mirror of
https://github.com/yiGmMk/wxocr.git
synced 2026-04-22 16:17:09 +08:00
add uv & page & testcase & docker
This commit is contained in:
yi_t
@@ -0,0 +1,3 @@
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.venv
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__pycache__
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temp/*
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@@ -0,0 +1 @@
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3.12
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@@ -11,6 +11,7 @@ COPY wcocr.cpython-312-x86_64-linux-gnu.so /app/wcocr.cpython-312-x86_64-linux-g
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COPY wx /app/wx
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COPY main.py /app/main.py
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COPY templates /app/templates
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WORKDIR /app
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@@ -0,0 +1,15 @@
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version: "3.8"
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services:
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wechat-ocr-api:
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build: .
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ports:
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- "5000:5000"
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container_name: wechat-ocr-api
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# 使用预构建的镜像
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# wechat-ocr-api:
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# image: golangboyme/wxocr
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# ports:
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# - "5000:5000"
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# container_name: wechat-ocr-api
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Binary file not shown.
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After Width: | Height: | Size: 65 KiB |
@@ -2,34 +2,39 @@ import wcocr
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import os
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import uuid
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import base64
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from flask import Flask, request, jsonify
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from flask import Flask, request, jsonify, render_template, send_from_directory
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app = Flask(__name__)
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wcocr.init("/app/wx/opt/wechat/wxocr", "/app/wx/opt/wechat")
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wcocr.init("./wx/opt/wechat/wxocr", "./wx/opt/wechat")
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@app.route('/ocr', methods=['POST'])
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@app.route("/ocr", methods=["POST"])
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def ocr():
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try:
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# Get base64 image from request
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image_data = request.json.get('image')
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image_data = request.json.get("image")
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if not image_data:
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return jsonify({'error': 'No image data provided'}), 400
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return jsonify({"error": "No image data provided"}), 400
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# Extract image type from base64 data
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image_type, base64_data = extract_image_type(image_data)
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if not image_type:
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return jsonify({"error": "Invalid base64 image data"}), 400
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# Create temp directory if not exists
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temp_dir = 'temp'
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temp_dir = "temp"
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if not os.path.exists(temp_dir):
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os.makedirs(temp_dir)
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# Generate unique filename and save image
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filename = os.path.join(temp_dir, f"{str(uuid.uuid4())}.png")
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filename = os.path.join(temp_dir, f"{str(uuid.uuid4())}.{image_type}")
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try:
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image_bytes = base64.b64decode(image_data)
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with open(filename, 'wb') as f:
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image_bytes = base64.b64decode(base64_data)
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with open(filename, "wb") as f:
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f.write(image_bytes)
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# Process image with OCR
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result = wcocr.ocr(filename)
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return jsonify({'result': result})
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return jsonify({"result": result})
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finally:
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# Clean up temp file
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@@ -37,7 +42,44 @@ def ocr():
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os.remove(filename)
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except Exception as e:
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return jsonify({'error': str(e)}), 500
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return jsonify({"error": str(e)}), 500
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if __name__ == '__main__':
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app.run(host='0.0.0.0', port=5000, threaded=True)
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# 创建静态文件夹
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static_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "static")
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if not os.path.exists(static_dir):
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os.makedirs(static_dir)
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def extract_image_type(base64_data):
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# Check if the base64 data has the expected prefix
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if base64_data.startswith("data:image/"):
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# Extract the image type from the prefix
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prefix_end = base64_data.find(";base64,")
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if prefix_end != -1:
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return (
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base64_data[len("data:image/") : prefix_end],
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base64_data.split(";base64,")[-1],
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)
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return "png", base64_data
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@app.route("/")
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def index():
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return render_template("index.html")
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if __name__ == "__main__":
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# 确保templates目录存在
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templates_dir = os.path.join(
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os.path.dirname(os.path.abspath(__file__)), "templates"
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)
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if not os.path.exists(templates_dir):
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os.makedirs(templates_dir)
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# 确保temp目录存在
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temp_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "temp")
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if not os.path.exists(temp_dir):
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os.makedirs(temp_dir)
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app.run(host="0.0.0.0", port=5000, threaded=True)
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@@ -0,0 +1,9 @@
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[project]
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name = "wxocr"
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version = "0.1.0"
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description = "Add your description here"
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readme = "README.md"
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requires-python = ">=3.11"
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dependencies = [
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"flask>=3.1.0",
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]
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@@ -0,0 +1,441 @@
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<!DOCTYPE html>
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<html lang="zh-CN">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>微信OCR文字识别工具</title>
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<style>
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:root {
