add folder img

2/1.py

@@ -20,7 +20,7 @@ def histogram_equalization(im):
 
 
 if __name__ == "__main__":
-    im = cv.imread(r"src.jpg", cv.IMREAD_GRAYSCALE)
+    im = cv.imread(r"img\iris.jpg", cv.IMREAD_GRAYSCALE)
     im1 = global_linear_transmation(im, 0, 150)
     im2 = global_linear_transmation(im, 100)
     im3 = global_linear_transmation(im, 50, 150)
@@ -39,11 +39,11 @@ if __name__ == "__main__":
     plt.imshow(im4, cmap="gray")
     plt.title("equalized")
     plt.subplot(245)
-    plt.hist(im1.flatten(), 256, [0, 256])
+    plt.hist(im1.flatten(), 256, [0, 256])  # type: ignore
     plt.subplot(246)
-    plt.hist(im2.flatten(), 256, [0, 256])
+    plt.hist(im2.flatten(), 256, [0, 256])  # type: ignore
     plt.subplot(247)
-    plt.hist(im3.flatten(), 256, [0, 256])
+    plt.hist(im3.flatten(), 256, [0, 256])  # type: ignore
     plt.subplot(248)
-    plt.hist(im4.flatten(), 256, [0, 256])
+    plt.hist(im4.flatten(), 256, [0, 256])  # type: ignore
     plt.show()

2/2.py

@@ -10,7 +10,7 @@ def gamma_trans(img, gamma=1.0):
 
 
 if __name__ == "__main__":
-    im = cv.imread(r"src.jpg", cv.IMREAD_GRAYSCALE)
+    im = cv.imread(r"img\iris.jpg", cv.IMREAD_GRAYSCALE)
     im1 = gamma_trans(im, 0.5)
     im2 = gamma_trans(im, 1.5)
     plt.figure()

2/3.py

@@ -1,9 +1,9 @@
 import cv2 as cv
-from matplotlib import pyplot as plt
+import matplotlib.pyplot as plt
 import numpy as np
 
 if __name__ == "__main__":
-    img = cv.imread(r"src.jpg", 0)
+    img = cv.imread(r"img\iris.jpg", 0)
     fil1 = 1 / 16 * np.array([[1, 2, 1], [2, 4, 2], [1, 2, 1]])
     fil2 = 1 / 9 * np.array([[1, 1, 1], [1, 1, 1], [1, 1, 1]])
     fil3 = 1 / 10 * np.array([[1, 1, 1], [1, 2, 1], [1, 1, 1]])
@@ -12,7 +12,6 @@ if __name__ == "__main__":
     ImgSmoothed2 = cv.filter2D(img, -1, fil2, borderType=cv.BORDER_DEFAULT)
     ImgSmoothed3 = cv.filter2D(img, -1, fil3, borderType=cv.BORDER_DEFAULT)
     ImgSharp = cv.filter2D(img, -1, fil4, borderType=cv.BORDER_DEFAULT)
-
     plt.figure()
     plt.subplot(221)
     plt.imshow(ImgSmoothed1, cmap="gray")

2/4.py

@@ -33,15 +33,15 @@ def addGaussianNoise(src, means, sigma):
 
 
 if __name__ == "__main__":
-    im = cv.imread(r"src.jpg", cv.IMREAD_GRAYSCALE)
+    im = cv.imread(r"img\iris.jpg", cv.IMREAD_GRAYSCALE)
     im1 = addSaltAndPepper(im, 0.1)
     im11 = cv.blur(im1, (3, 3))
     im12 = cv.medianBlur(im1, 3)
     im13 = cv.GaussianBlur(im1, (3, 3), 1)
     im2 = addGaussianNoise(im, 0, 0.1)
-    im21 = cv.blur(im2, (3, 3))
-    im22 = cv.medianBlur(im2, 3)
-    im23 = cv.GaussianBlur(im2, (3, 3), 1)
+    im21 = cv.blur(im2, (3, 3))  # type: ignore
+    im22 = cv.medianBlur(im2, 3)  # type: ignore
+    im23 = cv.GaussianBlur(im2, (3, 3), 1)  # type: ignore
     plt.figure()
     plt.subplot(241)
     plt.imshow(im1, cmap="gray")

2/5.py

@@ -3,12 +3,11 @@ from matplotlib import pyplot as plt
 import numpy as np
 
 if __name__ == "__main__":
-    img = cv.imread(r"src.jpg", 0)
+    img = cv.imread(r"img\iris.jpg", 0)
     lplc = np.array([[0, -1, 0], [-1, 4, -1], [0, -1, 0]])
     lplcEnhance = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]])
     ImgLplc = cv.filter2D(img, -1, lplc, borderType=cv.BORDER_DEFAULT)
     ImgLplcEnhance = cv.filter2D(img, -1, lplcEnhance, borderType=cv.BORDER_DEFAULT)
-
     plt.figure()
     plt.subplot(131)
     plt.imshow(img, cmap="gray")