add tia aug

ppocr/data/rec/img_tools.py

@@ -19,6 +19,8 @@ import random
 from ppocr.utils.utility import initial_logger
 logger = initial_logger()
 
+from .text_image_aug.augment import tia_distort, tia_stretch, tia_perspective
+
 
 def get_bounding_box_rect(pos):
     left = min(pos[0])
@@ -196,6 +198,9 @@ class Config:
         self.h = h
 
         self.perspective = True
+        self.stretch = True
+        self.distort = True
+
         self.crop = True
         self.affine = False
         self.reverse = True
@@ -299,41 +304,40 @@ def warp(img, ang):
     config.make(w, h, ang)
     new_img = img
 
+    prob = 0.4
+
+    if config.distort:
+        img_height, img_width = img.shape[0:2]
+        if random.random() <= prob and img_height >= 20 and img_width >= 20:
+            new_img = tia_distort(new_img, random.randint(3, 6))
+
+    if config.stretch:
+        img_height, img_width = img.shape[0:2]
+        if random.random() <= prob and img_height >= 20 and img_width >= 20:
+            new_img = tia_stretch(new_img, random.randint(3, 6))
+
     if config.perspective:
-        tp = random.randint(1, 100)
-        if tp >= 50:
-            warpR, (r1, c1), ratio, dst = get_warpR(config)
-            new_w = int(np.max(dst[:, 0])) - int(np.min(dst[:, 0]))
-            new_img = cv2.warpPerspective(
-                new_img,
-                warpR, (int(new_w * ratio), h),
-                borderMode=config.borderMode)
+        if random.random() <= prob:
+            new_img = tia_perspective(new_img)
+
     if config.crop:
         img_height, img_width = img.shape[0:2]
-        tp = random.randint(1, 100)
-        if tp >= 50 and img_height >= 20 and img_width >= 20:
+        if random.random() <= prob and img_height >= 20 and img_width >= 20:
             new_img = get_crop(new_img)
-    if config.affine:
-        warpT = get_warpAffine(config)
-        new_img = cv2.warpAffine(
-            new_img, warpT, (w, h), borderMode=config.borderMode)
+
     if config.blur:
-        tp = random.randint(1, 100)
-        if tp >= 50:
+        if random.random() <= prob:
             new_img = blur(new_img)
     if config.color:
-        tp = random.randint(1, 100)
-        if tp >= 50:
+        if random.random() <= prob:
             new_img = cvtColor(new_img)
     if config.jitter:
         new_img = jitter(new_img)
     if config.noise:
-        tp = random.randint(1, 100)
-        if tp >= 50:
+        if random.random() <= prob:
             new_img = add_gasuss_noise(new_img)
     if config.reverse:
-        tp = random.randint(1, 100)
-        if tp >= 50:
+        if random.random() <= prob:
             new_img = 255 - new_img
     return new_img
 
@@ -360,7 +364,7 @@ def process_image(img,
         text = char_ops.encode(label)
         if len(text) == 0 or len(text) > max_text_length:
             logger.info(
-                "Warning in ppocr/data/rec/img_tools.py: Wrong data type."
+                "Warning in ppocr/data/rec/img_tools.py:line362: Wrong data type."
                 "Excepted string with length between 1 and {}, but "
                 "got '{}'. Label is '{}'".format(max_text_length,
                                                  len(text), label))
@@ -382,6 +386,7 @@ def process_image(img,
                     % loss_type
     return (norm_img)
 
+
 def resize_norm_img_srn(img, image_shape):
     imgC, imgH, imgW = image_shape
 
@@ -408,30 +413,39 @@ def resize_norm_img_srn(img, image_shape):
 
     return np.reshape(img_black, (c, row, col)).astype(np.float32)
 
-def srn_other_inputs(image_shape,
-                     num_heads,
-                     max_text_length,
-                     char_num):
+
+def srn_other_inputs(image_shape, num_heads, max_text_length, char_num):
 
     imgC, imgH, imgW = image_shape
     feature_dim = int((imgH / 8) * (imgW / 8))
 
-    encoder_word_pos = np.array(range(0, feature_dim)).reshape((feature_dim, 1)).astype('int64')
-    gsrm_word_pos = np.array(range(0, max_text_length)).reshape((max_text_length, 1)).astype('int64')
+    encoder_word_pos = np.array(range(0, feature_dim)).reshape(
+        (feature_dim, 1)).astype('int64')
+    gsrm_word_pos = np.array(range(0, max_text_length)).reshape(
+        (max_text_length, 1)).astype('int64')
 
