add distort and space
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tink2123
parent
5067126e8c
commit
97a3af3bbf
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@ -14,6 +14,7 @@ Global:
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character_type: ch
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character_dict_path: ./ppocr/utils/ppocr_keys_v1.txt
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loss_type: ctc
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distort: true
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reader_yml: ./configs/rec/rec_chinese_reader.yml
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pretrain_weights:
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checkpoints:
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@ -13,6 +13,7 @@ Global:
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max_text_length: 25
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character_type: en
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loss_type: ctc
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distort: true
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reader_yml: ./configs/rec/rec_icdar15_reader.yml
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pretrain_weights: ./pretrain_models/rec_mv3_none_bilstm_ctc/best_accuracy
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checkpoints:
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@ -45,6 +45,8 @@ class LMDBReader(object):
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self.use_tps = False
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if "tps" in params:
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self.ues_tps = True
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if "distort" in params:
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self.use_distort = params['distort']
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if params['mode'] == 'train':
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self.batch_size = params['train_batch_size_per_card']
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self.drop_last = True
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@ -142,7 +144,8 @@ class LMDBReader(object):
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label=label,
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char_ops=self.char_ops,
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loss_type=self.loss_type,
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max_text_length=self.max_text_length)
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max_text_length=self.max_text_length,
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distort=self.use_distort)
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if outs is None:
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continue
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yield outs
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@ -185,6 +188,8 @@ class SimpleReader(object):
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self.use_tps = False
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if "tps" in params:
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self.use_tps = True
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if "distort" in params:
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self.use_distort = params['distort']
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if params['mode'] == 'train':
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self.batch_size = params['train_batch_size_per_card']
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self.drop_last = True
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@ -232,9 +237,14 @@ class SimpleReader(object):
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img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
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label = substr[1]
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outs = process_image(img, self.image_shape, label,
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self.char_ops, self.loss_type,
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self.max_text_length)
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outs = process_image(
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img=img,
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image_shape=self.image_shape,
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label=label,
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char_ops=self.char_ops,
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loss_type=self.loss_type,
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max_text_length=self.max_text_length,
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distort=self.use_distort)
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if outs is None:
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continue
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yield outs
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@ -15,6 +15,7 @@
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import math
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import cv2
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import numpy as np
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import random
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from ppocr.utils.utility import initial_logger
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logger = initial_logger()
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@ -89,6 +90,252 @@ def get_img_data(value):
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return imgori
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def flag():
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"""
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flag
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"""
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return 1 if random.random() > 0.5000001 else -1
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def cvtColor(img):
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"""
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cvtColor
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"""
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hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
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delta = 0.001 * random.random() * flag()
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hsv[:, :, 2] = hsv[:, :, 2] * (1 + delta)
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new_img = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
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return new_img
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def blur(img):
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"""
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blur
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"""
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h, w, _ = img.shape
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if h > 10 and w > 10:
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return cv2.GaussianBlur(img, (5, 5), 1)
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else:
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return img
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def doudong(img):
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"""
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doudong
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"""
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w, h, _ = img.shape
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if h > 10 and w > 10:
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thres = min(w, h)
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s = int(random.random() * thres * 0.01)
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src_img = img.copy()
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for i in range(s):
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img[i:, i:, :] = src_img[:w - i, :h - i, :]
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return img
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else:
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return img
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def add_gasuss_noise(image, mean=0, var=0.1):
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noise = np.random.normal(mean, var**0.5, image.shape)
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out = image + 0.5 * noise
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out = np.clip(out, 0, 255)
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out = np.uint8(out)
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return out
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def get_crop(image):
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"""
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random crop
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"""
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h, w, _ = image.shape
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top_min = 1
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top_max = 8
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top_crop = int(random.randint(top_min, top_max))
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crop_img = image.copy()
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ratio = random.randint(0, 1)
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if ratio:
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crop_img = crop_img[top_crop:h, :, :]
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else:
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crop_img = crop_img[0:h - top_crop, :, :]
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return crop_img
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class Config:
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"""
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Config
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"""
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def __init__(self, ):
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self.anglex = random.random() * 30
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self.angley = random.random() * 15
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self.anglez = random.random() * 10
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self.fov = 42
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self.r = 0
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self.shearx = random.random() * 0.3
