如何在Python中实现机器可读区(MRZ)识别
Machine Readable Zone (MRZ) is a crucial feature adopted in modern passports, visas, and ID cards. It contains essential information about the document holder, such as their name, gender, country code, and document number. MRZ recognition plays a key role in border control, airport security, and hotel check-in processes. In this tutorial, we will demonstrate how to leverage the Dynamsoft Capture Vision SDK to implement MRZ recognition across Windows, Linux, and macOS platforms. This guide will provide a step-by-step approach to harness the SDK’s powerful features, making cross-platform MRZ detection seamless and efficient.
Python MRZ Recognition Demo on macOS
Prerequisites
Dynamsoft Capture Vision Trial License: Obtain a 30-Day trial license key for the Dynamsoft Capture Vision SDK.
-
Python Packages: Install the required Python packages using the following commands:
pip install dynamsoft-capture-vision-bundle opencv-python
登录后复制What are these packages for?
- dynamsoft-capture-vision-bundle is the Dynamsoft Capture Vision SDK for Python.
- opencv-python captures camera frames and displays processed image results.
Getting Started with the Dynamsoft Python Capture Vision Example
The official MRZ scanner example demonstrates how to create a simple Python-based MRZ reader using the Dynamsoft Capture Vision SDK in a short time.
Let's take a look at the source code and analyze its functionality:
import sys from dynamsoft_capture_vision_bundle import * import os class MRZResult: def __init__(self, item: ParsedResultItem): self.doc_type = item.get_code_type() self.raw_text=[] self.doc_id = None self.surname = None self.given_name = None self.nationality = None self.issuer = None self.gender = None self.date_of_birth = None self.date_of_expiry = None if self.doc_type == "MRTD_TD3_PASSPORT": if item.get_field_value("passportNumber") != None and item.get_field_validation_status("passportNumber") != EnumValidationStatus.VS_FAILED: self.doc_id = item.get_field_value("passportNumber") elif item.get_field_value("documentNumber") != None and item.get_field_validation_status("documentNumber") != EnumValidationStatus.VS_FAILED: self.doc_id = item.get_field_value("documentNumber") line = item.get_field_value("line1") if line is not None: if item.get_field_validation_status("line1") == EnumValidationStatus.VS_FAILED: line += ", Validation Failed" self.raw_text.append(line) line = item.get_field_value("line2") if line is not None: if item.get_field_validation_status("line2") == EnumValidationStatus.VS_FAILED: line += ", Validation Failed" self.raw_text.append(line) line = item.get_field_value("line3") if line is not None: if item.get_field_validation_status("line3") == EnumValidationStatus.VS_FAILED: line += ", Validation Failed" self.raw_text.append(line) if item.get_field_value("nationality") != None and item.get_field_validation_status("nationality") != EnumValidationStatus.VS_FAILED: self.nationality = item.get_field_value("nationality") if item.get_field_value("issuingState") != None and item.get_field_validation_status("issuingState") != EnumValidationStatus.VS_FAILED: self.issuer = item.get_field_value("issuingState") if item.get_field_value("dateOfBirth") != None and item.get_field_validation_status("dateOfBirth") != EnumValidationStatus.VS_FAILED: self.date_of_birth = item.get_field_value("dateOfBirth") if item.get_field_value("dateOfExpiry") != None and item.get_field_validation_status("dateOfExpiry") != EnumValidationStatus.VS_FAILED: self.date_of_expiry = item.get_field_value("dateOfExpiry") if item.get_field_value("sex") != None and item.get_field_validation_status("sex") != EnumValidationStatus.VS_FAILED: self.gender = item.get_field_value("sex") if item.get_field_value("primaryIdentifier") != None and item.get_field_validation_status("primaryIdentifier") != EnumValidationStatus.VS_FAILED: self.surname = item.get_field_value("primaryIdentifier") if item.get_field_value("secondaryIdentifier") != None and item.get_field_validation_status("secondaryIdentifier") != EnumValidationStatus.VS_FAILED: self.given_name = item.get_field_value("secondaryIdentifier") def to_string(self): msg = (f"Raw Text:\n") for index, line in enumerate(self.raw_text): msg += (f"\tLine {index + 1}: {line}\n") msg+=(f"Parsed Information:\n" f"\tDocumentType: {self.doc_type or ''}\n" f"\tDocumentID: {self.doc_id or ''}\n" f"\tSurname: {self.surname or ''}\n" f"\tGivenName: {self.given_name or ''}\n" f"\tNationality: {self.nationality or ''}\n" f"\tIssuingCountryorOrganization: {self.issuer or ''}\n" f"\tGender: {self.gender or ''}\n" f"\tDateofBirth(YYMMDD): {self.date_of_birth or ''}\n" f"\tExpirationDate(YYMMDD): {self.date_of_expiry or ''}\n") return msg def print_results(result: ParsedResult) -> None: tag = result.get_original_image_tag() if isinstance(tag, FileImageTag): print("File:", tag.get_file_path()) if result.get_error_code() != EnumErrorCode.EC_OK: print("Error:", result.get_error_string()) else: items = result.get_items() print("Parsed", len(items), "MRZ Zones.") for item in items: mrz_result = MRZResult(item) print(mrz_result.to_string()) if __name__ == '__main__': print("**********************************************************") print("Welcome to Dynamsoft Capture Vision - MRZ Sample") print("**********************************************************") error_code, error_message = LicenseManager.init_license("LICENSE-KEY") if error_code != EnumErrorCode.EC_OK and error_code != EnumErrorCode.EC_LICENSE_CACHE_USED: print("License initialization failed: ErrorCode:", error_code, ", ErrorString:", error_message) else: cvr_instance = CaptureVisionRouter() while (True): image_path = input( ">> Input your image full path:\n" ">> 'Enter' for sample image or 'Q'/'q' to quit\n" ).strip('\'"') if image_path.lower() == "q": sys.exit(0) if image_path == "": image_path = "../Images/passport-sample.jpg" if not os.path.exists(image_path): print("The image path does not exist.") continue result = cvr_instance.capture(image_path, "ReadPassportAndId") if result.get_error_code() != EnumErrorCode.EC_OK: print("Error:", result.get_error_code(), result.get_error_string()) else: parsed_result = result.get_parsed_result() if parsed_result is None or len(parsed_result.get_items()) == 0: print("No parsed results.") else: print_results(parsed_result) input("Press Enter to quit...")
