【代码篇】读取图片中曲线的坐标值（python版）

任务：

识别该图片中每隔0.01s的荷载数据，并导入到Excel中。

思路：

1. 预处理图片

2. 截取竖条

2.1 代码

import cv2
from PIL import Image
import sys


def cut_image(image):
    width, height = image.size
    print(width, height)
    item_width = int(width / 800)
    box_list = []
    # (left, upper, right, lower)
    for i in range(800):#0.01s为一段，8s=800个0.01s
        box = (i*item_width,0,(i+1)*item_width,height)
        box_list.append(box)

    image_list = [image.crop(box) for box in box_list] #crop用于切割图片
    return image_list

def save_images(image_list):
    index = 1
    for image in image_list:
        image.save('D:/python_opencv/img/'+str(index) + '.png', 'PNG')
        index += 1

if __name__ == '__main__':
    file_path = "D:/002.png"
    image = Image.open(file_path)
    #image.show()
    image_list = cut_image(image)
    save_images(image_list)

2.2 效果

3. 对每个竖条图片处理

3.1 代码

import cv2
import numpy as np
import shutil
import os
import math 
import matplotlib.pyplot as plt

Img_FOLDER = "D:/python_opencv/img"

list_y = []

for i in range(800): 
    img = cv2.imread('D:/python_opencv/img/'+str(i+1) + '.png')
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    low_hsv = np.array([0,0,46]) 
    high_hsv = np.array([180,255,220])
    mask = cv2.inRange(hsv,lowerb=low_hsv,upperb=high_hsv)     #利用cv2.inRange函数设阈值，去除背景部分
    dst = cv2.GaussianBlur(mask,(3,3),0)
    yx = np.column_stack(np.where(mask==255))
    yx = 702-yx
    y = max(yx[:,0]) 
    y_new = y*50/702 - 10  #根据像素与所需尺度的关系确定
    list_y.append(y_new)

list_x = np.arange(0,8,0.01) 

plt.plot(list_x,list_y,label='Frist line',linewidth=3,color='r') 
plt.show() 

cv2.waitKey(0)
cv2.destroyAllWindows()

3.2 效果

4. 导出

4.1 代码

import xlwt
wb = xlwt.Workbook()
# 添加一个表
ws = wb.add_sheet('result')

# 3个参数分别为行号，列号，和内容
# 需要注意的是行号和列号都是从0开始的

ws.write(0, 0, "x")
ws.write(0, 1, "y")
i = 1
for x in list_x:
    ws.write(i, 0, x)
    i += 1
    
j = 1
for y in list_y:
    ws.write(j, 1, y)
    j += 1

# 保存excel文件
wb.save('D:/python_opencv/result.xls')

4.2 效果