add v6 api

main_v6.py

@@ -9,6 +9,9 @@
 
 """
 
+.venv/Scripts/python post.py
+    测试接口
+
 curl -LsSf https://astral.sh/uv/install.sh | sh
 	# On Windows.
 powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
@@ -31,40 +34,239 @@ $env:UV_DEFAULT_INDEX="https://pypi.tuna.tsinghua.edu.cn/simple"
 .venv/Scripts/pip install paddlepaddle -i https://www.paddlepaddle.org.cn/packages/stable/cpu/
     .venv/bin/pip install paddlepaddle -i https://www.paddlepaddle.org.cn/packages/stable/cpu/
 
+.venv/Scripts/pip install flask
+
 """
 
-if __name__ == '__main__':
 
-    from paddleocr import PaddleOCR
-
-    import paddle
-    print(f"Paddle版本: {paddle.__version__}")
-    print(f"GPU可用: {paddle.is_compiled_with_cuda()}")
-    print(f"GPU数量: {paddle.device.cuda.device_count()}")
-
-    ocr = PaddleOCR(
-        text_detection_model_dir="./PPv6/PP-OCRv6_medium_det_safetensors",
-        text_recognition_model_dir="./PPv6/PP-OCRv6_medium_rec_safetensors",
-        lang='chinese_cht',                  # 繁体字典
-        return_word_box=True,                # 返回每个字符的坐标
-        use_doc_orientation_classify=True,   # 整页方向（横/倒）
-        use_doc_unwarping=False,             # 关闭弯曲矫正，单字坐标它才准。否则坐标是矫正后图像的坐标
-        use_textline_orientation=True,       # 文本行方向分类，竖排靠它
-        text_det_thresh=0.1,                 # 默认 0.3 对古籍太高，漏淡墨
-        text_det_box_thresh=0.1,             # 同上
-        text_rec_score_thresh=0.3,           # 过滤低置信，古籍可放低
-    )
-    import cv2
-
-    result = ocr.predict("./data/SWX0005_00000_00001.webp")
+is_debug = False
+is_debug_api = True
+
+dic_cache = {}
+
+from flask import Flask, request, jsonify
+import threading
+import platform
+
+app = Flask(__name__)
+
+import json
+import decimal
+import datetime
+import base64
+import numpy as np
+import cv2
+
+from collections import OrderedDict
+
+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
+
+def base64_to_mat(base64_str):
+    """
+    将 Base64 字符串转换为 OpenCV Mat 对象（NumPy 数组）
+    
+    参数:
+        base64_str (str): Base64 编码的图片字符串（不可以含前缀如 "data:image/jpeg;base64,"）
+    
+    返回:
+        Mat: OpenCV 图像对象（NumPy 数组），格式为 BGR
+    """
+    # 处理可能存在的 Base64 前缀（如 "data:image/jpeg;base64,"）
+    # if ',' in base64_str:
+    #     base64_data = base64_str.split(',')[1]  # 提取纯 Base64 数据部分
+    # else:
+    # base64_data = base64_str
+    
+    # 解码 Base64 字符串为二进制字节流
+    image_bytes = base64.b64decode(base64_str)
+    
+    # 将字节流转换为 NumPy 数组（数据类型 uint8）
+    nparr = np.frombuffer(image_bytes, np.uint8)
+    
+    # 使用 OpenCV 解码为 Mat 对象（BGR 格式）
+    mat = cv2.imdecode(nparr, cv2.IMREAD_UNCHANGED)
+
+    if len(mat.shape) != 3:  # 转彩图
+        mat = cv2.cvtColor(mat, cv2.COLOR_GRAY2BGR)
+    
+    return mat
+
+
+def ppresult_tojson(img, result):
+    global is_debug
+
+    jn = OrderedDict()
+    prism_wordsInfo = []
+    jn["prism_wordsInfo"] = prism_wordsInfo
+    jn["height"] = img.shape[0]
+    jn["width"] = img.shape[1]
+
     for res in result:
-        res.print()
-        res.save_to_img("output")
-        res.save_to_json("output")
-
-        # 单独保存预处理各阶段的图片（均为 BGR 格式）
-        pre = res['doc_preprocessor_res']  # pre['output_img'] draw_box.py 画字符框大体上准，但还不太准，坐标对应的就是 output_img , 而不是原图