-    lbl_weight = np.array([int(char_num-1)] * max_text_length).reshape((-1,1)).astype('int64')
+    lbl_weight = np.array([int(char_num - 1)] * max_text_length).reshape(
+        (-1, 1)).astype('int64')
 
-    gsrm_attn_bias_data = np.ones((1, max_text_length, max_text_length)) 
-    gsrm_slf_attn_bias1 = np.triu(gsrm_attn_bias_data, 1).reshape([-1, 1, max_text_length, max_text_length])
-    gsrm_slf_attn_bias1 = np.tile(gsrm_slf_attn_bias1, [1, num_heads, 1, 1]) * [-1e9] 
+    gsrm_attn_bias_data = np.ones((1, max_text_length, max_text_length))
+    gsrm_slf_attn_bias1 = np.triu(gsrm_attn_bias_data, 1).reshape(
+        [-1, 1, max_text_length, max_text_length])
+    gsrm_slf_attn_bias1 = np.tile(gsrm_slf_attn_bias1,
+                                  [1, num_heads, 1, 1]) * [-1e9]
 
-    gsrm_slf_attn_bias2 = np.tril(gsrm_attn_bias_data, -1).reshape([-1, 1, max_text_length, max_text_length])
-    gsrm_slf_attn_bias2 = np.tile(gsrm_slf_attn_bias2, [1, num_heads, 1, 1]) * [-1e9] 
+    gsrm_slf_attn_bias2 = np.tril(gsrm_attn_bias_data, -1).reshape(
+        [-1, 1, max_text_length, max_text_length])
+    gsrm_slf_attn_bias2 = np.tile(gsrm_slf_attn_bias2,
+                                  [1, num_heads, 1, 1]) * [-1e9]
 
     encoder_word_pos = encoder_word_pos[np.newaxis, :]
     gsrm_word_pos = gsrm_word_pos[np.newaxis, :]
 
-    return [lbl_weight, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2]
+    return [
+        lbl_weight, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1,
+        gsrm_slf_attn_bias2
+    ]
+
 
 def process_image_srn(img,
                       image_shape,
@@ -453,14 +467,16 @@ def process_image_srn(img,
             return None
         else:
             if loss_type == "srn":
-                text_padded = [int(char_num-1)] * max_text_length
+                text_padded = [int(char_num - 1)] * max_text_length
                 for i in range(len(text)):
                     text_padded[i] = text[i]
                     lbl_weight[i] = [1.0]
                 text_padded = np.array(text_padded)
                 text = text_padded.reshape(-1, 1)
-                return (norm_img, text,encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2,lbl_weight)
+                return (norm_img, text, encoder_word_pos, gsrm_word_pos,
+                        gsrm_slf_attn_bias1, gsrm_slf_attn_bias2, lbl_weight)
             else:
                 assert False, "Unsupport loss_type %s in process_image"\
                     % loss_type
-    return (norm_img, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2)
+    return (norm_img, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1,
+            gsrm_slf_attn_bias2)

