用python按照图像灰度值统计并筛选图片的操作(PIL,shutil,os)
我就废话不多说了,大家还是直接看代码吧!
importPIL.Image importnumpy importos importshutil defsum_right(path): img=PIL.Image.open(path) array=numpy.array(img) num=array.sum(axis=0) print(type(num)) res_left=0 res_right=0 foriinrange(256,512): res_right+=num[i] print(res_right) if__name__=='__main__': dir2=r"C:\Users\Howsome\Desktop\tst" dir1=r"C:\Users\Howsome\Desktop\AB" names=os.listdir(dir1) n=len(names) print("文件数量",n) res=0 average_5=25565356 average_25=26409377 average_5_right=10006019 #average_tmp=(average_25+average_5)//2 count=0 #show(os.path.join(dir1,"uni4F6C.png")) foriinrange(n): #取图片 img=PIL.Image.open(os.path.join(dir1,names[i])) file=os.path.join(dir1,names[i]) rmfile=os.path.join(dir2,names[i]) array=numpy.array(img) num=array.sum(axis=0) res_right=0 foriinrange(256,512): res_right+=num[i] average_5_right+=res_right/n ifres_right>average_5_right: shutil.copyfile(file,rmfile) os.remove(file) count+=1 print(average_5_right) print(count)
补充知识:python遍历灰度图像像素方法总结
啥也不说了,看代码吧!
importnumpyasnp importmatplotlib.pyplotasplt importcv2 importtime img=cv2.imread('lena.jpg',0) #以遍历每个像素取反为例 #方法1 t1=time.time() img1=np.copy(img) rows,cols=img1.shape[:2] forrowinrange(rows): forcolinrange(cols): img[row,col]=255-img[row,col] t2=time.time() print('方法1所需时间:',t2-t1) #方法2 t3=time.time() img2=np.copy(img) rows,cols=img2.shape[:2] img2=img2.reshape(rows*cols) #print(img2) foriinrange(rows*cols): img2[i]=255-img2[i] img2=img2.reshape(rows,cols) #print(img2) t4=time.time() print('方法2所需时间:',t4-t3) #方法3 t5=time.time() img3=np.copy(img) #使用多维迭代生成器 for(x,y),pixelinnp.ndenumerate(img3): img3[x,y]=255-pixel t6=time.time() print('方法3所需时间:',t6-t5)
测试结果:
方法1所需时间:0.14431977272033691 方法2所需时间:0.13863205909729004 方法3所需时间:0.24196243286132812
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