fce_aug.py 20.4 KB
Newer Older
z37757's avatar
z37757 已提交
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
# copyright (c) 2022 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.
"""
This code is refer from:
https://github.com/open-mmlab/mmocr/blob/main/mmocr/datasets/pipelines/transforms.py
"""
z37757's avatar
z37757 已提交
18 19 20
import numpy as np
from PIL import Image, ImageDraw
import cv2
z37757's avatar
z37757 已提交
21
from shapely.geometry import Polygon
z37757's avatar
z37757 已提交
22
import math
z37757's avatar
z37757 已提交
23
from ppocr.utils.poly_nms import poly_intersection
z37757's avatar
z37757 已提交
24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48


class RandomScaling:
    def __init__(self, size=800, scale=(3. / 4, 5. / 2), **kwargs):
        """Random scale the image while keeping aspect.

        Args:
            size (int) : Base size before scaling.
            scale (tuple(float)) : The range of scaling.
        """
        assert isinstance(size, int)
        assert isinstance(scale, float) or isinstance(scale, tuple)
        self.size = size
        self.scale = scale if isinstance(scale, tuple) \
            else (1 - scale, 1 + scale)

    def __call__(self, data):
        image = data['image']
        text_polys = data['polys']
        h, w, _ = image.shape

        aspect_ratio = np.random.uniform(min(self.scale), max(self.scale))
        scales = self.size * 1.0 / max(h, w) * aspect_ratio
        scales = np.array([scales, scales])
        out_size = (int(h * scales[1]), int(w * scales[0]))
z37757's avatar
z37757 已提交
49
        image = cv2.resize(image, out_size[::-1])
z37757's avatar
z37757 已提交
50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131

        data['image'] = image
        text_polys[:, :, 0::2] = text_polys[:, :, 0::2] * scales[1]
        text_polys[:, :, 1::2] = text_polys[:, :, 1::2] * scales[0]
        data['polys'] = text_polys

        return data


class RandomCropFlip:
    def __init__(self,
                 pad_ratio=0.1,
                 crop_ratio=0.5,
                 iter_num=1,
                 min_area_ratio=0.2,
                 **kwargs):
        """Random crop and flip a patch of the image.

        Args:
            crop_ratio (float): The ratio of cropping.
            iter_num (int): Number of operations.
            min_area_ratio (float): Minimal area ratio between cropped patch
                and original image.
        """
        assert isinstance(crop_ratio, float)
        assert isinstance(iter_num, int)
        assert isinstance(min_area_ratio, float)

        self.pad_ratio = pad_ratio
        self.epsilon = 1e-2
        self.crop_ratio = crop_ratio
        self.iter_num = iter_num
        self.min_area_ratio = min_area_ratio

    def __call__(self, results):
        for i in range(self.iter_num):
            results = self.random_crop_flip(results)

        return results

    def random_crop_flip(self, results):
        image = results['image']
        polygons = results['polys']
        ignore_tags = results['ignore_tags']
        if len(polygons) == 0:
            return results

        if np.random.random() >= self.crop_ratio:
            return results

        h, w, _ = image.shape
        area = h * w
        pad_h = int(h * self.pad_ratio)
        pad_w = int(w * self.pad_ratio)
        h_axis, w_axis = self.generate_crop_target(image, polygons, pad_h,
                                                   pad_w)
        if len(h_axis) == 0 or len(w_axis) == 0:
            return results

        attempt = 0
        while attempt < 50:
            attempt += 1
            polys_keep = []
            polys_new = []
            ignore_tags_keep = []
            ignore_tags_new = []
            xx = np.random.choice(w_axis, size=2)
            xmin = np.min(xx) - pad_w
            xmax = np.max(xx) - pad_w
            xmin = np.clip(xmin, 0, w - 1)
            xmax = np.clip(xmax, 0, w - 1)
            yy = np.random.choice(h_axis, size=2)
            ymin = np.min(yy) - pad_h
            ymax = np.max(yy) - pad_h
            ymin = np.clip(ymin, 0, h - 1)
            ymax = np.clip(ymax, 0, h - 1)
            if (xmax - xmin) * (ymax - ymin) < area * self.min_area_ratio:
                # area too small
                continue