ppocr/data/rec/text_image_aug/augment.py

+# -*- coding:utf-8 -*-
+# Author: RubanSeven
+# Reference: https://github.com/RubanSeven/Text-Image-Augmentation-python
+
+# import cv2
+import numpy as np
+from .warp_mls import WarpMLS
+
+
+def tia_distort(src, segment=4):
+    img_h, img_w = src.shape[:2]
+
+    cut = img_w // segment
+    thresh = cut // 3
+
+    src_pts = list()
+    dst_pts = list()
+
+    src_pts.append([0, 0])
+    src_pts.append([img_w, 0])
+    src_pts.append([img_w, img_h])
+    src_pts.append([0, img_h])
+
+    dst_pts.append([np.random.randint(thresh), np.random.randint(thresh)])
+    dst_pts.append(
+        [img_w - np.random.randint(thresh), np.random.randint(thresh)])
+    dst_pts.append(
+        [img_w - np.random.randint(thresh), img_h - np.random.randint(thresh)])
+    dst_pts.append(
+        [np.random.randint(thresh), img_h - np.random.randint(thresh)])
+
+    half_thresh = thresh * 0.5
+
+    for cut_idx in np.arange(1, segment, 1):
+        src_pts.append([cut * cut_idx, 0])
+        src_pts.append([cut * cut_idx, img_h])
+        dst_pts.append([
+            cut * cut_idx + np.random.randint(thresh) - half_thresh,
+            np.random.randint(thresh) - half_thresh
+        ])
+        dst_pts.append([
+            cut * cut_idx + np.random.randint(thresh) - half_thresh,
+            img_h + np.random.randint(thresh) - half_thresh
+        ])
+
+    trans = WarpMLS(src, src_pts, dst_pts, img_w, img_h)
+    dst = trans.generate()
+
+    return dst
+
+
+def tia_stretch(src, segment=4):
+    img_h, img_w = src.shape[:2]
+
+    cut = img_w // segment
+    thresh = cut * 4 // 5
+
+    src_pts = list()
+    dst_pts = list()
+
+    src_pts.append([0, 0])
+    src_pts.append([img_w, 0])
+    src_pts.append([img_w, img_h])
+    src_pts.append([0, img_h])
+
+    dst_pts.append([0, 0])
+    dst_pts.append([img_w, 0])
+    dst_pts.append([img_w, img_h])
+    dst_pts.append([0, img_h])
+
+    half_thresh = thresh * 0.5
+
+    for cut_idx in np.arange(1, segment, 1):
+        move = np.random.randint(thresh) - half_thresh
+        src_pts.append([cut * cut_idx, 0])
+        src_pts.append([cut * cut_idx, img_h])
+        dst_pts.append([cut * cut_idx + move, 0])
+        dst_pts.append([cut * cut_idx + move, img_h])
+
+    trans = WarpMLS(src, src_pts, dst_pts, img_w, img_h)
+    dst = trans.generate()
+
+    return dst
+
+
+def tia_perspective(src):
+    img_h, img_w = src.shape[:2]
+
+    thresh = img_h // 2
+
+    src_pts = list()
+    dst_pts = list()
+
+    src_pts.append([0, 0])
+    src_pts.append([img_w, 0])
+    src_pts.append([img_w, img_h])
+    src_pts.append([0, img_h])
+
+    dst_pts.append([0, np.random.randint(thresh)])
+    dst_pts.append([img_w, np.random.randint(thresh)])
+    dst_pts.append([img_w, img_h - np.random.randint(thresh)])
+    dst_pts.append([0, img_h - np.random.randint(thresh)])
+
+    trans = WarpMLS(src, src_pts, dst_pts, img_w, img_h)
+    dst = trans.generate()
+
+    return dst

