【11】python根據圖像處理的三維特徵繪製三維散點圖

【1】背景介紹

本文代碼是檢測一個物體的特徵包含面積，圓度，周長，然後生產一個三維散點圖。

【2】圖像處理類（ConcludeFeature.py）

import cv2
import numpy as np
import math

class ConcludeFeatures:

	#初始化參數
	def __init__(self, image):
		self.image= image

	#圖像預處理
	def pre_process(self):
		grayImg= cv2.cvtColor(self.image, cv2.COLOR_BGR2GRAY)
		r, threImg = cv2.threshold(grayImg, 102, 255, cv2.THRESH_BINARY_INV)
		return  threImg

	#計算圓度
	def compute_roundness(self):
		contours, hierarchy = cv2.findContours(np.array(self.image, dtype=np.uint8), cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
		numbercontous=len(contours)
		a = cv2.contourArea(contours[0]) * 4 * math.pi
		b = math.pow(cv2.arcLength(contours[0], True), 2)
		if b == 0:
			return 0
		return a / b

	#計算周長
	def copute_length(self):
		contours, hierarchy = cv2.findContours(np.array(self.image, dtype=np.uint8), cv2.RETR_TREE,
		cv2.CHAIN_APPROX_SIMPLE)
		length=cv2.arcLength(contours[0], True)
		return  length

	#計算面積
	def compute_area(self):
		contours, hierarchy = cv2.findContours(np.array(self.image, dtype=np.uint8), cv2.RETR_TREE,
		cv2.CHAIN_APPROX_SIMPLE)
		area = cv2.contourArea(contours[0])
		return  area

【3】圖像數據導入類（dataprocess）

import os
import  cv2

#載入圖片  處理後保存到一個文件夾
def read__image(open_path,save_path):
    nums=0
    images=[]
    for dir_image in os.listdir(open_path): # os.listdir() 方法用於返回指定的文件夾包含的文件或文件夾的名字的列表
        full_path = os.path.abspath(os.path.join(open_path,dir_image))
        if dir_image.endswith('.bmp'):
            image = cv2.imread(full_path)
            image_path = save_path+'%s-%s.jpg' % ('Cell',str(nums))
            cv2.imwrite(image_path, image)
            nums=nums+1

#載入圖片，處理後保存到一個列表中
def GetImg(open_path):
    patch=[]
    for dir_image in os.listdir(open_path): # os.listdir() 方法用於返回指定的文件夾包含的文件或文件夾的名字的列表
        full_path = os.path.abspath(os.path.join(open_path,dir_image))
        if dir_image.endswith('.jpg'):
            image = cv2.imread(full_path)
            #resImg=cv2.resize(image,(227,227))
            patch.append(image)
    return  patch

【4】主程序（main.py）

import cv2
import numpy as np
from ConcludeFeature import *
from dataprocess import GetImg
import matplotlib.pyplot as plt
from sklearn import preprocessing

img=cv2.imread("1.bmp",1)
picpath='D:\\pythonprocedure\\FeaturesDection\\4539class\\'
presave='D:\\pythonprocedure\\FeaturesDection\\4539threshold\\'
imgs=[]
imgs=GetImg(picpath)
featurepoint=[]

for index in range(len(imgs)):
	#preprocess image
	grayImg= cv2.cvtColor(imgs[index], cv2.COLOR_BGR2GRAY)
	r, threImg = cv2.threshold(grayImg, 102, 255, cv2.THRESH_BINARY_INV)
	#save image
	image_path = presave+'%s-%s.jpg' % ('precell',str(index))
	cv2.imwrite(image_path, threImg)
	#計算特徵參數
	conclude=ConcludeFeatures(threImg)
	area=conclude.compute_area()
	#保留三位有效數字
	area=round(area,3)
	roundness=conclude.compute_roundness()
	roundness=round(roundness,3)
	length=conclude.copute_length()
	length=round(length,3)
	#特徵結果保存
	point=[area,roundness,length]
	featurepoint.append(point)

#列表轉換爲數組
featurearray=np.array(featurepoint)

#數組歸一化
min_max_scaler = preprocessing.MinMaxScaler()
featurearray_minmax = min_max_scaler.fit_transform(featurearray)

#可視化
# 創建一個三維的繪圖工程
#繪製歸一化後的結果圖
ax=plt.subplot(121, projection='3d')
for index_point in range(len(featurearray_minmax)):
	x, y, z = featurearray_minmax[index_point][0], featurearray_minmax[index_point][1], featurearray_minmax[index_point][2]
	ax.scatter(x,y,z,s=1,c = 'r',marker = 'o')
ax.set_zlabel('Z-length')  # 座標軸
ax.set_ylabel('Y-roundness')
ax.set_xlabel('X-area')

#繪製未歸一化的圖
ax2=plt.subplot(122, projection='3d')
for index_point in range(len(featurearray)):
	x, y, z = featurearray[index_point][0], featurearray[index_point][1], featurearray[index_point][2]
	ax2.scatter(x,y,z,s=1,c = 'r',marker = 'o')
ax2.set_zlabel('Z-length')  # 座標軸
ax2.set_ylabel('Y-roundness')
ax2.set_xlabel('X-area')

#scatter(x,y,z,s=10,c = 'b',linewidths=5,marker = 'p')
'''
c:標記點的大小，linewidth:線寬，marker:標記點的形狀
'''

#顯示結果圖
plt.show()

cv2.waitKey()

【5】結果展示

【6】參考

Python中scatter函數參數詳解

plt.subplot()函數解析