            pts = np.stack([[xmin, xmax, xmax, xmin],
                            [ymin, ymin, ymax, ymax]]).T.astype(np.int32)
z37757's avatar
z37757 已提交
132
            pp = Polygon(pts)
z37757's avatar
z37757 已提交
133 134
            fail_flag = False
            for polygon, ignore_tag in zip(polygons, ignore_tags):
z37757's avatar
z37757 已提交
135
                ppi = Polygon(polygon.reshape(-1, 2))
z37757's avatar
z37757 已提交
136
                ppiou, _ = poly_intersection(ppi, pp)
z37757's avatar
z37757 已提交
137
                if np.abs(ppiou - float(ppi.area)) > self.epsilon and \
z37757's avatar
z37757 已提交
138 139 140
                        np.abs(ppiou) > self.epsilon:
                    fail_flag = True
                    break
z37757's avatar
z37757 已提交
141
                elif np.abs(ppiou - float(ppi.area)) < self.epsilon:
z37757's avatar
z37757 已提交
142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432
                    polys_new.append(polygon)
                    ignore_tags_new.append(ignore_tag)
                else:
                    polys_keep.append(polygon)
                    ignore_tags_keep.append(ignore_tag)

            if fail_flag:
                continue
            else:
                break

        cropped = image[ymin:ymax, xmin:xmax, :]
        select_type = np.random.randint(3)
        if select_type == 0:
            img = np.ascontiguousarray(cropped[:, ::-1])
        elif select_type == 1:
            img = np.ascontiguousarray(cropped[::-1, :])
        else:
            img = np.ascontiguousarray(cropped[::-1, ::-1])
        image[ymin:ymax, xmin:xmax, :] = img
        results['img'] = image

        if len(polys_new) != 0:
            height, width, _ = cropped.shape
            if select_type == 0:
                for idx, polygon in enumerate(polys_new):
                    poly = polygon.reshape(-1, 2)
                    poly[:, 0] = width - poly[:, 0] + 2 * xmin
                    polys_new[idx] = poly
            elif select_type == 1:
                for idx, polygon in enumerate(polys_new):
                    poly = polygon.reshape(-1, 2)
                    poly[:, 1] = height - poly[:, 1] + 2 * ymin
                    polys_new[idx] = poly
            else:
                for idx, polygon in enumerate(polys_new):
                    poly = polygon.reshape(-1, 2)
                    poly[:, 0] = width - poly[:, 0] + 2 * xmin
                    poly[:, 1] = height - poly[:, 1] + 2 * ymin
                    polys_new[idx] = poly
            polygons = polys_keep + polys_new
            ignore_tags = ignore_tags_keep + ignore_tags_new
            results['polys'] = np.array(polygons)
            results['ignore_tags'] = ignore_tags

        return results

    def generate_crop_target(self, image, all_polys, pad_h, pad_w):
        """Generate crop target and make sure not to crop the polygon
        instances.

        Args:
            image (ndarray): The image waited to be crop.
            all_polys (list[list[ndarray]]): All polygons including ground
                truth polygons and ground truth ignored polygons.
            pad_h (int): Padding length of height.
            pad_w (int): Padding length of width.
        Returns:
            h_axis (ndarray): Vertical cropping range.
            w_axis (ndarray): Horizontal cropping range.
        """
        h, w, _ = image.shape
        h_array = np.zeros((h + pad_h * 2), dtype=np.int32)
        w_array = np.zeros((w + pad_w * 2), dtype=np.int32)

        text_polys = []
        for polygon in all_polys:
            rect = cv2.minAreaRect(polygon.astype(np.int32).reshape(-1, 2))
            box = cv2.boxPoints(rect)
            box = np.int0(box)
            text_polys.append([box[0], box[1], box[2], box[3]])

        polys = np.array(text_polys, dtype=np.int32)
        for poly in polys:
            poly = np.round(poly, decimals=0).astype(np.int32)
            minx = np.min(poly[:, 0])
            maxx = np.max(poly[:, 0])
            w_array[minx + pad_w:maxx + pad_w] = 1
            miny = np.min(poly[:, 1])
            maxy = np.max(poly[:, 1])
            h_array[miny + pad_h:maxy + pad_h] = 1

        h_axis = np.where(h_array == 0)[0]
        w_axis = np.where(w_array == 0)[0]
        return h_axis, w_axis


class RandomCropPolyInstances:
    """Randomly crop images and make sure to contain at least one intact
    instance."""

