icdar2015_aliocr_char done

.gitignore

@@ -1,2 +1,3 @@
 icdar2015_aliocr/
+icdar2015_aliocr_char/
 poly.jpg

aliocr_IC15_char_convert.py

@@ -0,0 +1,385 @@
+
+# pip install numpy==1.26.4 opencv-python==4.6.0.66
+
+# see doc\lang\programming\pytorch\文本检测\DBNET 论文代码都有
+
+"""
+
+原图每一行剪出一张图, 对这张图做字符级标注
+
+给 DBNet 官方代码用
+
+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)
+
+"""
+
+
+"""
+
+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 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
+
+
+
+if __name__ == "__main__":
+
+    # 验证原版的文本标记框
+    # im = './datasets/icdar2015/train_images/img_1.jpg'
+    # gt = './datasets/icdar2015/train_gts/gt_img_1.txt'
+
+    # 验证自已生成的标记框
+    im = './icdar2015_aliocr_char/train_images/img_00000001.jpg'
+    gt = './icdar2015_aliocr_char/train_gts/gt_img_00000001.txt'
+
+    if os.path.exists(gt):
+
+        items = []
+        reader = open(gt, 'r', encoding='utf-8-sig').readlines()
+        for line in reader:
+            item = {}
+            parts = line.strip().split(',')
+            label = parts[-1]
+            if 'TD' in gt and label == '1':
+                label = '###'
+            line = [i.strip('\ufeff').strip('\xef\xbb\xbf') for i in parts]
+            if 'icdar' in gt:
+                poly = np.array(list(map(float, line[:8]))).reshape(
+                    (-1, 2)).tolist()
+            else:
+                num_points = math.floor((len(line) - 1) / 2) * 2
+                poly = np.array(list(map(float, line[:num_points]))).reshape(
+                    (-1, 2)).tolist()
+            item['poly'] = poly
+            item['text'] = label
+            # 多边形是用一个个的点表示的，起点连接第二个点，第二个连接第三个 ... 最后一点连接起点，构成一个闭合的区域
+            item['points'] = poly
+            # 此标记表示文字模糊不可辨认，文本框的标记是不可靠的
+            item['ignore'] = True if label == '###' else False
+            items.append(item)
+
+        img = cv2.imdecode(np.fromfile(im, dtype=np.uint8), -1)
+        # DBNet 原版代码只能处理彩图，所以统一处理成彩图
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+        for i in range(len(items)):
+            poly = items[i]['poly']
+            poly = np.array(poly)
+            poly = poly.astype(np.int32)
+
+            # cv2.fillPoly(img, pts=[ poly ], color=(0, 0, 255))
+
+            b = random.randint(0, 255)  # 用来生成[a,b]之间的随意整数，包括两个边界值。
+            g = random.randint(0, 255)
+            r = random.randint(0, 255)
+
+            # 只画线，不填充  # 就是画线，从起点连到第二个点 ... 最后一个点连到第一个点
+            cv2.polylines(img, [poly], isClosed=True,
+                          color=(b, g, r), thickness=1)
+
+        cv2.imwrite("poly.jpg", img)
+
+        # cv2.imshow("poly", img)
+        # cv2.waitKey()
+
+    # 开始转换
+
+    out_dir = 'icdar2015_aliocr_char'
+    if os.path.exists(out_dir):
+        import shutil
+        shutil.rmtree(out_dir)
+
+
+    # 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 = './data/json'  # '/yingedu/www/ocr_server/data/json'
+    dir_img = './data/img'  # '/yingedu/www/ocr_server/data/img'
+
+    train_list = []
+    train_list_path = os.path.join(out_dir, 'train_list.txt')
+
+    test_list = []
+    test_list_path = os.path.join(out_dir, 'test_list.txt')
+
+    g_count = 1
+    count = 1
+
+    json_paths = glob.glob('{}/*.json'.format(dir_json), recursive=True)
+
+    for json_path in json_paths:
+
+        base = Path(json_path).stem
+
+        img_train_path = os.path.join(dir_img, '{}.txt'.format(base))
+
+        if not os.path.exists(img_train_path):  # 没有相应的图片，可能被删除了
+            continue
+
+        jsn = load_json(json_path)
+
+        with open(img_train_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()
+
+
+        wordsInfo = jsn['prism_wordsInfo']
+        for j in range(len(wordsInfo)):
+            jo = wordsInfo[j]
+            word = jo["word"]
+            charInfo = jo["charInfo"]
+            # 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()
+
+            """
+            x y 宽高全部不靠谱, pos 里是对的
+            """
+
+
+
+            # 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
+            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']]
+
+            min_x = min(lu[0], ld[0])
+            max_x = max(ru[0], rd[0])
+
+            min_y = min(lu[1], ru[1])
+            max_y = max(rd[1], ld[1])
+
+            rows, cols = img.shape[:2]
+            if max_y >= rows:
+                max_y = rows - 1
+            if max_x >= cols:
+                max_x = cols - 1
+            
+            crop = img[min_y:max_y+1, min_x:max_x+1]
+
+            # cv2.imshow("crop", crop)
+            # cv2.waitKey()
+
+            is_train_img = random.choices([0, 1], weights=[0.15, 0.85])[0]
+                # 85% 的概率是训练图
+
+            img_name = "img_{:08d}.jpg".format(g_count)
+            gt_name = "gt_img_{:08d}.txt".format(g_count)
+
+
+            gt_txt_list = []
+
+            img_train_path = os.path.join(out_dir, 'train_images', img_name)
+            img_train_gt_path = os.path.join(out_dir, 'train_gts', gt_name)
+            img_test_path = os.path.join(out_dir, 'test_images', img_name)
+            img_test_gt_path = os.path.join(out_dir, 'test_gts', gt_name)
+
+            dir1 = os.path.dirname(img_train_path)
+            dir2 = os.path.dirname(img_train_gt_path)
+            dir3 = os.path.dirname(img_test_path)
+            dir4 = os.path.dirname(img_test_gt_path)            
+
+            if not os.path.exists(dir1):
+                    os.makedirs(dir1)
+            if not os.path.exists(dir2):
+                    os.makedirs(dir2)
+            if not os.path.exists(dir3):
+                    os.makedirs(dir3)
+            if not os.path.exists(dir4):
+                    os.makedirs(dir4)
+
+            if is_train_img:
+                train_list.append(img_name)
+                cv2.imwrite(img_train_path, crop)
+            else:
+                test_list.append(img_name)
+                cv2.imwrite(img_test_path, crop)
+
+
+            for info in charInfo:
+                wd = info["word"]
+                wd_x = info["x"]
+                wd_y = info["y"]
+                wd_w = info["w"]
+                wd_h = info["h"]
+
+                wd_crop = img[wd_y:wd_y+wd_h, wd_x:wd_x+wd_w]
+
+                wd_x_local = wd_x - min_x
+                wd_y_local = wd_y - min_y
+
+                wd_crop2 = crop[wd_y_local:wd_y_local+wd_h, wd_x_local:wd_x_local+wd_w]
+
+                # cv2.imshow("wd_crop", wd_crop2)
+                # cv2.waitKey()
+
+
+                lu_wd = [wd_x_local, wd_y_local]
+                ru_wd = [wd_x_local+wd_w, wd_y_local]
+                rd_wd = [wd_x_local+wd_w, wd_y_local+wd_h]
+                ld_wd = [wd_x_local, wd_y_local+wd_h]
+
+                # 生成 icdar2015 格式的人工标记训练数据（用于训练官方DB）
+                gt_txt_list.append( "{},{},{},{},{},{},{},{},{}".format(lu_wd[0], lu_wd[1], ru_wd[0], ru_wd[1], rd_wd[0], rd_wd[1], ld_wd[0], ld_wd[1], wd) )
+
+
+            gt_txt = "\n".join(gt_txt_list)
+
+            if is_train_img:
+                with open(img_train_gt_path, 'w', encoding='utf-8') as f:
+                    f.write(gt_txt)
+            else:
+                with open(img_test_gt_path, 'w', encoding='utf-8') as f:
+                    f.write(gt_txt)
+                    
+            
+            g_count += 1
+
+
+        print(f'### one task one. {count} / {len(json_paths)}')
+
+        count += 1
+
+    train_list_txt = "\n".join(train_list)
+    test_list_txt = "\n".join(test_list)
+
+    with open(os.path.join(out_dir, "train_list.txt"), 'w', encoding='utf-8') as f:
+        f.write(train_list_txt)
+
+    with open(os.path.join(out_dir, "test_list.txt"), 'w', encoding='utf-8') as f:
+        f.write(test_list_txt)
+
+    print('### all task done.')
+
+