Explanation
- The LicenseManager.init_license method initializes the Dynamsoft Capture Vision SDK with a valid license key.
- The CaptureVisionRouter class manages image processing tasks and coordinates various image processing modules. Its capture method processes the input image and returns the result.
- The ReadPassportAndId is a built-in template specifying the processing mode. The SDK supports various processing modes, such as MRZ recognition, document edge detection, and barcode detection.
- The get_parsed_result method retrieves the MRZ recognition result as a dictionary. The MRZResult class extracts and wraps the relevant MRZ information. Since this class can be reused across different applications, it is recommended to move it to a utils.py file.
In the next section, we will use OpenCV to visualize the MRZ recognition results and display the detected MRZ zones on the passport image.
Visualizing Machine Readable Zone Location in a Passport Image
In the code above, result is an instance of the CapturedResult class. Calling its get_recognized_text_lines_result() method retrieves a list of TextLineResultItem objects. Each TextLineResultItem object contains the coordinates of the detected text line. Use the following code snippet to extract the coordinates and draw contours on the passport image:
cv_image = cv2.imread(image_path) line_result = result.get_recognized_text_lines_result() items = line_result.get_items() for item in items: location = item.get_location() x1 = location.points[0].x y1 = location.points[0].y x2 = location.points[1].x y2 = location.points[1].y x3 = location.points[2].x y3 = location.points[2].y x4 = location.points[3].x y4 = location.points[3].y del location cv2.drawContours( cv_image, [np.intp([(x1, y1), (x2, y2), (x3, y3), (x4, y4)])], 0, (0, 255, 0), 2) cv2.imshow( "Original Image with Detected MRZ Zone", cv_image) cv2.waitKey(0) cv2.destroyAllWindows()
Scanning and Recognizing MRZ in Real-time via Webcam
Scanning and recognizing MRZ in real-time via webcam requires capturing a continuous image stream. We can use the OpenCV library to capture frames from the webcam and process them with the Dynamsoft Capture Vision SDK. The following code snippet demonstrates how to implement real-time MRZ recognition using a webcam:
from dynamsoft_capture_vision_bundle import * import cv2 import numpy as np import queue from utils import * class FrameFetcher(ImageSourceAdapter): def has_next_image_to_fetch(self) -> bool: return True def add_frame(self, imageData): self.add_image_to_buffer(imageData) class MyCapturedResultReceiver(CapturedResultReceiver): def __init__(self, result_queue): super().__init__() self.result_queue = result_queue def on_captured_result_received(self, captured_result): self.result_queue.put(captured_result) if __name__ == '__main__': errorCode, errorMsg = LicenseManager.init_license( "LICENSE-KEY") if errorCode != EnumErrorCode.EC_OK and errorCode != EnumErrorCode.EC_LICENSE_CACHE_USED: print("License initialization failed: ErrorCode:", errorCode, ", ErrorString:", errorMsg) else: vc = cv2.VideoCapture(0) if not vc.isOpened(): print("Error: Camera is not opened!") exit(1) cvr = CaptureVisionRouter() fetcher = FrameFetcher() cvr.set_input(fetcher) # Create a thread-safe queue to store captured items result_queue = queue.Queue() receiver = MyCapturedResultReceiver(result_queue) cvr.add_result_receiver(receiver) errorCode, errorMsg = cvr.start_capturing("ReadPassportAndId") if errorCode != EnumErrorCode.EC_OK: print("error:", errorMsg) while True: ret, frame = vc.read() if not ret: print("Error: Cannot read frame!") break fetcher.add_frame(convertMat2ImageData(frame)) if not result_queue.empty(): captured_result = result_queue.get_nowait() items = captured_result.get_items() for item in items: if item.get_type() == EnumCapturedResultItemType.CRIT_TEXT_LINE: text = item.get_text() line_results = text.split('\n') location = item.get_location() x1 = location.points[0].x y1 = location.points[0].y x2 = location.points[1].x y2 = location.points[1].y x3 = location.points[2].x y3 = location.points[2].y x4 = location.points[3].x y4 = location.points[3].y cv2.drawContours( frame, [np.intp([(x1, y1), (x2, y2), (x3, y3), (x4, y4)])], 0, (0, 255, 0), 2) delta = y3 - y1 for line_result in line_results: cv2.putText( frame, line_result, (x1, y1), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1, cv2.LINE_AA) y1 += delta del location elif item.get_type() == EnumCapturedResultItemType.CRIT_PARSED_RESULT: mrz_result = MRZResult(item) print(mrz_result.to_string()) if cv2.waitKey(1) & 0xFF == ord('q'): break cv2.imshow('frame', frame) cvr.stop_capturing() vc.release() cv2.destroyAllWindows()
Explanation
- The FrameFetcher class implements the ImageSourceAdapter interface to feed frame data into the built-in buffer.
- The MyCapturedResultReceiver class implements the CapturedResultReceiver interface. The on_captured_result_received method runs on a native C++ worker thread, sending CapturedResult objects to the main thread where they are stored in a thread-safe queue for further use.
- A CapturedResult contains several CapturedResultItem objects. The CRIT_TEXT_LINE type represents recognized text lines, while the CRIT_PARSED_RESULT type represents parsed MRZ data.
Running the Real-time MRZ Recognition Demo on Windows
Source Code
https://github.com/yushulx/python-mrz-scanner-sdk/tree/main/examples/official
以上是如何在Python中实现机器可读区(MRZ)识别的详细内容。更多信息请关注PHP中文网其他相关文章!

