add tia aug
This commit is contained in:
littletomatodonkey
parent
90f30dbeb6
commit
896e573975
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@ -19,6 +19,8 @@ import random
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from ppocr.utils.utility import initial_logger
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logger = initial_logger()
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from .text_image_aug.augment import tia_distort, tia_stretch, tia_perspective
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def get_bounding_box_rect(pos):
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left = min(pos[0])
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@ -196,6 +198,9 @@ class Config:
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self.h = h
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self.perspective = True
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self.stretch = True
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self.distort = True
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self.crop = True
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self.affine = False
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self.reverse = True
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@ -299,41 +304,40 @@ def warp(img, ang):
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config.make(w, h, ang)
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new_img = img
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prob = 0.4
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if config.distort:
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img_height, img_width = img.shape[0:2]
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if random.random() <= prob and img_height >= 20 and img_width >= 20:
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new_img = tia_distort(new_img, random.randint(3, 6))
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if config.stretch:
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img_height, img_width = img.shape[0:2]
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if random.random() <= prob and img_height >= 20 and img_width >= 20:
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new_img = tia_stretch(new_img, random.randint(3, 6))
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if config.perspective:
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tp = random.randint(1, 100)
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if tp >= 50:
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warpR, (r1, c1), ratio, dst = get_warpR(config)
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new_w = int(np.max(dst[:, 0])) - int(np.min(dst[:, 0]))
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new_img = cv2.warpPerspective(
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new_img,
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warpR, (int(new_w * ratio), h),
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borderMode=config.borderMode)
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if random.random() <= prob:
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new_img = tia_perspective(new_img)
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if config.crop:
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img_height, img_width = img.shape[0:2]
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tp = random.randint(1, 100)
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if tp >= 50 and img_height >= 20 and img_width >= 20:
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if random.random() <= prob and img_height >= 20 and img_width >= 20:
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new_img = get_crop(new_img)
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if config.affine:
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warpT = get_warpAffine(config)
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new_img = cv2.warpAffine(
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new_img, warpT, (w, h), borderMode=config.borderMode)
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if config.blur:
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tp = random.randint(1, 100)
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if tp >= 50:
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if random.random() <= prob:
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new_img = blur(new_img)
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if config.color:
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tp = random.randint(1, 100)
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if tp >= 50:
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if random.random() <= prob:
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new_img = cvtColor(new_img)
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if config.jitter:
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new_img = jitter(new_img)
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if config.noise:
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tp = random.randint(1, 100)
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if tp >= 50:
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if random.random() <= prob:
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new_img = add_gasuss_noise(new_img)
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if config.reverse:
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tp = random.randint(1, 100)
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if tp >= 50:
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if random.random() <= prob:
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new_img = 255 - new_img
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return new_img
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@ -360,7 +364,7 @@ def process_image(img,
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text = char_ops.encode(label)
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if len(text) == 0 or len(text) > max_text_length:
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logger.info(
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"Warning in ppocr/data/rec/img_tools.py: Wrong data type."
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"Warning in ppocr/data/rec/img_tools.py:line362: Wrong data type."
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"Excepted string with length between 1 and {}, but "
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"got '{}'. Label is '{}'".format(max_text_length,
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len(text), label))
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@ -382,6 +386,7 @@ def process_image(img,
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% loss_type
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return (norm_img)
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def resize_norm_img_srn(img, image_shape):
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imgC, imgH, imgW = image_shape
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@ -408,30 +413,39 @@ def resize_norm_img_srn(img, image_shape):
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return np.reshape(img_black, (c, row, col)).astype(np.float32)
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def srn_other_inputs(image_shape,
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num_heads,
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max_text_length,
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char_num):
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def srn_other_inputs(image_shape, num_heads, max_text_length, char_num):
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imgC, imgH, imgW = image_shape
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feature_dim = int((imgH / 8) * (imgW / 8))
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encoder_word_pos = np.array(range(0, feature_dim)).reshape((feature_dim, 1)).astype('int64')
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gsrm_word_pos = np.array(range(0, max_text_length)).reshape((max_text_length, 1)).astype('int64')
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encoder_word_pos = np.array(range(0, feature_dim)).reshape(
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(feature_dim, 1)).astype('int64')
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gsrm_word_pos = np.array(range(0, max_text_length)).reshape(
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(max_text_length, 1)).astype('int64')
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lbl_weight = np.array([int(char_num-1)] * max_text_length).reshape((-1,1)).astype('int64')
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lbl_weight = np.array([int(char_num - 1)] * max_text_length).reshape(
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(-1, 1)).astype('int64')
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gsrm_attn_bias_data = np.ones((1, max_text_length, max_text_length))
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gsrm_slf_attn_bias1 = np.triu(gsrm_attn_bias_data, 1).reshape([-1, 1, max_text_length, max_text_length])
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gsrm_slf_attn_bias1 = np.tile(gsrm_slf_attn_bias1, [1, num_heads, 1, 1]) * [-1e9]
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gsrm_attn_bias_data = np.ones((1, max_text_length, max_text_length))