-        cv2.imwrite("output/SWX0005_00000_00001_input_img.png",     pre['input_img'])
-        cv2.imwrite("output/SWX0005_00000_00001_rot_img.png",       pre['rot_img'])
-        cv2.imwrite("output/SWX0005_00000_00001_output_img.png",    pre['output_img'])  # draw_box.py 
-    pass
+        output_img = res['doc_preprocessor_res']['output_img']  # 这是预处理后的图片，坐标是这张图的坐标，而且还原不回去。关掉图像矫正后坐标就和原图坐标一致了
+        # use_doc_unwarping=False。
+        # img = output_img 
+        jsn = res.json['res']
+        text_word = jsn['text_word']
+        text_word_boxes = jsn['text_word_boxes']
+        rec_texts = jsn['rec_texts']
+        rec_boxes = jsn['rec_boxes']
+
+        for idx_line, (words, boxs) in enumerate(zip(text_word, text_word_boxes)):
+            text_line = rec_texts[idx_line]
+            text_box = rec_boxes[idx_line]
+
+            j = OrderedDict()
+            prism_wordsInfo.append( j )
+
+            lu = OrderedDict(x=text_box[0], y=text_box[1])
+            ru = OrderedDict(x=text_box[2], y=text_box[1])
+            rd = OrderedDict(x=text_box[2], y=text_box[3])
+            ld = OrderedDict(x=text_box[0], y=text_box[3])
+
+            j["word"] = text_line
+            j["pos"] = [ lu, ru, rd, ld ]
+
+
+            charInfo = []
+            j['charInfo'] = charInfo
+            j['angle'] = -1
+            j["x"] = lu["x"]
+            j["y"] = lu["y"]
+            j["width"] = ( max(ru["x"], rd["x"])) - ( min(lu["x"], ld["x"]) )
+            j["height"] = ( max(ld["y"], rd["y"])) - ( min(lu["y"], ru["y"]) )
+
+
+            img = cv2.rectangle(img, (lu['x'], lu['y']), (rd['x'], rd['y']), (255, 0, 0), 2)
+            if platform.system() == "Windows":
+                if is_debug_api:
+                    cv2.imshow('orig', img)
+                    cv2.waitKey(0)
+                pass
+            for idx_word, (word, box) in enumerate(zip(words, boxs)):
+
+                if (len(word) == 1):
+                    info = OrderedDict()
+                    charInfo.append( info )
+                    info["word"] = word
+                    info["x"] = box[0]
+                    info["y"] = box[1]
+                    info["w"] = box[2] - box[0]
+                    info["h"] = box[3] - box[1]
+                elif (len(word) > 1):
+                    for w in word:
+                        info = OrderedDict()
+                        charInfo.append( info )
+                        info["word"] = w
+                        info["x"] = box[0]
+                        info["y"] = box[1]
+                        info["w"] = box[2] - box[0]
+                        info["h"] = box[3] - box[1]
+
+                # print(word)
+                img = cv2.rectangle(img, (box[0], box[1]), (box[2], box[3]), (0, 255, 0), 2)  # 矩形的左上角, 矩形的右下角
+                if platform.system() == "Windows":
+                    if is_debug_api:
+                        cv2.imshow('orgin', img)
+                        cv2.waitKey(0)
+                    pass
+
+        # save_json('out.json', jn)
+        break  # 只处理第一张图的结果
+    
+    return jn
+