热AI工具

Undresser.AI Undress
人工智能驱动的应用程序,用于创建逼真的裸体照片

AI Clothes Remover
用于从照片中去除衣服的在线人工智能工具。

Undress AI Tool
免费脱衣服图片

Clothoff.io
AI脱衣机

Video Face Swap
使用我们完全免费的人工智能换脸工具轻松在任何视频中换脸!

热门文章

热工具

记事本++7.3.1
好用且免费的代码编辑器

SublimeText3汉化版
中文版,非常好用

禅工作室 13.0.1
功能强大的PHP集成开发环境

Dreamweaver CS6
视觉化网页开发工具

SublimeText3 Mac版
神级代码编辑软件(SublimeText3)

Python更易学且易用,C 则更强大但复杂。1.Python语法简洁,适合初学者,动态类型和自动内存管理使其易用,但可能导致运行时错误。2.C 提供低级控制和高级特性,适合高性能应用,但学习门槛高,需手动管理内存和类型安全。

要在有限的时间内最大化学习Python的效率,可以使用Python的datetime、time和schedule模块。1.datetime模块用于记录和规划学习时间。2.time模块帮助设置学习和休息时间。3.schedule模块自动化安排每周学习任务。

Python在开发效率上优于C ,但C 在执行性能上更高。1.Python的简洁语法和丰富库提高开发效率。2.C 的编译型特性和硬件控制提升执行性能。选择时需根据项目需求权衡开发速度与执行效率。

每天学习Python两个小时是否足够?这取决于你的目标和学习方法。1)制定清晰的学习计划,2)选择合适的学习资源和方法,3)动手实践和复习巩固,可以在这段时间内逐步掌握Python的基本知识和高级功能。

pythonlistsarepartofthestAndArdLibrary,herilearRaysarenot.listsarebuilt-In,多功能,和Rused ForStoringCollections,而EasaraySaraySaraySaraysaraySaraySaraysaraySaraysarrayModuleandleandleandlesscommonlyusedDduetolimitedFunctionalityFunctionalityFunctionality。

Python和C 各有优势,选择应基于项目需求。1)Python适合快速开发和数据处理,因其简洁语法和动态类型。2)C 适用于高性能和系统编程,因其静态类型和手动内存管理。

Python在自动化、脚本编写和任务管理中表现出色。1)自动化:通过标准库如os、shutil实现文件备份。2)脚本编写:使用psutil库监控系统资源。3)任务管理:利用schedule库调度任务。Python的易用性和丰富库支持使其在这些领域中成为首选工具。

Python在Web开发中的关键应用包括使用Django和Flask框架、API开发、数据分析与可视化、机器学习与AI、以及性能优化。1.Django和Flask框架:Django适合快速开发复杂应用,Flask适用于小型或高度自定义项目。2.API开发:使用Flask或DjangoRESTFramework构建RESTfulAPI。3.数据分析与可视化:利用Python处理数据并通过Web界面展示。4.机器学习与AI:Python用于构建智能Web应用。5.性能优化:通过异步编程、缓存和代码优