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gsrm_slf_attn_bias1 = np.triu(gsrm_attn_bias_data, 1).reshape(
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[-1, 1, max_text_length, max_text_length])
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gsrm_slf_attn_bias1 = np.tile(gsrm_slf_attn_bias1,
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[1, num_heads, 1, 1]) * [-1e9]
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gsrm_slf_attn_bias2 = np.tril(gsrm_attn_bias_data, -1).reshape([-1, 1, max_text_length, max_text_length])
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gsrm_slf_attn_bias2 = np.tile(gsrm_slf_attn_bias2, [1, num_heads, 1, 1]) * [-1e9]
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gsrm_slf_attn_bias2 = np.tril(gsrm_attn_bias_data, -1).reshape(
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[-1, 1, max_text_length, max_text_length])
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gsrm_slf_attn_bias2 = np.tile(gsrm_slf_attn_bias2,
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[1, num_heads, 1, 1]) * [-1e9]
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encoder_word_pos = encoder_word_pos[np.newaxis, :]
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gsrm_word_pos = gsrm_word_pos[np.newaxis, :]
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return [lbl_weight, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2]
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return [
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lbl_weight, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1,
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gsrm_slf_attn_bias2
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]
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def process_image_srn(img,
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image_shape,
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@ -453,14 +467,16 @@ def process_image_srn(img,
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return None
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else:
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if loss_type == "srn":
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text_padded = [int(char_num-1)] * max_text_length
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text_padded = [int(char_num - 1)] * max_text_length
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for i in range(len(text)):
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text_padded[i] = text[i]
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lbl_weight[i] = [1.0]
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text_padded = np.array(text_padded)
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text = text_padded.reshape(-1, 1)
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return (norm_img, text,encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2,lbl_weight)
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return (norm_img, text, encoder_word_pos, gsrm_word_pos,
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gsrm_slf_attn_bias1, gsrm_slf_attn_bias2, lbl_weight)
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else:
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assert False, "Unsupport loss_type %s in process_image"\
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% loss_type
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return (norm_img, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2)
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return (norm_img, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1,
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gsrm_slf_attn_bias2)
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@ -0,0 +1,107 @@
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# -*- coding:utf-8 -*-
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# Author: RubanSeven
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# Reference: https://github.com/RubanSeven/Text-Image-Augmentation-python
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# import cv2
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import numpy as np
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from .warp_mls import WarpMLS
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def tia_distort(src, segment=4):
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img_h, img_w = src.shape[:2]
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cut = img_w // segment
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thresh = cut // 3
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src_pts = list()
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dst_pts = list()
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src_pts.append([0, 0])
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src_pts.append([img_w, 0])
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src_pts.append([img_w, img_h])
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src_pts.append([0, img_h])
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dst_pts.append([np.random.randint(thresh), np.random.randint(thresh)])
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dst_pts.append(
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[img_w - np.random.randint(thresh), np.random.randint(thresh)])
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dst_pts.append(
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[img_w - np.random.randint(thresh), img_h - np.random.randint(thresh)])
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dst_pts.append(
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[np.random.randint(thresh), img_h - np.random.randint(thresh)])
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half_thresh = thresh * 0.5
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for cut_idx in np.arange(1, segment, 1):
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src_pts.append([cut * cut_idx, 0])
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src_pts.append([cut * cut_idx, img_h])
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dst_pts.append([
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cut * cut_idx + np.random.randint(thresh) - half_thresh,
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np.random.randint(thresh) - half_thresh
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])
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dst_pts.append([
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cut * cut_idx + np.random.randint(thresh) - half_thresh,
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img_h + np.random.randint(thresh) - half_thresh
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])
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trans = WarpMLS(src, src_pts, dst_pts, img_w, img_h)
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dst = trans.generate()
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return dst
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def tia_stretch(src, segment=4):
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img_h, img_w = src.shape[:2]
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cut = img_w // segment
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thresh = cut * 4 // 5
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src_pts = list()
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dst_pts = list()
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src_pts.append([0, 0])
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src_pts.append([img_w, 0])
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src_pts.append([img_w, img_h])
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src_pts.append([0, img_h])
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dst_pts.append([0, 0])
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dst_pts.append([img_w, 0])
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dst_pts.append([img_w, img_h])
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dst_pts.append([0, img_h])
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half_thresh = thresh * 0.5
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for cut_idx in np.arange(1, segment, 1):
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move = np.random.randint(thresh) - half_thresh
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src_pts.append([cut * cut_idx, 0])
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src_pts.append([cut * cut_idx, img_h])
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dst_pts.append([cut * cut_idx + move, 0])
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dst_pts.append([cut * cut_idx + move, img_h])
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trans = WarpMLS(src, src_pts, dst_pts, img_w, img_h)
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dst = trans.generate()
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return dst
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def tia_perspective(src):
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img_h, img_w = src.shape[:2]
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thresh = img_h // 2
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src_pts = list()
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dst_pts = list()
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src_pts.append([0, 0])
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src_pts.append([img_w, 0])