readme.txt

@@ -1,29 +0,0 @@
-
-see doc\lang\programming\pytorch\文本检测\DBNET   论文代码都有
-
-see 深入理解神经网络：从逻辑回归到CNN.md -> DBNet 可微分二值化
-
-see https://docs.opencv.org/4.x/d4/d43/tutorial_dnn_text_spotting.html
-
-    - DB_IC15_resnet50.onnx:
-    url: https://drive.google.com/uc?export=dowload&id=17_ABp79PlFt9yPCxSaarVc_DKTmrSGGf
-    sha: bef233c28947ef6ec8c663d20a2b326302421fa3
-    recommended parameter setting: -inputHeight=736, -inputWidth=1280;
-    description: This model is trained on ICDAR2015, so it can only detect English text instances.
-    
-    - DB_IC15_resnet18.onnx:
-    url: https://drive.google.com/uc?export=dowload&id=1vY_KsDZZZb_svd5RT6pjyI8BS1nPbBSX
-    sha: 19543ce09b2efd35f49705c235cc46d0e22df30b
-    recommended parameter setting: -inputHeight=736, -inputWidth=1280;
-    description: This model is trained on ICDAR2015, so it can only detect English text instances.
-
-see huggingface/ColorTextEditorV2
-               /imradv3
-               /iWeChatOcr
-
-
-// 验证    
-CUDA_VISIBLE_DEVICES=0 python demo.py experiments/seg_detector/ic15_resnet18_deform_thre.yaml --image_path datasets/icdar2015/test_images/img_97.jpg --resume /root/final --polygon --box_thresh 0.7 --visualize 
-
-
-opencv-python==4.6.0.66