aliocr_IC15_convert done.

.gitignore

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

aliocr_IC15_convert.py

@@ -1,6 +1,7 @@
 
 # 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
 
 """
 
@@ -8,21 +9,18 @@
 
 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。
+
 """
 
 
@@ -132,12 +130,12 @@ def cutPoly(img, pts):
 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'
+    # 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/imgs/training/img_1.jpg'
-    # gt = './icdar2015_aliocr/annotations/training/gt_img_1.txt'
+    im = './icdar2015_aliocr/train_images/img_000001.jpg'
+    gt = './icdar2015_aliocr/train_gts/gt_img_000001.txt'
 
     if os.path.exists(gt):
 
@@ -174,7 +172,7 @@ if __name__ == "__main__":
             poly = np.array(poly)
             poly = poly.astype(np.int32)
 
-            #cv2.fillPoly(img, pts=[ poly ], color=(0, 0, 255))
+            # 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)
@@ -184,16 +182,18 @@ if __name__ == "__main__":
             cv2.polylines(img, [poly], isClosed=True,
                           color=(b, g, r), thickness=1)
 
-        #cv2.imwrite("poly.jpg", img)
+        cv2.imwrite("poly.jpg", img)
 
-        cv2.imshow("poly", img)
-        cv2.waitKey()
+        # cv2.imshow("poly", img)
+        # cv2.waitKey()
 
     # 开始转换
 
     out_dir = 'icdar2015_aliocr'
-    # train_list.txt
-    # test_list.txt
+    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
@@ -215,21 +215,21 @@ if __name__ == "__main__":
 
         base = Path(json_path).stem
 
-        img_path = os.path.join(dir_img, '{}.txt'.format(base))
+        img_train_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
+        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_path, "r", encoding="utf-8") as fp:
+        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)
+            # cv2.imshow('img', img)
+            # cv2.waitKey(0)
 
         if len(img.shape) != 3:  # 转彩图
             img_color = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
@@ -242,199 +242,222 @@ if __name__ == "__main__":
         img_color_origin2 = img_color.copy()
 
 
-        # 生成1000 张一模一样的图
-        for i in range(1, 2): # 1000+1
 
-            num_img = i
+        img_name = "img_{:06d}.jpg".format(g_count)
+        gt_name = "gt_img_{:06d}.txt".format(g_count)
 
-            img_name = "img_{:06d}.jpg".format(num_img)
-            gt_name = "gt_img_{:06d}.txt".format(num_img)
+        is_train_img = random.choices([0, 1], weights=[0.15, 0.85])[0]
+            # 85% 的概率是训练图
 
-            is_train_img = random.choices([0, 1], weights=[0.15, 0.85])[0]
-                # 85% 的概率是训练图
+        gt_txt_list = []
 
-            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)
 
-            if is_train_img:
-                train_list.append(img_name)
-            else:
-                test_list.append(img_name)
-            # num_img += 1
+        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)            
 
-            img_path = os.path.join(out_dir, 'train_images', img_name)
-            img_gt_path = os.path.join(
-                out_dir, 'train_gts', gt_name)
-            
-            dir1 = os.path.dirname(img_path)
-            dir2 = os.path.dirname(img_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 not os.path.exists(dir1):
-                 os.makedirs(dir1)
+        if is_train_img:
+            train_list.append(img_name)
+            cv2.imwrite(img_train_path, img)
+        else:
+            test_list.append(img_name)
+            cv2.imwrite(img_test_path, img)
 
-            if not os.path.exists(dir2):
-                 os.makedirs(dir2)
 
-            cv2.imwrite(img_path, img)
+        wordsInfo = jsn['prism_wordsInfo']
+        for j in range(len(wordsInfo)):
+            jo = wordsInfo[j]
+            word = jo["word"]
+            # 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
+            """
+            # 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:
 
-            wordsInfo = jsn['prism_wordsInfo']
-            for j in range(len(wordsInfo)):
-                jo = wordsInfo[j]
-                word = jo["word"]
-                # 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()
+            #     # 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
 
-                word_x = jo['x']
-                word_y = jo['y']
-                word_width = jo['width']
-                word_height = jo['height']
+            #     cv2.imshow("green", img_color)
+            #     cv2.waitKey(0)
 