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src_pts.append([img_w, img_h])
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src_pts.append([0, img_h])
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dst_pts.append([0, np.random.randint(thresh)])
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dst_pts.append([img_w, np.random.randint(thresh)])
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dst_pts.append([img_w, img_h - np.random.randint(thresh)])
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dst_pts.append([0, img_h - np.random.randint(thresh)])
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trans = WarpMLS(src, src_pts, dst_pts, img_w, img_h)
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dst = trans.generate()
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return dst
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@ -0,0 +1,154 @@
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# -*- coding:utf-8 -*-
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# Author: RubanSeven
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# Reference: https://github.com/RubanSeven/Text-Image-Augmentation-python
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import math
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import numpy as np
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class WarpMLS:
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def __init__(self, src, src_pts, dst_pts, dst_w, dst_h, trans_ratio=1.):
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self.src = src
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self.src_pts = src_pts
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self.dst_pts = dst_pts
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self.pt_count = len(self.dst_pts)
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self.dst_w = dst_w
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self.dst_h = dst_h
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self.trans_ratio = trans_ratio
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self.grid_size = 100
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self.rdx = np.zeros((self.dst_h, self.dst_w))
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self.rdy = np.zeros((self.dst_h, self.dst_w))
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@staticmethod
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def __bilinear_interp(x, y, v11, v12, v21, v22):
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return (v11 * (1 - y) + v12 * y) * (1 - x) + (v21 *
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(1 - y) + v22 * y) * x
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def generate(self):
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self.calc_delta()
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return self.gen_img()
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def calc_delta(self):
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w = np.zeros(self.pt_count, dtype=np.float32)
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if self.pt_count < 2:
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return
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i = 0
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while 1:
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if self.dst_w <= i < self.dst_w + self.grid_size - 1:
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i = self.dst_w - 1
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elif i >= self.dst_w:
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break
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j = 0
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while 1:
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if self.dst_h <= j < self.dst_h + self.grid_size - 1:
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j = self.dst_h - 1
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elif j >= self.dst_h:
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break
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sw = 0
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swp = np.zeros(2, dtype=np.float32)
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swq = np.zeros(2, dtype=np.float32)
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new_pt = np.zeros(2, dtype=np.float32)
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cur_pt = np.array([i, j], dtype=np.float32)
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k = 0
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for k in range(self.pt_count):
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if i == self.dst_pts[k][0] and j == self.dst_pts[k][1]:
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break
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w[k] = 1. / (
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(i - self.dst_pts[k][0]) * (i - self.dst_pts[k][0]) +
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(j - self.dst_pts[k][1]) * (j - self.dst_pts[k][1]))
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sw += w[k]
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swp = swp + w[k] * np.array(self.dst_pts[k])
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swq = swq + w[k] * np.array(self.src_pts[k])
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if k == self.pt_count - 1:
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pstar = 1 / sw * swp
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qstar = 1 / sw * swq
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miu_s = 0
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for k in range(self.pt_count):
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if i == self.dst_pts[k][0] and j == self.dst_pts[k][1]:
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continue
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pt_i = self.dst_pts[k] - pstar
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miu_s += w[k] * np.sum(pt_i * pt_i)
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cur_pt -= pstar
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cur_pt_j = np.array([-cur_pt[1], cur_pt[0]])
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for k in range(self.pt_count):
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if i == self.dst_pts[k][0] and j == self.dst_pts[k][1]:
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continue
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pt_i = self.dst_pts[k] - pstar
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pt_j = np.array([-pt_i[1], pt_i[0]])
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tmp_pt = np.zeros(2, dtype=np.float32)
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tmp_pt[0] = np.sum(pt_i * cur_pt) * self.src_pts[k][0] - \
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np.sum(pt_j * cur_pt) * self.src_pts[k][1]
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tmp_pt[1] = -np.sum(pt_i * cur_pt_j) * self.src_pts[k][0] + \
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np.sum(pt_j * cur_pt_j) * self.src_pts[k][1]
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tmp_pt *= (w[k] / miu_s)
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new_pt += tmp_pt
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new_pt += qstar
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else:
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new_pt = self.src_pts[k]
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self.rdx[j, i] = new_pt[0] - i
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self.rdy[j, i] = new_pt[1] - j
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j += self.grid_size
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i += self.grid_size
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def gen_img(self):
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src_h, src_w = self.src.shape[:2]
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dst = np.zeros_like(self.src, dtype=np.float32)
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for i in np.arange(0, self.dst_h, self.grid_size):
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for j in np.arange(0, self.dst_w, self.grid_size):
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ni = i + self.grid_size
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nj = j + self.grid_size
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w = h = self.grid_size
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if ni >= self.dst_h:
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ni = self.dst_h - 1
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h = ni - i + 1
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if nj >= self.dst_w:
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nj = self.dst_w - 1
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w = nj - j + 1
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di = np.reshape(np.arange(h), (-1, 1))
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dj = np.reshape(np.arange(w), (1, -1))
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delta_x = self.__bilinear_interp(
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di / h, dj / w, self.rdx[i, j], self.rdx[i, nj],
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self.rdx[ni, j], self.rdx[ni, nj])
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||||
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
|
||||
Loading…
Reference in New Issue