m

ocr/find_paragraph.py

@@ -1,56 +0,0 @@
-"""
-Find paragraphs in loader response
-"""
-import time
-from selenium import webdriver
-from selenium.webdriver.common.by import By
-from selenium.webdriver.support import expected_conditions as EC
-from selenium.webdriver.support.ui import WebDriverWait
-import json
-
-options = webdriver.ChromeOptions()
-options.add_argument("--headless=new")
-options.add_argument("--no-sandbox")
-options.add_argument("--disable-dev-shm-usage")
-options.add_argument("--disable-blink-features=AutomationControlled")
-options.add_argument("user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36")
-options.add_experimental_option("excludeSwitches", ["enable-automation"])
-options.add_experimental_option("useAutomationExtension", False)
-options.set_capability("goog:loggingPrefs", {"performance": "ALL"})
-driver = webdriver.Chrome(options=options)
-driver.set_window_size(1400, 900)
-driver.execute_cdp_cmd("Network.enable", {})
-
-driver.get("https://www.shidianguji.com/zh/book/SWX0005")
-time.sleep(5)
-wait = WebDriverWait(driver, 10)
-try:
-    els = driver.find_elements(By.XPATH, "//*[contains(text(), '第二回')]")
-    if els:
-        print("Clicking 第二回")
-        driver.execute_script("arguments[0].click();", els[0])
-except Exception as e:
-    pass
-
-time.sleep(5)
-logs = driver.get_log("performance")
-for entry in logs:
-    msg = json.loads(entry["message"])["message"]
-    method = msg.get("method")
-    if method == "Network.responseReceived":
-        req_id = msg.get("params", {}).get("requestId")
-        url = msg.get("params", {}).get("response", {}).get("url", "")
-        if "__loader=__session" in url:
-            try:
-                body = driver.execute_cdp_cmd("Network.getResponseBody", {"requestId": req_id})
-                data = json.loads(body.get("body", ""))
-                print("Loader Response JSON keys:", list(data.keys()))
-                if "paragraphList" in data:
-                    print("Found paragraphList in Loader!")
-                    print("Type:", type(data["paragraphList"]))
-                    if isinstance(data["paragraphList"], list) and len(data["paragraphList"]) > 0:
-                        print("First item keys:", list(data["paragraphList"][0].keys()))
-                        print("First item content snippet:", str(data["paragraphList"][0])[:150])
-            except:
-                pass
-driver.quit()

ocr/getdata.py

@@ -376,7 +376,7 @@ def _enter_image_mode(driver: webdriver.Chrome) -> None:
 def main() -> int:
     ap = argparse.ArgumentParser()
     ap.add_argument("--book-id", default="TPM0001")
-    ap.add_argument("--chapter", default="新刻金瓶梅詞話卷之一_第二回")
+    ap.add_argument("--chapter", default="新刻金瓶梅詞話卷之一_第三回")
     ap.add_argument("--out", default="out")
     ap.add_argument("--headless", action="store_true")
     ap.add_argument("--timeout", type=int, default=20000)

ocr/kandianguji_ocr.py

@@ -1,98 +0,0 @@
-import os
-import base64
-import json
-import requests
-
-API_URL = "https://ocr.kandianguji.com/ocr_api"
-TOKEN = "8be3d75a-8a66-4b5d-9047-d4895e4c8000"
-EMAIL = "13788325535"
-
-def get_kandianguji_ocr_result(image_path: str, **kwargs):
-    """
-    调用看典古籍OCR API识别图像。
-    
-    参数:
-        image_path (str): 需要识别的图像文件路径。
-        **kwargs: 其他可选API参数 (如 version='v2', det_layout=True 等)。
-                  详细参数请参考 readme.txt 文档。
-                  
-    返回:
-        dict: API响应的JSON数据。
-    """
-    if not os.path.exists(image_path):
-        raise FileNotFoundError(f"指定的图像文件不存在: {image_path}")
-
-    # 1. 将图像转换为base64编码
-    with open(image_path, "rb") as img_file:
-        image_base64 = base64.b64encode(img_file.read()).decode('utf-8')
-
-    # 2. 构建基础请求数据
-    payload = {
-        "token": TOKEN,
-        "email": EMAIL,
-        "image": image_base64
-    }
-    
-    # 3. 合并可选参数
-    # 根据 readme.txt 提取的默认可选参数
-    default_options = {
-        "char_ocr": True,