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--primary-color: #07c160;
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--secondary-color: #576b95;
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}
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body {
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font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
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max-width: 1200px;
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margin: 0 auto;
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padding: 20px;
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background: #f5f5f5;
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color: #333;
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}
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.container {
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background: white;
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border-radius: 12px;
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padding: 30px;
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box-shadow: 0 2px 12px rgba(0, 0, 0, 0.1);
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}
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h1 {
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color: var(--primary-color);
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text-align: center;
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margin-bottom: 30px;
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}
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.upload-section {
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border: 2px dashed #ddd;
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border-radius: 8px;
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padding: 30px;
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text-align: center;
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margin: 20px 0;
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transition: all 0.3s;
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}
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.upload-section:hover {
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border-color: var(--primary-color);
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background: #f8fff9;
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}
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.preview-image {
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max-width: 100%;
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max-height: 400px;
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margin: 20px 0;
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border-radius: 8px;
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display: none;
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}
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.input-group {
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margin: 20px 0;
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}
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input[type="file"],
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input[type="text"] {
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width: 100%;
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padding: 12px;
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border: 1px solid #ddd;
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border-radius: 6px;
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margin: 10px 0;
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}
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button {
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background: var(--primary-color);
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color: white;
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border: none;
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padding: 12px 30px;
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border-radius: 6px;
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cursor: pointer;
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font-size: 16px;
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transition: all 0.3s;
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}
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button:hover {
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opacity: 0.9;
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transform: translateY(-1px);
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}
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.result-table {
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width: 100%;
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border-collapse: collapse;
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margin-top: 20px;
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display: none;
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}
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.result-table th,
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.result-table td {
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padding: 12px;
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border: 1px solid #eee;
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text-align: left;
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}
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.result-table th {
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background: var(--secondary-color);
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color: white;
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}
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.loading {
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display: none;
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text-align: center;
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color: var(--primary-color);
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margin: 20px 0;
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}
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.error {
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color: #ff4d4f;
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margin: 10px 0;
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display: none;
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}
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.image-container {
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position: relative;
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margin: 20px 0;
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display: inline-block;
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}
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.text-box {
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position: absolute;
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border: 2px solid var(--primary-color);
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background-color: rgba(7, 193, 96, 0.1);
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cursor: pointer;
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}
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.text-tooltip {
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position: absolute;
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background: white;
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border: 1px solid #ddd;
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padding: 8px;
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border-radius: 4px;
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box-shadow: 0 2px 8px rgba(0, 0, 0, 0.1);
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z-index: 100;
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display: none;
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max-width: 300px;
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word-break: break-word;
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}
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.confidence {
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color: var(--primary-color);
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font-weight: bold;
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}
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</style>
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</head>
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<body>
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<div class="container">
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<h1>微信OCR文字识别工具</h1>
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<!-- 上传区域 -->
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<div class="upload-section">
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<div class="input-group">
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<input type="file" id="fileInput" accept="image/*">
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<p>或拖拽图片到此区域</p>
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<input type="text" id="urlInput" placeholder="输入图片URL地址">
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</div>
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<button onclick="processImage()">开始识别</button>
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</div>
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<!-- 图片预览 -->
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<div class="image-container" id="imageContainer">
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<img id="preview" class="preview-image">
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<!-- 文本框将在这里动态添加 -->
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</div>
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<!-- 加载状态 -->
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<div class="loading" id="loading">识别中，请稍候...</div>
