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# -*- coding: utf-8 -*-
# time: 2022/10/17 13:25
# file: ocr_utils.py
import cv2
import math
import numpy as np
from PIL import Image, ImageDraw, ImageFont
def resize_img(img, input_size=600):
"""
resize img and limit the longest side of the image to input_size
"""
img = np.array(img)
im_shape = img.shape
im_size_max = np.max(im_shape[0:2])
im_scale = float(input_size) / float(im_size_max)
img = cv2.resize(img, None, None, fx=im_scale, fy=im_scale)
return img
def draw_ocr(
image,
boxes,
txts=None,
scores=None,
drop_score=0.5,
font_path="./fonts/font.ttf"
):
"""
Visualize the results of OCR detection and recognition
args:
image(Image|array): RGB image
boxes(list): boxes with shape(N, 4, 2)
txts(list): the texts
scores(list): txxs corresponding scores
drop_score(float): only scores greater than drop_threshold will be visualized
font_path: the path of font which is used to draw text
return(array):
the visualized img
"""
if scores is None:
scores = [1] * len(boxes)
box_num = len(boxes)
for i in range(box_num):
if scores is not None and (scores[i] < drop_score or math.isnan(scores[i])):
continue
box = np.reshape(np.array(boxes[i]), [-1, 1, 2]).astype(np.int64)
image = cv2.polylines(np.array(image), [box], True, (255, 0, 0), 2)
if txts is not None:
img = np.array(resize_img(image, input_size=600))
txt_img = text_visual(
txts,
scores,
img_h=img.shape[0],
img_w=600,
threshold=drop_score,
font_path=font_path
)
img = np.concatenate([np.array(img), np.array(txt_img)], axis=1)
return img
return image
def draw_ocr_box_txt(
image,
boxes,
txts,
scores=None,
drop_score=0.5,
font_path="./fonts/font.ttf"
):
image = Image.fromarray(image)
h, w = image.height, image.width
img_left = image.copy()
img_right = Image.new('RGB', (w, h), (255, 255, 255))
import random
random.seed(0)
draw_left = ImageDraw.Draw(img_left)
draw_right = ImageDraw.Draw(img_right)
for idx, (box, txt) in enumerate(zip(boxes, txts)):
if scores is not None and scores[idx] < drop_score:
continue
color = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))
draw_left.polygon(
[
box[0][0], box[0][1], box[1][0], box[1][1], box[2][0],
box[2][1], box[3][0], box[3][1]
],
fill=color)
draw_right.polygon(
[
box[0][0], box[0][1], box[1][0], box[1][1], box[2][0],
box[2][1], box[3][0], box[3][1]
],
outline=color)
box_height = math.sqrt((box[0][0] - box[3][0])**2 + (box[0][1] - box[3][
1])**2)
box_width = math.sqrt((box[0][0] - box[1][0])**2 + (box[0][1] - box[1][
1])**2)
if box_height > 2 * box_width:
font_size = max(int(box_width * 0.9), 10)
font = ImageFont.truetype(font_path, font_size, encoding="utf-8")
cur_y = box[0][1]
for c in txt:
char_size = font.getsize(c)
draw_right.text((box[0][0] + 3, cur_y), c, fill=(0, 0, 0), font=font)
cur_y += char_size[1]
else:
font_size = max(int(box_height * 0.8), 10)
font = ImageFont.truetype(font_path, font_size, encoding="utf-8")
draw_right.text([box[0][0], box[0][1]], txt, fill=(0, 0, 0), font=font)
img_left = Image.blend(image, img_left, 0.5)
img_show = Image.new('RGB', (w * 2, h), (255, 255, 255))
img_show.paste(img_left, (0, 0, w, h))
img_show.paste(img_right, (w, 0, w * 2, h))
return np.array(img_show)
def str_count(s):
"""
Count the number of Chinese characters,
a single English character and a single number
equal to half the length of Chinese characters.