-        "det_mode": "sp",
-        "det_layout": True,
-        "image_size": 0,
-        "return_position": True,
-        "return_choices": True,
-        "version": "v2",
-        "only_plain_text": False,
-        "return_layout": True,
-        "auto_insert_space": False,
-        "hp_line_words_angel": "left2right",
-        "sp_line_words_angel": "top2bottom"
-    }
-    
-    # 更新默认参数（如果 kwargs 中提供了新的值）
-    for key, value in default_options.items():
-        payload[key] = kwargs.get(key, value)
-
-    # 4. 发送POST请求
-    headers = {
-        "Content-Type": "application/json"
-    }
-    
-    try:
-        response = requests.post(API_URL, json=payload, headers=headers)
-        # 检查响应状态码
-        response.raise_for_status()
-        return response.json()
-    except requests.exceptions.RequestException as e:
-        print(f"API请求失败: {e}")
-        # 如果有响应体，打印出来以便调试
-        if 'response' in locals() and response is not None:
-            print(f"响应内容: {response.text}")
-        return None
-
-if __name__ == "__main__":
-    # 测试用例 (请替换为真实的古籍图片路径)
-    sample_image = "0375.jpg"
-    
-    print("看典古籍OCR API 接口测试")
-    print("-" * 30)
-    
-    if os.path.exists(sample_image):
-        print(f"正在识别图片: {sample_image}")
-        # 调用示例，可按需传入 v2 版本的特有参数
-        result = get_kandianguji_ocr_result(
-            sample_image, 
-            version="v2", 
-            det_layout=True
-        )
-        
-        if result:
-            print("\n识别结果:")
-            s = json.dumps(result, indent=2, ensure_ascii=False)
-            with open("out.json", 'w', encoding='utf-8') as f:
-                f.write(s)  
-            print(s)
-    else:
-        print(f"提示: 请在当前目录下放置一张名为 '{sample_image}' 的图片用于测试。")
-        print("或者修改 sample_image 变量的值指向有效的图片路径。")

ocr/ppcor_aliocr_convert.py

@@ -1,501 +0,0 @@
-"""
-# pip install numpy==1.23.5 opencv-python==4.10.0.84 -i https://mirrors.aliyun.com/pypi/simple/
-
-PPOCRLabel --lang ch  # 启动标注工具
-
-cp autodl-tmp/train_data.zip . && \
-unzip train_data.zip -d PaddleOCR
-
-# https://github.com/PaddlePaddle/PaddleOCR/blob/static/doc/doc_ch/FAQ.md
-
-# 7za a -t7z -m0=lzma -mx=9 -mfb=64 -md=32m -ms=on data.7z data/
-
-新建文件夹 train_data, 要标注的图片全部放在里面
-
-新建 train_data/Label.txt 内容如下
-行中用 \t 分隔 , points 的标记顺序是 左上 右上  右下 左下
-train_data/0093.bmp	[{"transcription":"参考答案及解析","points":[[525,179],[1295,167],[1295,268],[521,292]],"difficult":false}]
-train_data/0094.bmp	[{"transcription":"其他内容","points":[[525,179],[1295,167],[1295,268],[521,292]],"difficult":false}]
-
-
-给 PaddleOCR 用，前面是坐标和图片都变换；这里图像不变，坐标不变
-
-
-将阿里OCR 的识别结果（图片和标注）转换成 icdar2015 格式 (注意：它的文本是含 utf8 bom 的)
-
-给 mmocr 训练用。格式是 icdar2015 的格式，文件夹的组织方式是按照 mmocr 的要求创建的
-
-"""
-
-
-"""
-
-! unzip ./GD500.zip -d DB/datasets
-
-icdar2015 文本检测数据集
-标注格式: x1,y1,x2,y2,x3,y3,x4,y4,text
-
-其中, x1,y1为左上角坐标,x2,y2为右上角坐标,x3,y3为右下角坐标,x4,y4为左下角坐标。
-
-# 表示text难以辨认。
-"""
-
-import random
-from pathlib import Path
-import os
-import shutil
-import glob
-import base64
-from importlib.resources import path
-import math
-import numpy as np
-import cv2
-import json
-import decimal
-import datetime
-from pickletools import uint8
-class DecimalEncoder(json.JSONEncoder):
-    def default(self, o):
-        if isinstance(o, decimal.Decimal):
-            return float(o)
-        elif isinstance(o, datetime.datetime):
-            return str(o)
-        super(DecimalEncoder, self).default(o)
-
-
-def save_json(filename, dics):
-    with open(filename, 'w', encoding='utf-8') as fp:
-        json.dump(dics, fp, indent=4, cls=DecimalEncoder, ensure_ascii=False)
-        fp.close()
-
-
-def load_json(filename):
-    with open(filename, encoding='utf-8') as fp:
-        js = json.load(fp)
-        fp.close()
-        return js
-
-# convert string to json
-
-
-def parse(s):
-    return json.loads(s, strict=False)
-
-# convert dict to string
-
-
-def string(d):
-    return json.dumps(d, cls=DecimalEncoder, ensure_ascii=False)
