Merge pull request #3218 from WenmuZhou/copy_paste

add copy paste

ppocr/data/imaug/__init__.py

@@ -23,6 +23,7 @@ from .random_crop_data import EastRandomCropData, PSERandomCrop
 
 from .rec_img_aug import RecAug, RecResizeImg, ClsResizeImg, SRNRecResizeImg
 from .randaugment import RandAugment
+from .copy_paste import CopyPaste
 from .operators import *
 from .label_ops import *
 

ppocr/data/imaug/copy_paste.py

+# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import copy
+import cv2
+import random
+import numpy as np
+from PIL import Image
+from shapely.geometry import Polygon
+
+from ppocr.data.imaug.iaa_augment import IaaAugment
+from ppocr.data.imaug.random_crop_data import is_poly_outside_rect
+from tools.infer.utility import get_rotate_crop_image
+
+
+class CopyPaste(object):
+    def __init__(self, objects_paste_ratio=0.2, limit_paste=True, **kwargs):
+        self.ext_data_num = 1
+        self.objects_paste_ratio = objects_paste_ratio
+        self.limit_paste = limit_paste
+        augmenter_args = [{'type': 'Resize', 'args': {'size': [0.5, 3]}}]
+        self.aug = IaaAugment(augmenter_args)
+
+    def __call__(self, data):
+        src_img = data['image']
+        src_polys = data['polys'].tolist()
+        src_ignores = data['ignore_tags'].tolist()
+        ext_data = data['ext_data'][0]
+        ext_image = ext_data['image']
+        ext_polys = ext_data['polys']
+        ext_ignores = ext_data['ignore_tags']
+
+        indexs = [i for i in range(len(ext_ignores)) if not ext_ignores[i]]
+        select_num = max(
+            1, min(int(self.objects_paste_ratio * len(ext_polys)), 30))
+
+        random.shuffle(indexs)
+        select_idxs = indexs[:select_num]
+        select_polys = ext_polys[select_idxs]
+        select_ignores = ext_ignores[select_idxs]
+
+        src_img = cv2.cvtColor(src_img, cv2.COLOR_BGR2RGB)
+        ext_image = cv2.cvtColor(ext_image, cv2.COLOR_BGR2RGB)
+        src_img = Image.fromarray(src_img).convert('RGBA')
+        for poly, tag in zip(select_polys, select_ignores):
+            box_img = get_rotate_crop_image(ext_image, poly)
+
+            src_img, box = self.paste_img(src_img, box_img, src_polys)
+            if box is not None:
+                src_polys.append(box)
+                src_ignores.append(tag)
+        src_img = cv2.cvtColor(np.array(src_img), cv2.COLOR_RGB2BGR)
+        h, w = src_img.shape[:2]
+        src_polys = np.array(src_polys)
+        src_polys[:, :, 0] = np.clip(src_polys[:, :, 0], 0, w)
+        src_polys[:, :, 1] = np.clip(src_polys[:, :, 1], 0, h)
+        data['image'] = src_img
+        data['polys'] = src_polys
+        data['ignore_tags'] = np.array(src_ignores)
+        return data
+
+    def paste_img(self, src_img, box_img, src_polys):
+        box_img_pil = Image.fromarray(box_img).convert('RGBA')
+        src_w, src_h = src_img.size
+        box_w, box_h = box_img_pil.size
+        if box_w > src_w or box_h > src_h:
+            return src_img, None
+        angle = np.random.randint(0, 360)
+        box = np.array([[[0, 0], [box_w, 0], [box_w, box_h], [0, box_h]]])
+        box = rotate_bbox(box_img, box, angle)[0]
+
+        paste_x, paste_y = self.select_coord(src_polys, box, src_w - box_w,
+                                             src_h - box_h)
+        if paste_x is None:
+            return src_img, None
+        box[:, 0] += paste_x
+        box[:, 1] += paste_y
+        box_img_pil = box_img_pil.rotate(angle, expand=1)
+        r, g, b, A = box_img_pil.split()
+        src_img.paste(box_img_pil, (paste_x, paste_y), mask=A)
+
+        return src_img, box
+
+    def select_coord(self, src_polys, box, endx, endy):
+        if self.limit_paste:
+            xmin, ymin, xmax, ymax = box[:, 0].min(), box[:, 1].min(
+            ), box[:, 0].max(), box[:, 1].max()
+            for _ in range(50):
+                paste_x = random.randint(0, endx)
+                paste_y = random.randint(0, endy)
+                xmin1 = xmin + paste_x
+                xmax1 = xmax + paste_x
+                ymin1 = ymin + paste_y
+                ymax1 = ymax + paste_y
+
+                num_poly_in_rect = 0
+                for poly in src_polys:
+                    if not is_poly_outside_rect(poly, xmax1, ymin1,
+                                                xmax1 - xmin1, ymax1 - ymin1):
+                        num_poly_in_rect += 1
+                        break
+                if num_poly_in_rect == 0:
+                    return paste_x, paste_y
+            return None, None
+        else:
+            paste_x = random.randint(0, endx)
+            paste_y = random.randint(0, endy)
+            return paste_x, paste_y
+
+
+def get_union(pD, pG):
+    return Polygon(pD).union(Polygon(pG)).area
+
+
+def get_intersection_over_union(pD, pG):
+    return get_intersection(pD, pG) / get_union(pD, pG)
+
+
+def get_intersection(pD, pG):
+    return Polygon(pD).intersection(Polygon(pG)).area
+
+
+def rotate_bbox(img, text_polys, angle, scale=1):
+    """
+    from https://github.com/WenmuZhou/DBNet.pytorch/blob/master/data_loader/modules/augment.py
+    Args:
+        img: np.ndarray
+        text_polys: np.ndarray N*4*2
+        angle: int
+        scale: int
+
+    Returns:
+
+    """
+    w = img.shape[1]
+    h = img.shape[0]
+
+    rangle = np.deg2rad(angle)
+    nw = (abs(np.sin(rangle) * h) + abs(np.cos(rangle) * w))
+    nh = (abs(np.cos(rangle) * h) + abs(np.sin(rangle) * w))
+    rot_mat = cv2.getRotationMatrix2D((nw * 0.5, nh * 0.5), angle, scale)
+    rot_move = np.dot(rot_mat, np.array([(nw - w) * 0.5, (nh - h) * 0.5, 0]))
+    rot_mat[0, 2] += rot_move[0]
+    rot_mat[1, 2] += rot_move[1]
+
+    # ---------------------- rotate box ----------------------
+    rot_text_polys = list()
+    for bbox in text_polys:
+        point1 = np.dot(rot_mat, np.array([bbox[0, 0], bbox[0, 1], 1]))
+        point2 = np.dot(rot_mat, np.array([bbox[1, 0], bbox[1, 1], 1]))
+        point3 = np.dot(rot_mat, np.array([bbox[2, 0], bbox[2, 1], 1]))
+        point4 = np.dot(rot_mat, np.array([bbox[3, 0], bbox[3, 1], 1]))
+        rot_text_polys.append([point1, point2, point3, point4])
+    return np.array(rot_text_polys, dtype=np.float32)

