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main.py
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main.py
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import cv2
import matplotlib.pyplot as plt
import numpy as np
import time
import math
if __name__=='__main__':
start=time.perf_counter()
file_name='D:\FFOutput\\1600357086421.jpg'
img_cv= cv2.imread(file_name)
#把图像数组的正确读取（cv2本来是BGR）
img_rgb=cv2.cvtColor(img_cv, cv2.COLOR_BGR2RGB)
# plt.imshow(img_cv)
# plt.show()
plt.imshow(img_rgb)
plt.show()
#输出参数，看看
print('The type of the image:', type(img_cv))
print('shape{}'.format(img_cv.shape))
print('height: {}'.format(img_cv.shape[0]))
print('the de of img_rgb:{}'.format(img_cv.shape[2]))
print('max of img_rgb:{}'.format(img_cv.max()))
#maxar=max(img_cv.all())
#print('max of img_R:{}'.format(maxar))
#要求每个分量里的最大值呢？
#splite the RGB channel
numtx1=np.zeros_like(img_cv)
numtx2=np.zeros_like(img_cv)
numtx3 = np.zeros_like(img_cv)
numtx1[:,:,0]=img_rgb[:,:,0]
numtx2[:,:,1]=img_rgb[:,:,1]
numtx3[:,:,2] = img_rgb[:,:,2]
img_gray=cv2.cvtColor(img_cv,cv2.COLOR_BGR2GRAY)
#分离后的图显示
# plt.imshow(img_gray)
# plt.show()
# plt.imshow(numtx1)
# plt.show()
# plt.imshow( numtx2)
# plt.show()
# plt.imshow( numtx3)
# plt.show()
#图片上加入文字
# cv2.putText(img_cv, "yaodaoji", (200, 100), \
# cv2.FONT_HERSHEY_COMPLEX, 2.0, (100, 200, 200), 5)
# plt.imshow(img_cv)
# plt.show()
#绘制灰度直方图。简单函数试试
# x=np.linspace(-3,3,40)
# y=2*x
# y2=1/x
#
# plt.figure()
# plt.plot(x,y)
# plt.show()
#
# plt.figure()
# plt.bar(x, y2)
# plt.show()
color = ("blue", "green", "red")
for i, color in enumerate(color):
#老师教的办法。用起来
hist = cv2.calcHist([img_rgb], [i], None, [256], [0, 256])
plt.plot(hist, color=color)
plt.xlim([0, 256])
plt.show()
cv2.waitKey()
# #分别自己bar实现三通道直方图
# plt.figure()
# #把矩阵变成一维数组
# mtx1r=numtx1.ravel()
# #去掉零
# b = np.argwhere(mtx1r==0)
# mtx1r = np.delete(mtx1r, b)
# hist =np.zeros(256)
# #实现x，y（数组）
# xaxis=np.linspace(0,255,256)
# for i in range(255):
# mt1_tf=(mtx1r==i)
# hist[i]=np.sum(mt1_tf)
# plt.title("red bar")
# plt.bar(xaxis, hist)
# plt.show()
#
# #第二个通道就照写，改一下数字
# plt.figure()
# mtx2r=numtx2.ravel()
# b = np.argwhere(mtx2r==0)
# mtx2r = np.delete(mtx2r, b)
# hist =np.zeros(256)
# xaxis=np.linspace(0,255,256)
# for i in range(255):
# mt1_tf=(mtx2r==i)
# hist[i]=np.sum(mt1_tf)
# plt.title("green bar")
# plt.bar(xaxis, hist)
# plt.show()
#
# plt.figure()
# mtx3r=numtx3.ravel()
# b = np.argwhere(mtx3r==0)
# mtx3r = np.delete(mtx3r, b)
# hist =np.zeros(256)
# xaxis=np.linspace(0,255,256)
# for i in range(255):
# mt1_tf=(mtx3r==i)
# hist[i]=np.sum(mt1_tf)
# plt.title("blue bar")
# plt.bar(xaxis, hist)
# plt.show()
#下面 本质还是 利用hist函数实现的。不算自己写的
#比较运算速度那个我也还没做。
plt.figure()
mtx1r=numtx1.ravel()
b = np.argwhere(mtx1r==0)
mtx1r = np.delete(mtx1r, b)
plt.hist(mtx1r, 256, [0, 256])
plt.show()
cv2.waitKey()
plt.figure()
mtx2r=numtx2.ravel()
b = np.argwhere(mtx2r==0)
mtx2r = np.delete(mtx2r, b)
plt.hist(mtx2r, 256, [0, 256])
plt.show()
cv2.waitKey()
plt.figure()
mtx3r = numtx3.ravel()
b = np.argwhere(mtx3r == 0)
mtx3r = np.delete(mtx3r, b)
plt.hist(mtx3r, 256, [0, 256])
plt.show()
cv2.waitKey()
end=time.perf_counter()
print("{}".format(end-start))