-
-
-def transform(points, M):
-    # points 算出四个点变换后移动到哪里了
-    # points = np.array([[word_x,  word_y],              # 左上
-    #                    [word_x + word_width, word_y],                 # 右上
-    #                    [word_x + word_width, word_y + word_height],  # 右下
-    #                    [word_x, word_y + word_height],  # 左下
-    #                    ])
-    # add ones
-    ones = np.ones(shape=(len(points), 1))
-
-    points_ones = np.hstack([points, ones])
-
-    # transform points
-    transformed_points = M.dot(points_ones.T).T
-
-    transformed_points_int = np.round(
-        transformed_points, decimals=0).astype(np.int32)  # 批量四舍五入
-
-    return transformed_points_int
-
-
-def cutPoly(img, pts):
-    # img = cv2.imdecode(np.fromfile('./t.png', dtype=np.uint8), -1)
-    # pts = np.array([[10,150],[150,100],[300,150],[350,100],[310,20],[35,10]])
-
-    # (1) Crop the bounding rect
-    rect = cv2.boundingRect(pts)
-    x, y, w, h = rect
-    croped = img[y:y+h, x:x+w].copy()
-
-    # (2) make mask
-    pts = pts - pts.min(axis=0)
-
-    mask = np.zeros(croped.shape[:2], np.uint8)
-    cv2.drawContours(mask, [pts], -1, (255, 255, 255), -1, cv2.LINE_AA)
-
-    # (3) do bit-op
-    dst = cv2.bitwise_and(croped, croped, mask=mask)
-
-    # (4) add the white background
-    bg = np.ones_like(croped, np.uint8)*255
-    cv2.bitwise_not(bg, bg, mask=mask)
-    dst2 = bg + dst
-
-    # cv2.imwrite("croped.png", croped)
-    # cv2.imwrite("mask.png", mask)
-    # cv2.imwrite("dst.png", dst)
-    # cv2.imwrite("dst2.png", dst2)
-
-    return dst2
-
-
-def md5(fname):
-    import hashlib
-    hash_md5 = hashlib.md5()
-    with open(fname, "rb") as f:
-        for chunk in iter(lambda: f.read(4096), b""):
-            hash_md5.update(chunk)
-    return hash_md5.hexdigest()
-
-def get_all_md5():
-    m5s = []
-    import glob
-    jpgs = glob.glob('./bookimage/**/*.jpg', recursive=True)
-    for jpg in jpgs:
-       m5 = md5(jpg)
-       m5s.append( m5 )
-    return m5s
-
-def get_json_paths():
-    json_paths = []
-    m5s = get_all_md5()
-    for m5 in m5s:
-        j_pth = f"/yingedu/project/ocr_server_test/data/json/{m5.lower()}.json"
-        if os.path.exists(j_pth):
-            json_paths.append(j_pth)
-    return json_paths
-
-def do_convert_aliocr():
-
-    # 24HLZYZG64/0001.jpg c04d111ef69b9892d39f9430b6906047 md5是这个
-        # ls /yingedu/project/ocr_server_test/data/json/0bf0383ece9a533683e615bf57525812.json  aliocr 原始识别结果
-        # ls /yingedu/project/ocr_server_test/data/img/0bf0383ece9a533683e615bf57525812.txt    aliocr 原始识别图像（二值化降燥后压缩成2M）
-
-
-
-    root = 'train_data'
-    tmp = 'tmp'
-    label_path = os.path.join(root, 'Label.txt')
-    
-    key_path = os.path.join(root, 'keys.txt')
-
-    fileState_path = os.path.join(root, 'fileState.txt')
-
-    if os.path.exists(root):
-        shutil.rmtree(root)
-        # os.rmdir(root)
-    if os.path.exists(tmp):
-        shutil.rmtree(tmp)
-
-    if not os.path.exists(root):
-        os.makedirs(root)
-
-    if not os.path.exists(tmp):
-        os.makedirs(tmp)
-
-    label = ''
-    keys = ''
-    states = ''
-
-
-    dic_words = {}  # 所有词
-
-    # 开始转换
-
-    # https://help.aliyun.com/document_detail/294540.html 阿里云ocr结果字段定义
-    # prism-wordsInfo 里的 angle 文字块的角度，这个角度只影响width和height，当角度为-90、90、-270、270，width和height的值需要自行互换
-
-    dir_json = './测试图片/json' # '/yingedu/project/ocr_server_test/data/json' # './data/json'  
-    dir_img = './测试图片/img' # '/yingedu/project/ocr_server_test/data/img'        # './data/img'  
-
-    g_count = 1
-    g_count2 = 1
-
-
-    json_paths = glob.glob('{}/*.json'.format(dir_json), recursive=False)
-
-
-    #json_paths = get_json_paths()
-
-    for json_path in json_paths:
-
-        arr = []
-
-        base = Path(json_path).stem
-
-        # if base == '0bf0383ece9a533683e615bf57525812':
-        #     continue
-
-        img_path = os.path.join(dir_img, '{}.txt'.format(base))