ppocr/data/simple_dataset.py

@@ -14,6 +14,7 @@
 import numpy as np
 import os
 import random
+import traceback
 from paddle.io import Dataset
 
 from .imaug import transform, create_operators
@@ -69,6 +70,36 @@ class SimpleDataSet(Dataset):
         random.shuffle(self.data_lines)
         return
 
+    def get_ext_data(self):
+        ext_data_num = 0
+        for op in self.ops:
+            if hasattr(op, 'ext_data_num'):
+                ext_data_num = getattr(op, 'ext_data_num')
+                break
+        load_data_ops = self.ops[:2]
+        ext_data = []
+
+        while len(ext_data) < ext_data_num:
+            file_idx = self.data_idx_order_list[np.random.randint(self.__len__(
+            ))]
+            data_line = self.data_lines[file_idx]
+            data_line = data_line.decode('utf-8')
+            substr = data_line.strip("\n").split(self.delimiter)
+            file_name = substr[0]
+            label = substr[1]
+            img_path = os.path.join(self.data_dir, file_name)
+            data = {'img_path': img_path, 'label': label}
+            if not os.path.exists(img_path):
+                continue
+            with open(data['img_path'], 'rb') as f:
+                img = f.read()
+                data['image'] = img
+            data = transform(data, load_data_ops)
+            if data is None:
+                continue
+            ext_data.append(data)
+        return ext_data
+
     def __getitem__(self, idx):
         file_idx = self.data_idx_order_list[idx]
         data_line = self.data_lines[file_idx]
@@ -84,11 +115,13 @@ class SimpleDataSet(Dataset):
             with open(data['img_path'], 'rb') as f:
                 img = f.read()
                 data['image'] = img
+            data['ext_data'] = self.get_ext_data()
             outs = transform(data, self.ops)
-        except Exception as e:
+        except:
+            error_meg = traceback.format_exc()
             self.logger.error(
                 "When parsing line {}, error happened with msg: {}".format(
-                    data_line, e))
+                    data_line, error_meg))
             outs = None
         if outs is None:
             # during evaluation, we should fix the idx to get same results for many times of evaluation.

