python圖像灰度
⑴ '求助'python怎麼判斷圖片是否為灰度圖
這里判斷是否為灰度圖的標準是：每一個像素所對應的R、G、B的值是否相等。
def is_color_image(url):
im=Image.open(url)
pix=im.convert('RGB')
width=im.size[0]
height=im.size[1]
oimage_color_type="Grey Image"
is_color=[]
for x in range(width):
for y in range(height):
r,g,b=pix.getpixel((x,y))
r=int(r)
g=int(g)
b=int(b)
if (r==g) and (g==b):
pass
else:
oimage_color_type='Color Image'
return oimage_color_type
⑵ python轉換灰度圖像，為什麼結果出來以後是發黃色的 圖一是彩圖轉換後的結果，圖二是代碼
imshow（im，cmap='gray'）
⑶ 數字圖像處理Python實現圖像灰度變換、直方圖均衡、均值濾波
import CV2
import
import numpy as np
import random
使用的是pycharm
因為最近看了《銀翼殺手2049》，裡面Joi實在是太好看了所以原圖像就用Joi了
要求是灰度圖像，所以第一步先把圖像轉化成灰度圖像
# 讀入原始圖像
img = CV2.imread('joi.jpg')
# 灰度化處理
gray = CV2.cvtColor(img, CV2.COLOR_BGR2GRAY)
CV2.imwrite('img.png', gray)
第一個任務是利用分段函數增強灰度對比，我自己隨便寫了個函數大致是這樣的
def chng(a):
if a < 255/3:
b = a/2
elif a < 255/3*2:
b = (a-255/3)*2 + 255/6
else:
b = (a-255/3*2)/2 + 255/6 +255/3*2
return b
rows = img.shape[0]
cols = img.shape[1]
cover = .deep(gray)
for i in range(rows):
for j in range(cols):
cover[i][j] = chng(cover[i][j])
CV2.imwrite('cover.png', cover)
下一步是直方圖均衡化
# histogram equalization
def hist_equal(img, z_max=255):
H, W = img.shape
# S is the total of pixels
S = H * W * 1.
out = img.()
sum_h = 0.
for i in range(1, 255):
ind = np.where(img == i)
sum_h += len(img[ind])
z_prime = z_max / S * sum_h
out[ind] = z_prime
out = out.astype(np.uint8)
return out
covereq = hist_equal(cover)
CV2.imwrite('covereq.png', covereq)
在實現濾波之前先添加高斯雜訊和椒鹽雜訊（代碼來源於網路）
不知道這個椒鹽雜訊的名字是誰起的感覺隔壁小孩都饞哭了
用到了random.gauss()
percentage是雜訊佔比
def GaussianNoise(src,means,sigma,percetage):
NoiseImg=src
NoiseNum=int(percetage*src.shape[0]*src.shape[1])
for i in range(NoiseNum):
randX=random.randint(0,src.shape[0]-1)
randY=random.randint(0,src.shape[1]-1)
NoiseImg[randX, randY]=NoiseImg[randX,randY]+random.gauss(means,sigma)
if NoiseImg[randX, randY]< 0:
NoiseImg[randX, randY]=0
elif NoiseImg[randX, randY]>255:
NoiseImg[randX, randY]=255
return NoiseImg
def PepperandSalt(src,percetage):
NoiseImg=src
NoiseNum=int(percetage*src.shape[0]*src.shape[1])
for i in range(NoiseNum):
randX=random.randint(0,src.shape[0]-1)
randY=random.randint(0,src.shape[1]-1)
if random.randint(0,1)<=0.5:
NoiseImg[randX,randY]=0
else:
NoiseImg[randX,randY]=255
return NoiseImg
covereqg = GaussianNoise(covereq, 2, 4, 0.8)
CV2.imwrite('covereqg.png', covereqg)
covereqps = PepperandSalt(covereq, 0.05)
CV2.imwrite('covereqps.png', covereqps)
下面開始均值濾波和中值濾波了
就以n x n為例，均值濾波就是用這n x n個像素點灰度值的平均值代替中心點，而中值就是中位數代替中心點，邊界點周圍補0；前兩個函數的作用是算出這個點的灰度值，後兩個是對整張圖片進行
#均值濾波模板
def mean_filter(x, y, step, img):
sum_s = 0
for k in range(x-int(step/2), x+int(step/2)+1):
for m in range(y-int(step/2), y+int(step/2)+1):