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<!-- 错误提示 -->
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<div class="error" id="error"></div>
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<!-- 结果显示 -->
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<table class="result-table" id="resultTable">
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<thead>
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<tr>
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<th>文本内容</th>
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<th>置信度</th>
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<th>位置信息 (左, 上, 右, 下)</th>
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</tr>
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</thead>
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<tbody id="resultBody"></tbody>
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</table>
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<!-- 使用说明 -->
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<h2>API接口说明</h2>
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<h3>请求方式</h3>
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<pre>POST /ocr</pre>
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<h3>请求示例</h3>
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<pre>
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{
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"image": "BASE64_ENCODED_IMAGE_DATA"
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}</pre>
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<h3>返回示例</h3>
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<pre id="responseSample"></pre>
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</div>
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<script>
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// 默认的API地址
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const API_ENDPOINT = window.location.origin + '/ocr';
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// 初始化拖放功能
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const uploadSection = document.querySelector('.upload-section');
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uploadSection.addEventListener('dragover', (e) => {
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e.preventDefault();
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uploadSection.style.backgroundColor = '#f0fff0';
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});
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uploadSection.addEventListener('drop', (e) => {
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e.preventDefault();
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uploadSection.style.backgroundColor = '';
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const file = e.dataTransfer.files[0];
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handleFile(file);
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});
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// 处理文件选择
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document.getElementById('fileInput').addEventListener('change', function (e) {
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handleFile(e.target.files[0]);
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});
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// 处理文件上传
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async function handleFile(file) {
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if (!file) return;
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if (!file.type.startsWith('image/')) {
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showError('请上传图片文件');
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return;
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}
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// 显示预览图片
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const reader = new FileReader();
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reader.onload = (e) => {
|
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document.getElementById('preview').src = e.target.result;
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document.getElementById('preview').style.display = 'block';
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|
||||
// 清除之前的文本框
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const imageContainer = document.getElementById('imageContainer');
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const existingBoxes = imageContainer.querySelectorAll('.text-box, .text-tooltip');
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existingBoxes.forEach(box => box.remove());
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||||
};
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reader.readAsDataURL(file);
|
||||
}
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|
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// 开始处理图像
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async function processImage() {
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const file = document.getElementById('fileInput').files[0];
|
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const url = document.getElementById('urlInput').value;
|
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let base64Data = '';
|
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try {
|
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showLoading();
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clearError();
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|
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if (file) {
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base64Data = await fileToBase64(file);
|
||||
} else if (url) {
|
||||
base64Data = await urlToBase64(url);
|
||||
} else {
|
||||
showError('请选择图片或输入图片URL');
|
||||
return;
|
||||
}
|
||||
|
||||
// 发送请求
|
||||
const response = await fetch(API_ENDPOINT, {
|
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method: 'POST',
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
},
|
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body: JSON.stringify({ image: base64Data })
|
||||
});
|
||||
|
||||
const data = await response.json();
|
||||
handleResponse(data);
|
||||
} catch (error) {
|
||||
showError(`请求失败：${error.message}`);
|
||||
} finally {
|
||||
hideLoading();
|
||||
}
|
||||
}
|
||||
|
||||
// 处理响应数据
|
||||
function handleResponse(data) {
|
||||
// 处理新的响应结构
|
||||
const resultData = data.result || data;
|
||||
|
||||
if (resultData.errcode !== 0) {
|
||||
showError(`识别失败，错误码：${resultData.errcode}`);
|
||||
return;
|
||||
}
|
||||
|
||||
// 显示结果表格
|
||||
const tbody = document.getElementById('resultBody');
|
||||
tbody.innerHTML = '';
|
||||
resultData.ocr_response.forEach(item => {
|
||||
const row = document.createElement('tr');
|
||||
row.innerHTML = `
|
||||
<td>${item.text}</td>
|
||||
<td>${(item.rate * 100).toFixed(2)}%</td>
|
||||
<td>(${item.left.toFixed(1)}, ${item.top.toFixed(1)},
|
||||
${item.right.toFixed(1)}, ${item.bottom.toFixed(1)})</td>
|
||||
`;
|
||||
tbody.appendChild(row);
|
||||
});
|
||||
document.getElementById('resultTable').style.display = 'table';
|
||||
|
||||
// 在图片上绘制识别区域
|
||||
drawTextBoxes(resultData.ocr_response, resultData.width, resultData.height);
|
||||
}
|
||||
|
||||
// 在图片上绘制文本框
|
||||
function drawTextBoxes(ocrResults, originalWidth, originalHeight) {
|
||||
const imageContainer = document.getElementById('imageContainer');
|
||||
const preview = document.getElementById('preview');
|
||||
|
||||
// 清除之前的文本框
|
||||
const existingBoxes = imageContainer.querySelectorAll('.text-box, .text-tooltip');
|
||||
existingBoxes.forEach(box => box.remove());
|
||||
|
||||
// 获取图片的实际显示尺寸和位置
|
||||
const imgRect = preview.getBoundingClientRect();
|
||||
const containerRect = imageContainer.getBoundingClientRect();
|
||||
|
||||
// 计算图片相对于容器的偏移
|
||||
const offsetX = imgRect.left - containerRect.left;
|
||||
const offsetY = imgRect.top - containerRect.top;
|
||||
|
||||
// 计算缩放比例
|
||||
const scaleX = imgRect.width / originalWidth;
|
||||
const scaleY = imgRect.height / originalHeight;
|
||||
|
||||
// 为每个识别结果创建文本框
|
||||
ocrResults.forEach((item, index) => {
|
||||
// 创建文本框
|
||||
const textBox = document.createElement('div');
|
||||
textBox.className = 'text-box';
|
||||
|
||||
// 精确定位文本框，考虑图片在容器中的偏移
|
||||
const left = item.left * scaleX + offsetX;
|
||||
const top = item.top * scaleY + offsetY;
|
||||
const width = (item.right - item.left) * scaleX;
|
||||
const height = (item.bottom - item.top) * scaleY;
|
||||
|
||||
// 设置文本框位置和大小
|
||||
textBox.style.left = `${left}px`;
|
||||
textBox.style.top = `${top}px`;
|
||||
textBox.style.width = `${width}px`;
|
||||
textBox.style.height = `${height}px`;
|
||||
textBox.dataset.index = index;
|
||||
|
||||
// 创建提示框
|
||||
const tooltip = document.createElement('div');
|
||||
tooltip.className = 'text-tooltip';
|
||||
tooltip.innerHTML = `
|
||||
<div>${item.text}</div>
|
||||
<div class="confidence">置信度: ${(item.rate * 100).toFixed(2)}%</div>
|
||||
`;
|
||||
|
||||
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[[package]]
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wheels = [
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[[package]]
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name = "wxocr"
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version = "0.1.0"
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source = { virtual = "." }
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dependencies = [
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{ name = "flask" },
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]
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[package.metadata]
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requires-dist = [{ name = "flask", specifier = ">=3.1.0" }]
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Reference in New Issue
Block a user