args:
s(string): the input of string
return(int):
the number of Chinese characters
"""
import string
count_zh = count_pu = 0
s_len = len(s)
en_dg_count = 0
for c in s:
if c in string.ascii_letters or c.isdigit() or c.isspace():
en_dg_count += 1
elif c.isalpha():
count_zh += 1
else:
count_pu += 1
return s_len - math.ceil(en_dg_count / 2)
def text_visual(
texts,
scores,
img_h=400,
img_w=600,
threshold=0.,
font_path="./fonts/font.ttf"
):
"""
create new blank img and draw txt on it
args:
texts(list): the text will be draw
scores(list|None): corresponding score of each txt
img_h(int): the height of blank img
img_w(int): the width of blank img
font_path: the path of font which is used to draw text
return(array):
"""
if scores is not None:
assert len(texts) == len(scores), "The number of txts and corresponding scores must match"
def create_blank_img():
blank_img = np.ones(shape=[img_h, img_w], dtype=np.int8) * 255
blank_img[:, img_w - 1:] = 0
blank_img = Image.fromarray(blank_img).convert("RGB")
draw_txt = ImageDraw.Draw(blank_img)
return blank_img, draw_txt
blank_img, draw_txt = create_blank_img()
font_size = 20
txt_color = (0, 0, 0)
font = ImageFont.truetype(font_path, font_size, encoding="utf-8")
gap = font_size + 5
txt_img_list = []
count, index = 1, 0
for idx, txt in enumerate(texts):
index += 1
if scores[idx] < threshold or math.isnan(scores[idx]):
index -= 1
continue
first_line = True
while str_count(txt) >= img_w // font_size - 4:
tmp = txt
txt = tmp[:img_w // font_size - 4]
if first_line:
new_txt = str(index) + ': ' + txt
first_line = False
else:
new_txt = ' ' + txt
draw_txt.text((0, gap * count), new_txt, txt_color, font=font)
txt = tmp[img_w // font_size - 4:]
if count >= img_h // gap - 1:
txt_img_list.append(np.array(blank_img))
blank_img, draw_txt = create_blank_img()
count = 0
count += 1
if first_line:
new_txt = str(index) + ': ' + txt + ' ' + '%.3f' % (scores[idx])
else:
new_txt = " " + txt + " " + '%.3f' % (scores[idx])
draw_txt.text((0, gap * count), new_txt, txt_color, font=font)
# whether add new blank img or not
if count >= img_h // gap - 1 and idx + 1 < len(texts):
txt_img_list.append(np.array(blank_img))
blank_img, draw_txt = create_blank_img()
count = 0
count += 1
txt_img_list.append(np.array(blank_img))
if len(txt_img_list) == 1:
blank_img = np.array(txt_img_list[0])
else:
blank_img = np.concatenate(txt_img_list, axis=1)
return np.array(blank_img)
def base64_to_cv2(b64str):
import base64
data = base64.b64decode(b64str.encode('utf8'))
data = np.fromstring(data, np.uint8)
data = cv2.imdecode(data, cv2.IMREAD_COLOR)
return data
def draw_boxes(image, boxes, scores=None, drop_score=0.5):
if scores is None:
scores = [1] * len(boxes)
for (box, score) in zip(boxes, scores):
if score < drop_score:
continue
box = np.reshape(np.array(box), [-1, 1, 2]).astype(np.int64)
image = cv2.polylines(np.array(image), [box], True, (255, 0, 0), 2)
return image
def get_rotate_crop_image(img, points):
'''
img_height, img_width = img.shape[0:2]
left = int(np.min(points[:, 0]))
right = int(np.max(points[:, 0]))
top = int(np.min(points[:, 1]))
bottom = int(np.max(points[:, 1]))
img_crop = img[top:bottom, left:right, :].copy()
points[:, 0] = points[:, 0] - left
points[:, 1] = points[:, 1] - top
'''
assert len(points) == 4, "shape of points must be 4*2"
img_crop_width = int(
max(
np.linalg.norm(points[0] - points[1]),
np.linalg.norm(points[2] - points[3])))
img_crop_height = int(
max(
np.linalg.norm(points[0] - points[3]),
np.linalg.norm(points[1] - points[2])))
pts_std = np.float32([[0, 0], [img_crop_width, 0],
[img_crop_width, img_crop_height],
[0, img_crop_height]])
M = cv2.getPerspectiveTransform(points, pts_std)
dst_img = cv2.warpPerspective(
img,
M, (img_crop_width, img_crop_height),
borderMode=cv2.BORDER_REPLICATE,
flags=cv2.INTER_CUBIC
)
dst_img_height, dst_img_width = dst_img.shape[0:2]
if dst_img_height * 1.0 / dst_img_width >= 1.5:
dst_img = np.rot90(dst_img)
return dst_img
if __name__ == '__main__':
pass
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