-                if abs(angle) == 90 or abs(angle) == 270:
-                    word_width = jo['height']
-                    word_height = jo['width']
-                elif angle != 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
+            #     ])
 
-                    # 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.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("green", img_color)
-                    cv2.waitKey(0)
+            #     cv2.imshow("polys", 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
-                    ])
+            #     # 获取图像的维度，并计算中心
+            #     (h, w) = img_color.shape[:2]
+            #     (cX, cY) = (w // 2, h // 2)
 
-                    # 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
+            #     # - (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("polys", img_color)
-                    cv2.waitKey(0)
+            #     cv2.imshow("after trans", 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)
+            #     # 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
 
-                    # - (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()
+            #     # 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))
 
-                    cv2.imshow("after trans", img_color)
-                    cv2.waitKey(0)
+            #     points_ones = np.hstack([points, ones])
 
-                    # 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
+            #     # transform points
+            #     transformed_points = M.dot(points_ones.T).T
 
-                    # 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))
+            #     transformed_points_int = np.round(
+            #         transformed_points, decimals=0).astype(np.int32)  # 批量四舍五入
 
-                    points_ones = np.hstack([points, ones])
+            #     cv2.polylines(img_color, [transformed_points_int], isClosed=True, color=(
+            #         0, 0, 255), thickness=2)  # 画转换后的点
 
-                    # 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_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.polylines(img_color, [transformed_points_int], isClosed=True, color=(
-                        0, 0, 255), thickness=2)  # 画转换后的点
+                
+
+            #     cv2.imshow("orgin", img_color_origin)
+            #     cv2.waitKey(0)
 
 
-                    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��，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']]
 
+            # 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[0], lu[1], ru[0], ru[1], rd[0], rd[1], ld[0], ld[1], word) )
 
-                # 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"])
+            # 绘制矩形
+            start_point = (x, y)  # 矩形的左上角
 
-                x2 = int(pos[2]["x"])  # 右下
-                y2 = int(pos[2]["y"])
+            end_point = (x2, y2)  # 矩形的右下角
 
-                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']]
+            color = (0, 0, 255)  # BGR
 
-                # 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[0], lu[1], ru[0], ru[1], rd[0], rd[1], ld[0], ld[1], word) )
+            thickness = 2
 
-                # 绘制矩形
-                start_point = (x, y)  # 矩形的左上角
+            # 逐行画框
+            img_color = cv2.rectangle(img_color, start_point, end_point, color, thickness)
+            # cv2.imshow("box", img_color)
+            # cv2.waitKey(0)
 
-                end_point = (x2, y2)  # 矩形的右下角
+        gt_txt = "\n".join(gt_txt_list)
 
-                color = (0, 0, 255)  # BGR
+        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)
+            
+        
+        print(f'### one task one. {g_count} / {len(json_paths)}')
 
-                thickness = 2
+        g_count += 1
 
-                # 逐行画框
-                img_color = cv2.rectangle(img_color, start_point, end_point, color, thickness)
-                cv2.imshow("box", img_color)
+        
 
-                cv2.waitKey(0)
+    
 
-                points = [ lu, ru, rd, ld ]
+                # 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 ] )
+                # 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)
+                # 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)
+                #    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[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
+                #             [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
-                        ]
-                   )
+                #             [min( points[3][0], points1[3][0] ), max( points[3][1], points1[3][1] )]  # 左下
+                #         ]
+                #    )
 
-                   img_cuted = cutPoly(img, ps3)
-                   cv2.imwrite(f'./tmp/{g_count}.jpg', img_cuted)
-                   with open(f'./tmp/{g_count}.txt', 'w', encoding='utf-8') as f:
-	                    f.write(word)
+                #    img_cuted = cutPoly(img, ps3)
+                #    cv2.imwrite(f'./tmp/{g_count}.jpg', img_cuted)
+                #    with open(f'./tmp/{g_count}.txt', 'w', encoding='utf-8') as f:
+	            #         f.write(word)
                 #    g_count += 1
 
                 # cv2.polylines(img_color, [points], isClosed=True, color=(   # 多边形，框得比较全
@@ -580,5 +603,6 @@ if __name__ == "__main__":
             #     fp.write(gt_txt)
 
 
+    print('### all task done.')