+from paddleocr import PaddleOCR
+import paddle
+print(f"Paddle版本: {paddle.__version__}")
+print(f"GPU可用: {paddle.is_compiled_with_cuda()}")
+print(f"GPU数量: {paddle.device.cuda.device_count()}")
+ocr = PaddleOCR(
+    text_detection_model_dir="./PPv6/PP-OCRv6_medium_det_safetensors",
+    text_recognition_model_dir="./PPv6/PP-OCRv6_medium_rec_safetensors",
+    lang='chinese_cht',                  # 繁体字典
+    return_word_box=True,                # 返回每个字符的坐标
+    use_doc_orientation_classify=True,   # 整页方向（横/倒）
+    use_doc_unwarping=False,             # 关闭弯曲矫正，单字坐标它才准。否则坐标是矫正后图像的坐标
+    use_textline_orientation=True,       # 文本行方向分类，竖排靠它
+    text_det_thresh=0.1,                 # 默认 0.3 对古籍太高，漏淡墨
+    text_det_box_thresh=0.1,             # 同上
+    text_rec_score_thresh=0.3,           # 过滤低置信，古籍可放低
+)
+
+
+# 限制同时处理的请求数量为1
+ocr_semaphore = threading.Semaphore(1)
+@app.route('/ppocrv6', methods=['post'])
+def ppv6():
+    # request.json 只能够接受方法为POST、Body为raw，header 内容为 application/json类型的数据
+    # print(request.json, type(request.json))
+
+    # 使用 request.form 来接受 x-www-form-urlencoded 格式的数据
+    # print(request.form, type(request.form))
+    
+    # form_data = request.form.to_dict()
+    # if "img" not in form_data:
+    #     return jsonify([])
+    
+    # base64_str = form_data["img"]
+
+    # 非阻塞方式获取信号量
+    if not ocr_semaphore.acquire(blocking=False):
+        return jsonify({"warning": "wait pre task done."})
+
+    try:
+
+        base64_str = request.json['img']
+        
+        img = base64_to_mat(base64_str)
+
+        result = ocr.predict(
+            input = img, 
+            return_word_box=True
+            )
+            
+        jn = ppresult_tojson(img.copy(), result)
+
+        return jsonify(jn)
+    
+    except Exception as e:
+        return jsonify({"error": str(e)})
+    finally:
+        ocr_semaphore.release()
+
+if __name__ == '__main__':
+
+    if is_debug:
+        import cv2
+
+        result = ocr.predict("./data/SWX0005_00000_00001.webp")
+        for res in result:
+            res.print()
+            res.save_to_img("output")
+            res.save_to_json("output")
+
+            # 单独保存预处理各阶段的图片（均为 BGR 格式）
+            pre = res['doc_preprocessor_res']  # pre['output_img'] draw_box.py 画字符框大体上准，但还不太准，坐标对应的就是 output_img , 而不是原图
+            cv2.imwrite("output/SWX0005_00000_00001_input_img.png",     pre['input_img'])
+            cv2.imwrite("output/SWX0005_00000_00001_rot_img.png",       pre['rot_img'])
+            cv2.imwrite("output/SWX0005_00000_00001_output_img.png",    pre['output_img'])  # draw_box.py 
+        pass
+    else:
+        app.run(host="0.0.0.0", port=9346, debug=True)

post.py

@@ -1,3 +1,6 @@
+
+# .venv/Scripts/python post.py
+
 import requests
 import base64
 import json
@@ -5,8 +8,9 @@ import os
 
 def test_ppocr():
     # Configuration
-    url = "http://127.0.0.1:8889/ppocr"
-    image_path = r"e:\huggingface_echodict\t\PPOCRLLM\data\0022.jpg"
+    # url = "http://127.0.0.1:8889/ppocr"
+    url = "http://127.0.0.1:9346/ppocrv6"
+    image_path = r"data/SWX0005_00000_00001.webp"
 
     # Check if image exists
     if not os.path.exists(image_path):