tools/infer/predict_system.py

@@ -33,7 +33,7 @@ import tools.infer.predict_det as predict_det
 import tools.infer.predict_cls as predict_cls
 from ppocr.utils.utility import get_image_file_list, check_and_read_gif
 from ppocr.utils.logging import get_logger
-from tools.infer.utility import draw_ocr_box_txt
+from tools.infer.utility import draw_ocr_box_txt, get_rotate_crop_image
 logger = get_logger()
 
 
@@ -49,39 +49,6 @@ class TextSystem(object):
         if self.use_angle_cls:
             self.text_classifier = predict_cls.TextClassifier(args)
 
-    def get_rotate_crop_image(self, img, points):
-        '''
-        img_height, img_width = img.shape[0:2]
-        left = int(np.min(points[:, 0]))
-        right = int(np.max(points[:, 0]))
-        top = int(np.min(points[:, 1]))
-        bottom = int(np.max(points[:, 1]))
-        img_crop = img[top:bottom, left:right, :].copy()
-        points[:, 0] = points[:, 0] - left
-        points[:, 1] = points[:, 1] - top
-        '''
-        img_crop_width = int(
-            max(
-                np.linalg.norm(points[0] - points[1]),
-                np.linalg.norm(points[2] - points[3])))
-        img_crop_height = int(
-            max(
-                np.linalg.norm(points[0] - points[3]),
-                np.linalg.norm(points[1] - points[2])))
-        pts_std = np.float32([[0, 0], [img_crop_width, 0],
-                              [img_crop_width, img_crop_height],
-                              [0, img_crop_height]])
-        M = cv2.getPerspectiveTransform(points, pts_std)
-        dst_img = cv2.warpPerspective(
-            img,
-            M, (img_crop_width, img_crop_height),
-            borderMode=cv2.BORDER_REPLICATE,
-            flags=cv2.INTER_CUBIC)
-        dst_img_height, dst_img_width = dst_img.shape[0:2]
-        if dst_img_height * 1.0 / dst_img_width >= 1.5:
-            dst_img = np.rot90(dst_img)
-        return dst_img
-
     def print_draw_crop_rec_res(self, img_crop_list, rec_res):
         bbox_num = len(img_crop_list)
         for bno in range(bbox_num):
@@ -102,7 +69,7 @@ class TextSystem(object):
 
         for bno in range(len(dt_boxes)):
             tmp_box = copy.deepcopy(dt_boxes[bno])
-            img_crop = self.get_rotate_crop_image(ori_im, tmp_box)
+            img_crop = get_rotate_crop_image(ori_im, tmp_box)
             img_crop_list.append(img_crop)
         if self.use_angle_cls and cls:
             img_crop_list, angle_list, elapse = self.text_classifier(

tools/infer/utility.py

@@ -506,5 +506,40 @@ def draw_boxes(image, boxes, scores=None, drop_score=0.5):
     return image
 
 
+def get_rotate_crop_image(img, points):
+    '''
+    img_height, img_width = img.shape[0:2]
+    left = int(np.min(points[:, 0]))
+    right = int(np.max(points[:, 0]))
+    top = int(np.min(points[:, 1]))
+    bottom = int(np.max(points[:, 1]))
+    img_crop = img[top:bottom, left:right, :].copy()
+    points[:, 0] = points[:, 0] - left
+    points[:, 1] = points[:, 1] - top
+    '''
+    assert len(points) == 4, "shape of points must be 4*2"
+    img_crop_width = int(
+        max(
+            np.linalg.norm(points[0] - points[1]),
+            np.linalg.norm(points[2] - points[3])))
+    img_crop_height = int(
+        max(
+            np.linalg.norm(points[0] - points[3]),
+            np.linalg.norm(points[1] - points[2])))
+    pts_std = np.float32([[0, 0], [img_crop_width, 0],
+                          [img_crop_width, img_crop_height],
+                          [0, img_crop_height]])
+    M = cv2.getPerspectiveTransform(points, pts_std)
+    dst_img = cv2.warpPerspective(
+        img,
+        M, (img_crop_width, img_crop_height),
+        borderMode=cv2.BORDER_REPLICATE,
+        flags=cv2.INTER_CUBIC)
+    dst_img_height, dst_img_width = dst_img.shape[0:2]
+    if dst_img_height * 1.0 / dst_img_width >= 1.5:
+        dst_img = np.rot90(dst_img)
+    return dst_img
+
+
 if __name__ == '__main__':
     pass