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self.sheary = random.random() * 0.05
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self.borderMode = cv2.BORDER_REPLICATE
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def make(self, w, h, ang):
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"""
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make
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"""
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self.anglex = random.random() * 5 * flag()
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self.angley = random.random() * 5 * flag()
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self.anglez = -1 * random.random() * int(ang) * flag()
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self.fov = 42
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self.r = 0
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self.shearx = 0
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self.sheary = 0
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self.borderMode = cv2.BORDER_REPLICATE
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self.w = w
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self.h = h
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self.perspective = 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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self.noise = True
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self.dou = False
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self.blur = True
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self.color = True
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def rad(x):
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"""
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rad
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"""
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return x * np.pi / 180
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def get_warpR(config):
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"""
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get_warpR
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"""
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anglex, angley, anglez, fov, w, h, r = \
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config.anglex, config.angley, config.anglez, config.fov, config.w, config.h, config.r
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if w > 69 and w < 112:
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anglex = anglex * 1.5
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z = np.sqrt(w**2 + h**2) / 2 / np.tan(rad(fov / 2))
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# Homogeneous coordinate transformation matrix
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rx = np.array([[1, 0, 0, 0],
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[0, np.cos(rad(anglex)), -np.sin(rad(anglex)), 0], [
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0,
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-np.sin(rad(anglex)),
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np.cos(rad(anglex)),
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0,
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], [0, 0, 0, 1]], np.float32)
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ry = np.array([[np.cos(rad(angley)), 0, np.sin(rad(angley)), 0],
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[0, 1, 0, 0], [
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-np.sin(rad(angley)),
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0,
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np.cos(rad(angley)),
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0,
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], [0, 0, 0, 1]], np.float32)
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rz = np.array([[np.cos(rad(anglez)), np.sin(rad(anglez)), 0, 0],
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[-np.sin(rad(anglez)), np.cos(rad(anglez)), 0, 0],
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[0, 0, 1, 0], [0, 0, 0, 1]], np.float32)
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r = rx.dot(ry).dot(rz)
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# generate 4 points
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pcenter = np.array([h / 2, w / 2, 0, 0], np.float32)
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p1 = np.array([0, 0, 0, 0], np.float32) - pcenter
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p2 = np.array([w, 0, 0, 0], np.float32) - pcenter
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p3 = np.array([0, h, 0, 0], np.float32) - pcenter
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p4 = np.array([w, h, 0, 0], np.float32) - pcenter
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dst1 = r.dot(p1)
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dst2 = r.dot(p2)
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dst3 = r.dot(p3)
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dst4 = r.dot(p4)
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list_dst = [dst1, dst2, dst3, dst4]
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org = np.array([[0, 0], [w, 0], [0, h], [w, h]], np.float32)
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dst = np.zeros((4, 2), np.float32)
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# Project onto the image plane
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for i in range(4):
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dst[i, 0] = list_dst[i][0] * z / (z - list_dst[i][2]) + pcenter[0]
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dst[i, 1] = list_dst[i][1] * z / (z - list_dst[i][2]) + pcenter[1]
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warpR = cv2.getPerspectiveTransform(org, dst)
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dst1, dst2, dst3, dst4 = dst
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r1 = int(min(dst1[1], dst2[1]))
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r2 = int(max(dst3[1], dst4[1]))
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c1 = int(min(dst1[0], dst3[0]))
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c2 = int(max(dst2[0], dst4[0]))
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try:
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ratio = min(1.0 * h / (r2 - r1), 1.0 * w / (c2 - c1))
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dx = -c1
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dy = -r1
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T1 = np.float32([[1., 0, dx], [0, 1., dy], [0, 0, 1.0 / ratio]])
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ret = T1.dot(warpR)
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except:
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ratio = 1.0
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T1 = np.float32([[1., 0, 0], [0, 1., 0], [0, 0, 1.]])
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ret = T1
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return ret, (-r1, -c1), ratio, dst
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def get_warpAffine(config):
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"""
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get_warpAffine
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"""
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anglez = config.anglez
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rz = np.array([[np.cos(rad(anglez)), np.sin(rad(anglez)), 0],
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[-np.sin(rad(anglez)), np.cos(rad(anglez)), 0]], np.float32)
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return rz
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def warp(img, ang):
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"""
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warp
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"""
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h, w, _ = img.shape
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config = Config()
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config.make(w, h, ang)
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new_img = img
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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 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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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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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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new_img = cvtColor(new_img)
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if config.dou:
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new_img = doudong(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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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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new_img = 255 - new_img
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return new_img
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def process_image(img,
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image_shape,
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label=None,
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@ -96,7 +343,10 @@ def process_image(img,
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loss_type=None,
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max_text_length=None,
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tps=None,
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infer_mode=False):
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infer_mode=False,
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distort=False):
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if distort:
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img = warp(img, 10)
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if infer_mode and char_ops.character_type == "ch" and not tps:
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norm_img = resize_norm_img_chinese(img, image_shape)
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else:
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@ -6620,4 +6620,5 @@ j
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緖
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續
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紹
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懮
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懮
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