    def __init__(self, crop_ratio=5.0 / 8.0, min_side_ratio=0.4, **kwargs):
        super().__init__()
        self.crop_ratio = crop_ratio
        self.min_side_ratio = min_side_ratio

    def sample_valid_start_end(self, valid_array, min_len, max_start, min_end):

        assert isinstance(min_len, int)
        assert len(valid_array) > min_len

        start_array = valid_array.copy()
        max_start = min(len(start_array) - min_len, max_start)
        start_array[max_start:] = 0
        start_array[0] = 1
        diff_array = np.hstack([0, start_array]) - np.hstack([start_array, 0])
        region_starts = np.where(diff_array < 0)[0]
        region_ends = np.where(diff_array > 0)[0]
        region_ind = np.random.randint(0, len(region_starts))
        start = np.random.randint(region_starts[region_ind],
                                  region_ends[region_ind])

        end_array = valid_array.copy()
        min_end = max(start + min_len, min_end)
        end_array[:min_end] = 0
        end_array[-1] = 1
        diff_array = np.hstack([0, end_array]) - np.hstack([end_array, 0])
        region_starts = np.where(diff_array < 0)[0]
        region_ends = np.where(diff_array > 0)[0]
        region_ind = np.random.randint(0, len(region_starts))
        end = np.random.randint(region_starts[region_ind],
                                region_ends[region_ind])
        return start, end

    def sample_crop_box(self, img_size, results):
        """Generate crop box and make sure not to crop the polygon instances.

        Args:
            img_size (tuple(int)): The image size (h, w).
            results (dict): The results dict.
        """

        assert isinstance(img_size, tuple)
        h, w = img_size[:2]

        key_masks = results['polys']

        x_valid_array = np.ones(w, dtype=np.int32)
        y_valid_array = np.ones(h, dtype=np.int32)

        selected_mask = key_masks[np.random.randint(0, len(key_masks))]
        selected_mask = selected_mask.reshape((-1, 2)).astype(np.int32)
        max_x_start = max(np.min(selected_mask[:, 0]) - 2, 0)
        min_x_end = min(np.max(selected_mask[:, 0]) + 3, w - 1)
        max_y_start = max(np.min(selected_mask[:, 1]) - 2, 0)
        min_y_end = min(np.max(selected_mask[:, 1]) + 3, h - 1)

        for mask in key_masks:
            mask = mask.reshape((-1, 2)).astype(np.int32)
            clip_x = np.clip(mask[:, 0], 0, w - 1)
            clip_y = np.clip(mask[:, 1], 0, h - 1)
            min_x, max_x = np.min(clip_x), np.max(clip_x)
            min_y, max_y = np.min(clip_y), np.max(clip_y)

            x_valid_array[min_x - 2:max_x + 3] = 0
            y_valid_array[min_y - 2:max_y + 3] = 0

        min_w = int(w * self.min_side_ratio)
        min_h = int(h * self.min_side_ratio)

        x1, x2 = self.sample_valid_start_end(x_valid_array, min_w, max_x_start,
                                             min_x_end)
        y1, y2 = self.sample_valid_start_end(y_valid_array, min_h, max_y_start,
                                             min_y_end)

        return np.array([x1, y1, x2, y2])

    def crop_img(self, img, bbox):
        assert img.ndim == 3
        h, w, _ = img.shape
        assert 0 <= bbox[1] < bbox[3] <= h
        assert 0 <= bbox[0] < bbox[2] <= w
        return img[bbox[1]:bbox[3], bbox[0]:bbox[2]]

    def __call__(self, results):
        image = results['image']
        polygons = results['polys']
        ignore_tags = results['ignore_tags']
        if len(polygons) < 1:
            return results

        if np.random.random_sample() < self.crop_ratio:

            crop_box = self.sample_crop_box(image.shape, results)
            img = self.crop_img(image, crop_box)
            results['image'] = img
            # crop and filter masks
            x1, y1, x2, y2 = crop_box
            w = max(x2 - x1, 1)
            h = max(y2 - y1, 1)
            polygons[:, :, 0::2] = polygons[:, :, 0::2] - x1
            polygons[:, :, 1::2] = polygons[:, :, 1::2] - y1

