] = "application/json; charset=utf-8" | |
| return response | |
| else: | |
| result = "Different Hand !" | |
| # print(f"\n 2 images are from the different hand\n similarity: {score}") | |
| response = jsonify({"result": result, "score": float(score)}) | |
| response.status_code = 200 | |
| response.headers["Content-Type"] = "application/json; charset=utf-8" | |
| return response | |
| if __name__ == '__main__': | |
| port = int(os.environ.get("PORT", 8080)) | |
| app.run(host='0.0.0.0', port=port) |