查看配置：
win10系统+Python 3.9.13 + NVIDIA GeForce RTX 3080 Ti
安装环境：
1）下载 CUDA Toolkit 12.6
2）安装 CUDA Toolkit
查看是否安装成功
nvcc --version
3）安装 PaddlePaddle GPU 版本（配合 CUDA 12.6）
PaddlePaddle安装指令
查看 PaddlePaddle 版本：
import paddle
print(paddle.__version__)
4）安装paddleocr
虚拟环境安装：
# 创建并激活虚拟环境
python -m venv venv
venv\Scripts\activate
# 安装 paddleocr 到当前虚拟环境中
pip install paddleocr
# 或者指定国内镜像
pip install paddleocr -i https://pypi.tuna.tsinghua.edu.cn/simple
查看paddleocr版本：
import paddleocr
print(paddleocr.__version__)
版本错误可安装指定版本：
pip install paddleocr==2.10.0
目录
TestPaddle/
├── ocr_server.py
├── ch_PP-OCRv4_det_server_infer
| ├── inference.pdiparams
| ├── inference.pdiparams.info
| └── inference.pdmodel
├── ch_PP-OCRv4_rec_server_infer
| ├── inference.pdiparams
| ├── inference.pdiparams.info
| └── inference.pdmodel
└── cls
| ├── inference.pdiparams
| ├── inference.pdiparams.info
| └── inference.pdmodel
└── 001.jpg
ocr_server.py:
from flask import Flask, request, jsonify
from paddleocr import PaddleOCR
import cv2
import os
import time
import numpy as np
import json
from datetime import datetime
import requests
import urllib.parse
# 测试命令：curl "http://localhost:8082/image-ocr?templateCode=23&path=001.jpg"
# 现场 虚拟环境激活 py38\Scripts\activate
app = Flask(__name__)
# # 获取当前脚本所在目录作为基础路径
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
# 初始化 OCR 引擎（使用本地模型路径）
ocr_engine = PaddleOCR(
use_angle_cls=False,
lang="ch",
det_model_dir=os.path.join(BASE_DIR, 'ch_PP-OCRv4_det_server_infer'),
rec_model_dir=os.path.join(BASE_DIR, 'ch_PP-OCRv4_rec_server_infer'),
cls_model_dir=os.path.join(BASE_DIR, 'cls'),
use_gpu=True,
use_pdserving=False,
det_limit_side_len=3264,
det_db_thresh=0.8,
det_db_box_thresh=0.6,
det_db_unclip_ratio=3,
rec_image_shape="3, 48, 64",
e2e_limit_side_len=3264,
e2e_pgnet_score_thresh=0.0001,
download_model=False
)
@app.route('/image-ocr', methods=['GET'])
def image_ocr():
try:
# 获取查询参数
template_code = request.args.get('templateCode')
image_path = request.args.get('path')
# 检查参数是否为空
if not image_path:
return jsonify({
'Status': 'Error',
'Message': 'Missing required parameter: path',
'ReceivedAt': get_current_time_with_ms()
}), 400
# 可选：打印 templateCode（但不参与 OCR 处理）
print(f"Template Code: {template_code}")
# # 检查图像是否存在
# if not os.path.exists(image_path):
# return jsonify({
# 'Status': 'Error',
# 'Message': f'Image file does not exist: {image_path}',
# 'ReceivedAt': get_current_time_with_ms()
# }), 400
# 读取图像
# img = cv2.imread(image_path)
img = read_image(image_path)
if img is None:
return jsonify({
'Status': 'Error',
'Message': 'Failed to load image.',
'ReceivedAt': get_current_time_with_ms()
}), 400
# 设置边缘裁剪距离
gap = 10
closingPix = 5
gray_image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
height, width = gray_image.shape[:2]
# 设置边缘10个像素为255
height, width = gray_image.shape[:2]
gray_image[:, :gap] = 255
gray_image[:, width - 10:] = 255
gray_image[:gap, :] = 255
gray_image[height - gap:, :] = 255
# 二值化处理
_, binary = cv2.threshold(gray_image, 254, 255, cv2.THRESH_BINARY_INV)
# 先膨胀5次，再腐蚀5次
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
morphed = cv2.dilate(binary, kernel, iterations=closingPix)
morphed = cv2.erode(morphed, kernel, iterations=closingPix)
# 寻找连通域
num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(morphed, connectivity=8)
# 收集所有灰度为 0 的区域对应的矩形（排除背景）
rectangles = []
for i in range(1, num_labels): # 从1开始跳过背景
x, y, w, h, _ = stats[i]
rectangles.append((x, y, w, h))
# print("Rectangle", i)
# 按照先 y（行），再 x（列）排序
merged_rects_sorted = sorted(rectangles, key=lambda r: (r[1], r[0]))
sum_message = ""
final_message_parts = []
for idx, rect in enumerate(merged_rects_sorted, start=1):
x, y, w, h = rect
# 创建一个全白的图像（与原图大小一致）
mask = np.ones_like(img) * 255 # 灰度图为 255 的白色背景图像
# 将 rect 区域替换为原图中的内容
mask[y:y + h, x:x + w] = img[y:y + h, x:x + w]
# img = mask
# cv2.imwrite("test Rectangle"+str(idx)+".jpeg", mask)
print(f"Rectangle {idx}: x={x}, y={y}, w={w}, h={h}")
# 执行 OCR
result = ocr_engine.ocr(mask, cls=False)
# 格式化结果
message_lines = []
for line in result:
if line is not None:
for word_info in line:
text = word_info[1][0]
coords = word_info[0]
coord_str = ",".join([f"({int(x)},{int(y)})" for x, y in coords])
message_lines.append(f"{text}:{coord_str}")
message = "Rectangle"+str(idx)+"{" + ";".join(message_lines) + "}"
sum_message = sum_message + message
return jsonify({
'Status': 'Success',
'Message': sum_message,
'ReceivedAt': get_current_time_with_ms()
})
except Exception as e:
return jsonify({
'Status': 'Error',
'Message': str(e),
'ReceivedAt': get_current_time_with_ms()
}), 500
def get_current_time_with_ms():
"""返回当前系统时间，格式为 YYYY-MM-DD HH:MM:SS.sss"""
return time.strftime('%Y-%m-%d %H:%M:%S.') + f"{int(time.time() * 1000) % 1000:03d}"
def read_image(image_source):
"""
读取图像，支持 HTTP URL 和本地路径
:param image_source: 图像地址，可以是 URL 或本地路径
:return: OpenCV 图像对象，失败返回 None
"""
if urllib.parse.urlparse(image_source).scheme in ('http', 'https'):
# 是网络URL，使用 requests 下载
try:
response = requests.get(image_source, timeout=10)
response.raise_for_status()
image_array = np.frombuffer(response.content, dtype=np.uint8)
image = cv2.imdecode(image_array, cv2.IMREAD_COLOR)
except Exception as e:
print(f"读取网络图像失败: {e}")
return None
else:
# 当作本地路径处理
if os.path.exists(image_source):
image = cv2.imread(image_source)
else:
print(f"本地路径不存在: {image_source}")
return None
return image
if __name__ == '__main__':
app.run(host='0.0.0.0', port=8082, threaded=True)
测试：