            valid_masks_list = []
            valid_tags_list = []
            for ind, polygon in enumerate(polygons):
                if (polygon[:, ::2] > -4).all() and (
                        polygon[:, ::2] < w + 4).all() and (
                            polygon[:, 1::2] > -4).all() and (
                                polygon[:, 1::2] < h + 4).all():
                    polygon[:, ::2] = np.clip(polygon[:, ::2], 0, w)
                    polygon[:, 1::2] = np.clip(polygon[:, 1::2], 0, h)
                    valid_masks_list.append(polygon)
                    valid_tags_list.append(ignore_tags[ind])

            results['polys'] = np.array(valid_masks_list)
            results['ignore_tags'] = valid_tags_list

        return results

    def __repr__(self):
        repr_str = self.__class__.__name__
        return repr_str


class RandomRotatePolyInstances:
    def __init__(self,
                 rotate_ratio=0.5,
                 max_angle=10,
                 pad_with_fixed_color=False,
                 pad_value=(0, 0, 0),
                 **kwargs):
        """Randomly rotate images and polygon masks.

        Args:
            rotate_ratio (float): The ratio of samples to operate rotation.
            max_angle (int): The maximum rotation angle.
            pad_with_fixed_color (bool): The flag for whether to pad rotated
               image with fixed value. If set to False, the rotated image will
               be padded onto cropped image.
            pad_value (tuple(int)): The color value for padding rotated image.
        """
        self.rotate_ratio = rotate_ratio
        self.max_angle = max_angle
        self.pad_with_fixed_color = pad_with_fixed_color
        self.pad_value = pad_value

    def rotate(self, center, points, theta, center_shift=(0, 0)):
        # rotate points.
        (center_x, center_y) = center
        center_y = -center_y
        x, y = points[:, ::2], points[:, 1::2]
        y = -y

        theta = theta / 180 * math.pi
        cos = math.cos(theta)
        sin = math.sin(theta)

        x = (x - center_x)
        y = (y - center_y)

        _x = center_x + x * cos - y * sin + center_shift[0]
        _y = -(center_y + x * sin + y * cos) + center_shift[1]

        points[:, ::2], points[:, 1::2] = _x, _y
        return points

    def cal_canvas_size(self, ori_size, degree):
        assert isinstance(ori_size, tuple)
        angle = degree * math.pi / 180.0
        h, w = ori_size[:2]

        cos = math.cos(angle)
        sin = math.sin(angle)
        canvas_h = int(w * math.fabs(sin) + h * math.fabs(cos))
        canvas_w = int(w * math.fabs(cos) + h * math.fabs(sin))

        canvas_size = (canvas_h, canvas_w)
        return canvas_size

    def sample_angle(self, max_angle):
        angle = np.random.random_sample() * 2 * max_angle - max_angle
        return angle

    def rotate_img(self, img, angle, canvas_size):
        h, w = img.shape[:2]
        rotation_matrix = cv2.getRotationMatrix2D((w / 2, h / 2), angle, 1)
        rotation_matrix[0, 2] += int((canvas_size[1] - w) / 2)
        rotation_matrix[1, 2] += int((canvas_size[0] - h) / 2)

        if self.pad_with_fixed_color:
            target_img = cv2.warpAffine(
                img,
                rotation_matrix, (canvas_size[1], canvas_size[0]),
                flags=cv2.INTER_NEAREST,
                borderValue=self.pad_value)
        else:
            mask = np.zeros_like(img)
            (h_ind, w_ind) = (np.random.randint(0, h * 7 // 8),
                              np.random.randint(0, w * 7 // 8))
            img_cut = img[h_ind:(h_ind + h // 9), w_ind:(w_ind + w // 9)]
z37757's avatar
z37757 已提交
433 434
            img_cut = cv2.resize(img_cut, (canvas_size[1], canvas_size[0]))

z37757's avatar
z37757 已提交
435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506
            mask = cv2.warpAffine(
                mask,
                rotation_matrix, (canvas_size[1], canvas_size[0]),
                borderValue=[1, 1, 1])
            target_img = cv2.warpAffine(
                img,
                rotation_matrix, (canvas_size[1], canvas_size[0]),
                borderValue=[0, 0, 0])
            target_img = target_img + img_cut * mask

        return target_img

    def __call__(self, results):
        if np.random.random_sample() < self.rotate_ratio:
            image = results['image']
            polygons = results['polys']
            h, w = image.shape[:2]

