add flask api

main.py

@@ -1,4 +1,22 @@
-# see huggingface\BallonsTranslator\main.py
+# see huggingface/BallonsTranslator/main.py
+# see huggingface/project/flask_auto_selection.py
+
+
+
+from modules.textdetector.ctd.inference import TextDetector as CTDModel
+from modules.ocr.mit48px import Model48pxOCR
+
+CTD_ONNX_PATH = 'data/models/comictextdetector.pt.onnx'
+device = 'cpu'
+detect_size = 1280
+
+ctd_model = CTDModel(CTD_ONNX_PATH, detect_size=detect_size, device=device)
+
+
+OCR48PXMODEL_PATH = 'data/models/ocr_ar_48px.ckpt'
+ocr_model = Model48pxOCR(OCR48PXMODEL_PATH, device)
+
+
 import json, os, sys, time, io
 import os.path as osp
 
@@ -7,6 +25,48 @@ import PIL
 import cv2
 import numpy as np
 
+is_debug = True
+
+dic_cache = {}
+
+from flask import Flask, request, jsonify
+
+app = Flask(__name__)
+
+import base64
+import math, re
+
+def save_json(filename, dics):
+    with open(filename, 'w', encoding='utf-8') as fp:
+        json.dump(dics, fp, indent=4, 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 jsonparse(s):
+    return json.loads(s, strict=False)
+
+def jsonstring(d):
+    return json.dumps(d, ensure_ascii=False)
+
+def show_img(image, target_width=400):
+    # 获取原始图片的宽度和高度
+    original_height, original_width = image.shape[:2]
+    
+    # 计算缩放比例和目标高度
+    scale = target_width / original_width
+    target_height = int(original_height * scale)
+    
+    # 等比例缩放图片
+    resized_image = cv2.resize(image, (target_width, target_height), interpolation=cv2.INTER_AREA)
+    cv2.imshow("green", resized_image)
+    cv2.waitKey(0)
+    return resized_image
+
 # see utils\io_utils.py
 def imread(imgpath, read_type=cv2.IMREAD_COLOR, max_retry_limit=5, retry_interval=0.1):
     if not osp.exists(imgpath):
@@ -43,7 +103,106 @@ def chunks(lst, n):
     for i in range(0, len(lst), n):
         yield lst[i:i + n]
 
-if __name__ == '__main__':
+def ocr(img):
+
+    # All text detectors only support 3 channels input 
+    if img.ndim == 3 and img.shape[2] == 4:
+        img = cv2.cvtColor(img, cv2.COLOR_RGBA2RGB)
+
+    _, mask, blk_list = ctd_model(img)
+
+    fnt_rsz = 1.0
+    fnt_max = -1
+    fnt_min = -1
+    for blk in blk_list:
+        sz = blk._detected_font_size * fnt_rsz
+        if fnt_max > 0:
+            sz = min(fnt_max, sz)
+        if fnt_min > 0:
+            sz = max(fnt_min, sz)
+        blk.font_size = sz
+        blk._detected_font_size = sz
+
+    ksize = 2
+    if ksize > 0:
+        element = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2 * ksize + 1, 2 * ksize + 1),(ksize, ksize)) # 创建一个椭圆形的结构元素（kernel），用于后续的形态学操作  # 元素的尺寸 # (ksize, ksize) ：椭圆的锚点（中心点）
+        mask = cv2.dilate(mask, element) # 对 mask 图像进行膨胀操作（dilate），使用上面创建的椭圆结构元素。膨胀操作可以让白色区域（通常是前景或目标区域）变大，常用于去除小的黑洞、连接断开的区域等。
+
+    for blk in blk_list:
+        blk.det_model = 'ctd'
+
+    need_save_mask = True
+    detect_counter = 0
+
+    detect_counter += 1
+
+    for blk in blk_list:
+        blk.text = []
+
+    split_textblk = False
+    seg_func = None
+
+    model_text_height = 48
+    model_maxwidth = 8100
+
+    from utils.textblock import collect_textblock_regions
+
+    chunk_size = 16
+
+    regions, textblk_lst_indices = collect_textblock_regions(img, blk_list, model_text_height, model_maxwidth, split_textblk, seg_func)
+
+
+    ocr_model(blk_list, regions, textblk_lst_indices, chunk_size=chunk_size)
+
+
+    img_draw = img.copy()
+
+
+    # ui\mainwindow.py
+    for blk in blk_list:
+        text = blk.get_text()
+        for line in blk.lines:
+            img_draw = cv2.rectangle(img_draw, line[0], line[3], (0, 0, 255), 2)  # 在一行坚排文字的左边画一条红线
+
+
+    return blk_list, img_draw
+
+@app.route('/comicocr', methods=['post'])
+def comicocr():
+    global dic_cache
+
+    # 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))
+
+
+    img_b64_str = request.json['img']
+    img_bytes = base64.b64decode(img_b64_str)
+    
+    imgData = np.frombuffer(img_bytes, dtype=np.uint8)
+    img = cv2.imdecode(imgData, -1)
+
+    # All text detectors only support 3 channels input 
+    if img.ndim == 3 and img.shape[2] == 4:
+        img = cv2.cvtColor(img, cv2.COLOR_RGBA2RGB)
+
+    
+    cv2.imshow('test', img)
+    cv2.waitKey()
+    
+    return jsonify({})
+
+
+def main():
+    if is_debug:
+        img = imread('E:/huggingface/BallonsTranslator/assets/kcc-0010.jpg')
+        blk_list, img_draw = ocr(img)
+
+        cv2.imwrite("E:/xxxxxxxxxxxxxxxx.jpg", img_draw)
+
+    else:
+        app.run(host="0.0.0.0", port=2393, debug=True)
+    
+    return
 
     from modules.textdetector.ctd.inference import TextDetector as CTDModel
     from modules.ocr.mit48px import Model48pxOCR
@@ -144,3 +303,7 @@ if __name__ == '__main__':
     pass
 
 
+if __name__ == '__main__':
+    main()
+ 
+

requirements.txt

@@ -24,4 +24,5 @@ httpcore==1.0.5
 openai==2.6.0
 openapi-schema-validator==0.6.2
 openapi-spec-validator==0.7.1
+flask==3.1.0
 # winsdk==1.0.0b10