-
-        if not os.path.exists(img_path):  # 没有相应的图片，可能被删除了
-            print(f'Warnnig: no image {img_path}')
-            continue
-
-        jsn = load_json(json_path)
-
-        if not ('prism_wordsInfo' in jsn):
-            print(f'Warning: no charater in {img_path}')
-            continue
-
-        with open(img_path, "r", encoding="utf-8") as fp:
-            imgdata = fp.read()
-            imgdata = base64.b64decode(imgdata)
-            imgdata = np.frombuffer(imgdata, np.uint8)
-            img = cv2.imdecode(imgdata, cv2.IMREAD_UNCHANGED)
-
-            # cv2.imshow('img', img)
-            # cv2.waitKey(0)
-
-        if len(img.shape) != 3:  # 转彩图
-            img_color = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
-            img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)  # DBNet 原版只能处理彩图，这里转一下
-
-        else:
-            img_color = img.copy()
-
-        img_color_origin = img_color.copy()
-        img_color_origin2 = img_color.copy()
-
-        name = f'{g_count}.jpg'
-        dst_img_path = f'{root}/{name}'
-        states += f'G:\\train_data\\{name}\t1\n'
-        g_count += 1
-
-        cv2.imwrite(dst_img_path, img)
-
-        wordsInfo = jsn['prism_wordsInfo']
-        for j in range(len(wordsInfo)):
-            jo = wordsInfo[j]
-            word = jo["word"]
-
-            for w in list(word):
-                if not (w in dic_words):
-                    dic_words[w] = True
-
-            # prism-wordsInfo 里的 angle 文字块的角度，这个角度只影响width和height，当角度为-90、90、-270、270，width和height的值需要自行互换
-            angle = jo['angle']
-
-            img_color = img_color_origin.copy()
-
-            word_x = jo['x']
-            word_y = jo['y']
-            word_width = jo['width']
-            word_height = jo['height']
-
-            if abs(angle) == 90 or abs(angle) == 270:
-                word_width = jo['height']
-                word_height = jo['width']
-            elif angle != 0:
-
-                # 变换前画出绿框，方便追踪点的前后变化
-                # img_color = cv2.rectangle(img_color, (word_x, word_y), (
-                #     word_x + word_width, word_y + word_height), (0, 255, 0), 2)  # 矩形的左上角, 矩形的右下角
-
-                # cv2.imshow("green", img_color)
-                # cv2.waitKey(0)
-
-                # 变换前的多边形蓝框
-                points = np.array([
-                    [word_x,  word_y],                             # 左上
-                    [word_x + word_width, word_y],                 # 右上
-                    [word_x + word_width, word_y + word_height],  # 右下
-                    [word_x, word_y + word_height],                # 左下
-                ])
-
-                # # cv2.fillPoly(img_color, pts=[points], color=(255, 0, 0)) # 填充
-                # cv2.polylines(img_color, [points], isClosed=True, color=(
-                #     255, 0, 0), thickness=1)  # 只画线，不填充
-
-                # cv2.imshow("polys", img_color)
-                # cv2.waitKey(0)
-
-                # 获取图像的维度，并计算中心
-                (h, w) = img_color.shape[:2]
-                (cX, cY) = (w // 2, h // 2)
-
-                # - (cX,cY): 旋转的中心点坐标
-                # - 180: 旋转的度数，正度数表示逆时针旋转，而负度数表示顺时针旋转。
-                # - 1.0：旋转后图像的大小，1.0原图，2.0变成原来的2倍，0.5变成原来的0.5倍
-                # 1° = π/180弧度   1 弧度 =  180 / 3.1415926   // 0.0190033 是Mathematica 算出来的弧度，先转换成角度  // -0.0190033 * (180 / 3.1415926)
-                M = cv2.getRotationMatrix2D((cX, cY), angle, 1.0)
-                img_color = cv2.warpAffine(img_color, M, (w, h))
-                img_color_transform = img_color.copy()
-
-                # cv2.imshow("after trans", img_color)
-                # cv2.waitKey(0)
-
-                # https://docs.opencv.org/2.4/doc/tutorials/imgproc/imgtrans/warp_affine/warp_affine.html  # 原理
-                # https://stackoverflow.com/questions/30327659/how-can-i-remap-a-point-after-an-image-rotation # How can I remap a point after an image rotation?