if k-int(step/2) 0 or k+int(step/2)+1 > img.shape[0]
or m-int(step/2) 0 or m+int(step/2)+1 > img.shape[1]:
sum_s += 0
else:
sum_s += img[k][m] / (step*step)
return sum_s
#中值濾波模板
def median_filter(x, y, step, img):
sum_s=[]
for k in range(x-int(step/2), x+int(step/2)+1):
for m in range(y-int(step/2), y+int(step/2)+1):
if k-int(step/2) 0 or k+int(step/2)+1 > img.shape[0]
or m-int(step/2) 0 or m+int(step/2)+1 > img.shape[1]:
sum_s.append(0)
else:
sum_s.append(img[k][m])
sum_s.sort()
return sum_s[(int(step*step/2)+1)]
def median_filter_go(img, n):
img1 = .deep(img)
for i in range(img.shape[0]):
for j in range(img.shape[1]):
img1[i][j] = median_filter(i, j, n, img)
return img1
def mean_filter_go(img, n):
img1 = .deep(img)
for i in range(img.shape[0]):
for j in range(img.shape[1]):
img1[i][j] = mean_filter(i, j, n, img)
return img1
完整main代碼如下：
if __name__ == "__main__":
# 讀入原始圖像
img = CV2.imread('joi.jpg')
# 灰度化處理
gray = CV2.cvtColor(img, CV2.COLOR_BGR2GRAY)
CV2.imwrite('img.png', gray)
rows = img.shape[0]
cols = img.shape[1]
cover = .deep(gray)
for i in range(rows):
for j in range(cols):
cover[i][j] = chng(cover[i][j])
CV2.imwrite('cover.png', cover)
covereq = hist_equal(cover)
CV2.imwrite('covereq.png', covereq)
covereqg = GaussianNoise(covereq, 2, 4, 0.8)
CV2.imwrite('covereqg.png', covereqg)
covereqps = PepperandSalt(covereq, 0.05)
CV2.imwrite('covereqps.png', covereqps)
meanimg3 = mean_filter_go(covereqps, 3)
CV2.imwrite('medimg3.png', meanimg3)
meanimg5 = mean_filter_go(covereqps, 5)
CV2.imwrite('meanimg5.png', meanimg5)
meanimg7 = mean_filter_go(covereqps, 7)
CV2.imwrite('meanimg7.png', meanimg7)
medimg3 = median_filter_go(covereqg, 3)
CV2.imwrite('medimg3.png', medimg3)
medimg5 = median_filter_go(covereqg, 5)
CV2.imwrite('medimg5.png', medimg5)
medimg7 = median_filter_go(covereqg, 7)
CV2.imwrite('medimg7.png', medimg7)
medimg4 = median_filter_go(covereqps, 7)
CV2.imwrite('medimg4.png', medimg4)
⑷ python io. imread如何設置參數，使讀取的圖片為灰度圖
方法一：在使用OpenCV讀取圖片的同時將圖片轉換為灰度圖：
img = cv2.imread(imgfile, cv2.IMREAD_GRAYSCALE)
print("cv2.imread(imgfile, cv2.IMREAD_GRAYSCALE)結果如下：")
print('大小：{}'.format(img.shape))
print("類型：%s"%type(img))
print(img)
運行結果如下圖所示：
方法二：使用OpenCV，先讀取圖片，然後在轉換為灰度圖：
img = cv2.imread(imgfile)
#print(img.shape)
#print(img)
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) #Y = 0.299R + 0.587G + 0.114B
print("cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)結果如下：")
print('大小:{}'.format(gray_img.shape))
print("類型：%s" % type(gray_img))
print(gray_img)
運行結果如下：
方法三：使用PIL庫中的Image模塊：
img = np.array(Image.open(imgfile).convert('L'), 'f') #讀取圖片，灰度化，轉換為數組，L = 0.299R + 0.587G + 0.114B。'f'為float類型
print("Image方法的結果如下：")
print('大小：{}'.format(img.shape))
print("類型：%s" % type(img))
print(img)