ppocr/data/rec/text_image_aug/warp_mls.py

+# -*- coding:utf-8 -*-
+# Author: RubanSeven
+# Reference: https://github.com/RubanSeven/Text-Image-Augmentation-python
+import math
+import numpy as np
+
+
+class WarpMLS:
+    def __init__(self, src, src_pts, dst_pts, dst_w, dst_h, trans_ratio=1.):
+        self.src = src
+        self.src_pts = src_pts
+        self.dst_pts = dst_pts
+        self.pt_count = len(self.dst_pts)
+        self.dst_w = dst_w
+        self.dst_h = dst_h
+        self.trans_ratio = trans_ratio
+        self.grid_size = 100
+        self.rdx = np.zeros((self.dst_h, self.dst_w))
+        self.rdy = np.zeros((self.dst_h, self.dst_w))
+
+    @staticmethod
+    def __bilinear_interp(x, y, v11, v12, v21, v22):
+        return (v11 * (1 - y) + v12 * y) * (1 - x) + (v21 *
+                                                      (1 - y) + v22 * y) * x
+
+    def generate(self):
+        self.calc_delta()
+        return self.gen_img()
+
+    def calc_delta(self):
+        w = np.zeros(self.pt_count, dtype=np.float32)
+
+        if self.pt_count < 2:
+            return
+
+        i = 0
+        while 1:
+            if self.dst_w <= i < self.dst_w + self.grid_size - 1:
+                i = self.dst_w - 1
+            elif i >= self.dst_w:
+                break
+
+            j = 0
+            while 1:
+                if self.dst_h <= j < self.dst_h + self.grid_size - 1:
+                    j = self.dst_h - 1
+                elif j >= self.dst_h:
+                    break
+
+                sw = 0
+                swp = np.zeros(2, dtype=np.float32)
+                swq = np.zeros(2, dtype=np.float32)
+                new_pt = np.zeros(2, dtype=np.float32)
+                cur_pt = np.array([i, j], dtype=np.float32)
+
+                k = 0
+                for k in range(self.pt_count):
+                    if i == self.dst_pts[k][0] and j == self.dst_pts[k][1]:
+                        break
+
+                    w[k] = 1. / (
+                        (i - self.dst_pts[k][0]) * (i - self.dst_pts[k][0]) +
+                        (j - self.dst_pts[k][1]) * (j - self.dst_pts[k][1]))
+
+                    sw += w[k]
+                    swp = swp + w[k] * np.array(self.dst_pts[k])
+                    swq = swq + w[k] * np.array(self.src_pts[k])
+
+                if k == self.pt_count - 1:
+                    pstar = 1 / sw * swp
+                    qstar = 1 / sw * swq
+
+                    miu_s = 0
+                    for k in range(self.pt_count):
+                        if i == self.dst_pts[k][0] and j == self.dst_pts[k][1]:
+                            continue
+                        pt_i = self.dst_pts[k] - pstar
+                        miu_s += w[k] * np.sum(pt_i * pt_i)
+
+                    cur_pt -= pstar
+                    cur_pt_j = np.array([-cur_pt[1], cur_pt[0]])
+
+                    for k in range(self.pt_count):
+                        if i == self.dst_pts[k][0] and j == self.dst_pts[k][1]:
+                            continue
+
+                        pt_i = self.dst_pts[k] - pstar
+                        pt_j = np.array([-pt_i[1], pt_i[0]])
+
+                        tmp_pt = np.zeros(2, dtype=np.float32)
+                        tmp_pt[0] = np.sum(pt_i * cur_pt) * self.src_pts[k][0] - \
+                                    np.sum(pt_j * cur_pt) * self.src_pts[k][1]
+                        tmp_pt[1] = -np.sum(pt_i * cur_pt_j) * self.src_pts[k][0] + \
+                                    np.sum(pt_j * cur_pt_j) * self.src_pts[k][1]
+                        tmp_pt *= (w[k] / miu_s)
+                        new_pt += tmp_pt
+
+                    new_pt += qstar
+                else:
+                    new_pt = self.src_pts[k]
+
+                self.rdx[j, i] = new_pt[0] - i
+                self.rdy[j, i] = new_pt[1] - j
+
+                j += self.grid_size
+            i += self.grid_size
+
+    def gen_img(self):
+        src_h, src_w = self.src.shape[:2]
+        dst = np.zeros_like(self.src, dtype=np.float32)
+
+        for i in np.arange(0, self.dst_h, self.grid_size):
+            for j in np.arange(0, self.dst_w, self.grid_size):
+                ni = i + self.grid_size
+                nj = j + self.grid_size
+                w = h = self.grid_size
+                if ni >= self.dst_h:
+                    ni = self.dst_h - 1
+                    h = ni - i + 1
+                if nj >= self.dst_w:
+                    nj = self.dst_w - 1
+                    w = nj - j + 1
+
+                di = np.reshape(np.arange(h), (-1, 1))
+                dj = np.reshape(np.arange(w), (1, -1))
+                delta_x = self.__bilinear_interp(
+                    di / h, dj / w, self.rdx[i, j], self.rdx[i, nj],
+                    self.rdx[ni, j], self.rdx[ni, nj])
+                delta_y = self.__bilinear_interp(
+                    di / h, dj / w, self.rdy[i, j], self.rdy[i, nj],
+                    self.rdy[ni, j], self.rdy[ni, nj])
+                nx = j + dj + delta_x * self.trans_ratio
+                ny = i + di + delta_y * self.trans_ratio
+                nx = np.clip(nx, 0, src_w - 1)
+                ny = np.clip(ny, 0, src_h - 1)
+                nxi = np.array(np.floor(nx), dtype=np.int32)
+                nyi = np.array(np.floor(ny), dtype=np.int32)
+                nxi1 = np.array(np.ceil(nx), dtype=np.int32)
+                nyi1 = np.array(np.ceil(ny), dtype=np.int32)
+
+                if len(self.src.shape) == 3:
+                    x = np.tile(np.expand_dims(ny - nyi, axis=-1), (1, 1, 3))
+                    y = np.tile(np.expand_dims(nx - nxi, axis=-1), (1, 1, 3))
+                else:
+                    x = ny - nyi
+                    y = nx - nxi
+                dst[i:i + h, j:j + w] = self.__bilinear_interp(
+                    x, y, self.src[nyi, nxi], self.src[nyi, nxi1],
+                    self.src[nyi1, nxi], self.src[nyi1, nxi1])
+
+        dst = np.clip(dst, 0, 255)
+        dst = np.array(dst, dtype=np.uint8)
+
+        return dst