-                # 如何得到移动后的坐标点
-
-                # points 算出四个点变换后移动到哪里了
-                points = np.array([[word_x,  word_y],              # 左上
-                                   # 右上
-                                   [word_x + word_width, word_y],
-                                   [word_x + word_width, word_y + \
-                                       word_height],  # 右下
-                                   [word_x, word_y + word_height],  # 左下
-                                   ])
-                # add ones
-                ones = np.ones(shape=(len(points), 1))
-
-                points_ones = np.hstack([points, ones])
-
-                # transform points
-                transformed_points = M.dot(points_ones.T).T
-
-                transformed_points_int = np.round(
-                    transformed_points, decimals=0).astype(np.int32)  # 批量四舍五入
-
-                # cv2.polylines(img_color, [transformed_points_int], isClosed=True, color=(
-                #     0, 0, 255), thickness=2)  # 画转换后的点
-
-                # cv2.polylines(img_color_origin, [points], isClosed=True, color=(
-                #     random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)), thickness=2)  # 画转换前的点
-
-                # cv2.imshow("orgin", img_color_origin)
-                # cv2.waitKey(0)
-
-            # 四个角的位置 # 左上、右上、右下、左下，当NeedRotate为true时，如果最外层的angle不为0，需要按照angle矫正图片后，坐标才准确（错，经验证不需要）
-            pos = jo["pos"]
-            x = int(pos[0]["x"])  # 左上
-            y = int(pos[0]["y"])
-
-            x2 = int(pos[2]["x"])  # 右下
-            y2 = int(pos[2]["y"])
-
-            lu = [pos[0]['x'], pos[0]['y']]  # left up  四个角顺时针方向数
-            ru = [pos[1]['x'], pos[1]['y']]
-            rd = [pos[2]['x'], pos[2]['y']]
-            ld = [pos[3]['x'], pos[3]['y']]
-
-            # 生成 icdar2015 格式的人工标记训练数据（用于训练 mmocr）
-            # gt_txt_list.append( "{},{},{},{},{},{},{},{},{}".format(lu[0], lu[1], ru[0], ru[1], rd[0], rd[1], ld[0], ld[1], word) )
-
-            # 绘制矩形
-            start_point = (x, y)  # 矩形的左上角
-
-            end_point = (x2, y2)  # 矩形的右下角
-
-            color = (0, 0, 255)  # BGR
-
-            thickness = 2
-
-            # 逐行画框
-            # img_color_origin2 = cv2.rectangle(img_color_origin2, start_point, end_point, color, thickness)
-            # cv2.imshow("box", img_color_origin2)
-
-            # cv2.waitKey(0)
-
-            points = [lu, ru, rd, ld]
-
-            points0 = np.array([[word_x,  word_y],              # 左上
-                                  # 右上
-                                  [word_x + word_width, word_y],
-                                    [word_x + word_width, word_y + \
-                                     word_height],  # 右下
-                                    [word_x, word_y + word_height],  # 左下
-                                  ])
-            points1 = np.array([lu, ru, rd, ld])
-
-            if not (abs(angle) == 90 or abs(angle) == 270) and angle != 0:
-                points = transform(points, M)
-            else:
-                points = np.array(points)
-
-            ps3 = np.array(
-                [
-                    [min(points[0][0], points1[0][0]), min(
-                        points[0][1], points1[0][1])],  # 左上(取最两者中最小的)
-
-                    [max(points[1][0], points1[1][0]), min(
-                        points[1][1], points1[1][1])],  # 右上
-
-                    [max(points[2][0], points1[2][0]), max(
-                        points[2][1], points1[2][1])],  # 右下
-
-                    [min(points[3][0], points1[3][0]), max(
-                        points[3][1], points1[3][1])]  # 左下
-                ]