            angle = self.sample_angle(self.max_angle)
            canvas_size = self.cal_canvas_size((h, w), angle)
            center_shift = (int((canvas_size[1] - w) / 2), int(
                (canvas_size[0] - h) / 2))
            image = self.rotate_img(image, angle, canvas_size)
            results['image'] = image
            # rotate polygons
            rotated_masks = []
            for mask in polygons:
                rotated_mask = self.rotate((w / 2, h / 2), mask, angle,
                                           center_shift)
                rotated_masks.append(rotated_mask)
            results['polys'] = np.array(rotated_masks)

        return results

    def __repr__(self):
        repr_str = self.__class__.__name__
        return repr_str


class SquareResizePad:
    def __init__(self,
                 target_size,
                 pad_ratio=0.6,
                 pad_with_fixed_color=False,
                 pad_value=(0, 0, 0),
                 **kwargs):
        """Resize or pad images to be square shape.

        Args:
            target_size (int): The target size of square shaped image.
            pad_with_fixed_color (bool): The flag for whether to pad rotated
               image with fixed value. If set to False, the rescales image will
               be padded onto cropped image.
            pad_value (tuple(int)): The color value for padding rotated image.
        """
        assert isinstance(target_size, int)
        assert isinstance(pad_ratio, float)
        assert isinstance(pad_with_fixed_color, bool)
        assert isinstance(pad_value, tuple)

        self.target_size = target_size
        self.pad_ratio = pad_ratio
        self.pad_with_fixed_color = pad_with_fixed_color
        self.pad_value = pad_value

    def resize_img(self, img, keep_ratio=True):
        h, w, _ = img.shape
        if keep_ratio:
            t_h = self.target_size if h >= w else int(h * self.target_size / w)
            t_w = self.target_size if h <= w else int(w * self.target_size / h)
        else:
            t_h = t_w = self.target_size
z37757's avatar
z37757 已提交
507
        img = cv2.resize(img, (t_w, t_h))
z37757's avatar
z37757 已提交
508 509 510 511 512 513 514 515 516 517 518 519 520 521
        return img, (t_h, t_w)

    def square_pad(self, img):
        h, w = img.shape[:2]
        if h == w:
            return img, (0, 0)
        pad_size = max(h, w)
        if self.pad_with_fixed_color:
            expand_img = np.ones((pad_size, pad_size, 3), dtype=np.uint8)
            expand_img[:] = self.pad_value
        else:
            (h_ind, w_ind) = (np.random.randint(0, h * 7 // 8),
                              np.random.randint(0, w * 7 // 8))
            img_cut = img[h_ind:(h_ind + h // 9), w_ind:(w_ind + w // 9)]
z37757's avatar
z37757 已提交
522
            expand_img = cv2.resize(img_cut, (pad_size, pad_size))
z37757's avatar
z37757 已提交
523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550
        if h > w:
            y0, x0 = 0, (h - w) // 2
        else:
            y0, x0 = (w - h) // 2, 0
        expand_img[y0:y0 + h, x0:x0 + w] = img
        offset = (x0, y0)

        return expand_img, offset

    def square_pad_mask(self, points, offset):
        x0, y0 = offset
        pad_points = points.copy()
        pad_points[::2] = pad_points[::2] + x0
        pad_points[1::2] = pad_points[1::2] + y0
        return pad_points

    def __call__(self, results):
        image = results['image']
        polygons = results['polys']
        h, w = image.shape[:2]

        if np.random.random_sample() < self.pad_ratio:
            image, out_size = self.resize_img(image, keep_ratio=True)
            image, offset = self.square_pad(image)
        else:
            image, out_size = self.resize_img(image, keep_ratio=False)
            offset = (0, 0)
        results['image'] = image
z37757's avatar
z37757 已提交
551 552 553 554 555 556 557
        try:
            polygons[:, :, 0::2] = polygons[:, :, 0::2] * out_size[
                1] / w + offset[0]
            polygons[:, :, 1::2] = polygons[:, :, 1::2] * out_size[
                0] / h + offset[1]
        except:
            pass
z37757's avatar
z37757 已提交
558 559 560 561 562 563 564
        results['polys'] = polygons

        return results

    def __repr__(self):
        repr_str = self.__class__.__name__
        return repr_str