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detection_exam_num.py
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detection_exam_num.py
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'''
Author: wangxin
Date: 2021-05-25 10:31:01
LastEditTime: 2021-07-01 13:55:35
LastEditors: Please set LastEditors
Description: 检测考号
'''
# coding=utf-8
import cv2
import numpy as np
from PIL import Image
from utils import sort_contours
def get_exam_num_area(image_path):
""" 获取图片中待检测的考号填充区域
Args:
image_path (String): 图片地址
Returns:
[type]: [description]
"""
image = Image.open(image_path)
image_width = image.width
img = cv2.imread(image_path)
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
threshold_img = cv2.threshold(gray_img, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
# 先腐蚀与膨胀, 高亮化学生填充的考号
kernel = np.ones((9, 9), np.uint8)
erode_img = cv2.erode(threshold_img, kernel, iterations=1)
kernel = np.ones((9, 9), np.uint8)
dilate_img = cv2.dilate(erode_img, kernel, iterations=1)
# 学生填充的考号，最左边边缘的x轴坐标
exam_number_left_x = float("inf")
# 学生填充的考号，最右边边缘的x轴坐标
exam_number_right_x = 0
cnts, _ = cv2.findContours(dilate_img.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
for c in cnts:
(x, y, w, h) = cv2.boundingRect(c)
if x > image_width / 2:
if x < exam_number_left_x:
exam_number_left_x = x
if x + w > exam_number_right_x:
exam_number_right_x = x + w
# 通过x轴坐标，缩小待检测区域的范围
threshold_img = threshold_img[:, exam_number_left_x - 15:exam_number_right_x + 15]
# 再通过检测图片中面积最大的轮廓（考号手写区域, 而不是填充区域）, 进一步缩小范围
cnts, _ = cv2.findContours(threshold_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)
mix_y = None
num_card_cnt = None
for c in cnts:
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.06 * peri, True)
(x, y, w, h) = cv2.boundingRect(approx)
if len(approx) == 4:
cv2.imwrite('out/num_card.jpg', threshold_img[y:y + h, x:x + w])
num_card_cnt = c
mix_y = y + h
break
threshold_img = threshold_img[mix_y:, :]
return threshold_img, num_card_cnt
def get_exam_num_height(img):
""" 获取考号填充区域, 行中心与行中心的y轴坐标间隔
Args:
img ([type]): 图片
Returns:
[float]: 行中心与行中心的y轴坐标间隔
"""
# 膨胀
kernel = np.ones((5, 5), np.uint8)
dilate_img = cv2.dilate(img, kernel, iterations=1)
# 第一行待填充考号的中心的x轴坐标
first_line_center_y = None
# 第二行待填充考号的中心的x轴坐标
second_line_center_y = None
# 第一行待填充考号的底部边缘的y坐标
first_line_bottom_y = None
cnts, _ = cv2.findContours(dilate_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
cnts, _ = sort_contours(cnts, 'top-to-bottom')
for c in cnts:
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.06 * peri, True)
(x, y, w, h) = cv2.boundingRect(approx)
center_y = (2 * y + h) / 2
if h > 10:
if first_line_center_y is None:
first_line_center_y = center_y
first_line_bottom_y = y + h
if center_y > first_line_bottom_y and second_line_center_y is None:
second_line_center_y = center_y
break
print(type(second_line_center_y))
return second_line_center_y - first_line_center_y
def detection_exam_num(image_path):
""" 识别图片中学生填充的考号
Args:
image_path (String): 图片地址
Returns:
[list]: 识别的考号结果
"""
# 获取图片中考号填充区域范围
thresh_img, _ = get_exam_num_area(image_path)
# 获取考号填充区域, 每2行的中心y轴坐标间隔
line_y_height = get_exam_num_height(thresh_img)
# 腐蚀与膨胀
kernel = np.ones((9, 9), np.uint8)
erode_img = cv2.erode(thresh_img, kernel, iterations=1)
kernel = np.ones((9, 9), np.uint8)
dilate_img = cv2.dilate(erode_img, kernel, iterations=1)
# 学生填充考号的识别结果
num_card = []
cnts, _ = cv2.findContours(dilate_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
cnts, boundingBoxes = sort_contours(cnts, 'left-to-right')
for c in cnts:
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.06 * peri, True)
(x, y, w, h) = cv2.boundingRect(approx)
num_card.append(int(y / line_y_height))
return num_card
if __name__ == '__main__':
num_card = detection_exam_num('out/sub_answer_card_0.jpg')
print('num_card: ', num_card)