-            )
-
-            # img_cuted = cutPoly(img, points1)
-            # cv2.imwrite(f'./tmp/{g_count2}.jpg', img_cuted)
-            # with open(f'./tmp/{g_count2}.txt', 'w', encoding='utf-8') as f:
-            #     f.write(word)
-            # g_count2 += 1
-
-            # cv2.polylines(img_color_origin, [points], isClosed=True, color=(   # 多边形，框得比较全
-            #     100, 0, 255), thickness=2)  # 只画线，不填充
-
-            
-            arr.append( {"transcription":f"{word}","points":[lu, ru, rd, ld],"difficult":False} )
-
-
-            cv2.polylines(img_color_origin, [points1], isClosed=True, color=(
-                random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)), thickness=2)  # 画转换前的点
-
-            # cv2.polylines(img_color_origin, [ps3], isClosed=True, color=(255, 0, 0), thickness=2)
-
-            # cv2.imshow("orgin", img_color_origin)
-            # cv2.waitKey(0)
-
-            # break
-
-
-        arr_str = string(arr)
-        line = f'{dst_img_path}\t{arr_str}\n'
-        label += line
-
-        print( f'{g_count - 1} / {len(json_paths)} task done.' )
-
-    ks = list( dic_words.keys() )
-
-    keys = '\n'.join(ks)
-
-    with open(label_path, "w", encoding='utf-8') as fp:
-        fp.write(label)
-
-    with open(key_path, "w", encoding='utf-8') as fp:
-        fp.write(keys)
-
-    with open(fileState_path, "w", encoding='utf-8') as fp:
-        fp.write(states)
-
-    print('all task done.')
-
-def show_box_shidianguji(pth_img):
-    imgData = np.fromfile(pth_img, dtype=np.uint8)
-    img = cv2.imdecode(imgData, cv2.IMREAD_UNCHANGED)
-    
-    if len(img.shape) != 3:  # 转彩图
-        img_color = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
-        img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)  # DBNet 原版只能处理彩图，这里转一下
-    else:
-        img_color = img.copy()
-
-
-    charPoly = {
-        "x0": 537, "y0": 67, "x1": 578, "y1": 66, "x2": 578, "y2": 122, "x3": 537,"y3": 123
-    }
-    
-    lu = [charPoly["x0"], charPoly["y0"]]
-    ru = [charPoly["x1"], charPoly["y1"]]
-    rd = [charPoly["x2"], charPoly["y2"]]
-    ld = [charPoly["x3"], charPoly["y3"]]
-    points = np.array([lu, ru, rd, ld])
-
-    cv2.polylines(img_color, [points], isClosed=True, color=(  # 多边形，框得比较全
-        100, 0, 255), thickness=2)  # 只画线，不填充
-
-    cv2.imshow("box", img_color)
-    cv2.waitKey(0)
-
-    pass
-
-if __name__ == "__main__":
-    # do_convert_aliocr()
-    show_box_shidianguji("out/images/0000_1kzz90e16h5ic.webp")
-    pass

ocr/qwen_ocr.py

@@ -1,43 +0,0 @@
-# 如果没有安装OpenAI Python SDK，可使用命令安装：pip install OpenAI
-
-import base64
-from openai import OpenAI
-
-# 读取图片并转为 base64
-image_path = "0375.jpg"
-with open(image_path, "rb") as f:
-    image_data = base64.b64encode(f.read()).decode("utf-8")
-
-# 初始化客户端
-client = OpenAI(
-    base_url="https://www.autodl.art/api/v1",
-    api_key="7bPZpONEQgxsP7kniD1AzmP3Tr0e84nmfExGroS1v11CBUKi",
-)
-
-# 调用接口（这里使用了stream=True进行流式响应）
-stream = client.chat.completions.create(
-    model="qwen3.6-plus",
-    messages=[
-        {
-            "role": "user",
-            "content": [
-                {
-                    "type": "text",
-                    "text": "提取图片中的文字，按阅读顺序输出"
-                },
-                {
-                    "type": "image_url",
-                    "image_url": {
-                        "url": f"data:image/jpeg;base64,{image_data}"
-                    }
-                }
-            ]
-        }
-    ],
-    stream=True,
-)
-
-# 流式打印输出
-for chunk in stream:
-    if chunk.choices and chunk.choices[0].delta.content:
-        print(chunk.choices[0].delta.content, end="")