环境:Python3.8 和 OpenCV
内容:通过图像颜色进行文字提取
文字提取步骤
1. 模糊图片,削弱噪声
2. 获取二值图
3. 形态学操作,完善二值图
4. 轮廓提取,获得字符区域
import cv2 as cv
import numpy as np
import matplotlib.pyplot as plt
# 封装图片显示函数
def image_show(image):
if image.ndim == 2:
plt.imshow(image, cmap='gray')
else:
image = cv.cvtColor(image, cv.COLOR_BGR2RGB)
plt.imshow(image)
plt.show()
if __name__ == '__main__':
# 读取原图
img_words = cv.imread('words.jpg')
# 高斯模糊
img_gaussian = cv.blur(img_words, ksize=(5, 5))
# 区域颜色
colors = np.array([[0, 135, 195],
[0, 220, 235],
[15, 0, 175],
[50, 80, 135],
[225, 90, 125]])
# 波动范围
dis = 40
# 获取掩码
masks = []
for i in range(len(colors)):
mask = cv.inRange(img_words, colors[i] - dis, colors[i] + dis)
masks.append(mask)
# 形态学腐蚀--开运算
kernel = cv.getStructuringElement(cv.MORPH_RECT, ksize=(3, 3))
for i in range(len(colors)):
masks[i] = cv.morphologyEx(masks[i], cv.MORPH_OPEN, kernel)
# 寻找轮廓
words = [] # 存储单词
minArea = 2000 # 最小面积
for mask in masks:
cns, hir = cv.findContours(mask, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
for cnt in cns:
if cv.contourArea(cnt) > minArea:
[x, y, w, h] = cv.boundingRect(cnt)
words.append(mask[y: y + h, x: x + w])
# 循环显示结果
for word in words:
image_show(word)
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