Add 6

6/1.py

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+import cv2 as cv
+import matplotlib.pyplot as plt
+
+plt.rcParams["font.sans-serif"] = ["SimSun"]
+
+img = cv.imread(r"img\wirebond-mask.tif", cv.IMREAD_GRAYSCALE)
+_, img_binary = cv.threshold(img, 128, 255, cv.THRESH_BINARY + cv.THRESH_OTSU)
+plt.figure()
+plt.subplot(141)
+plt.axis("off")
+plt.imshow(img_binary, cmap="gray")
+plt.title("原图像")
+i = 1
+for kernelSize in [11, 15, 45]:
+    kernel = cv.getStructuringElement(cv.MORPH_RECT, (kernelSize, kernelSize))
+    img_result = cv.erode(img_binary, kernel, iterations=1)
+    i += 1
+    plt.subplot(140 + i)
+    plt.axis("off")
+    plt.imshow(img_result, cmap="gray")
+    plt.title(f"{kernelSize}×{kernelSize}")
+plt.tight_layout()
+plt.show()

6/2.py

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+import cv2 as cv
+import matplotlib.pyplot as plt
+
+plt.rcParams["font.sans-serif"] = ["SimSun"]
+
+img = cv.imread(r"6\2.png", cv.IMREAD_GRAYSCALE)
+_, img_binary = cv.threshold(img, 128, 255, cv.THRESH_BINARY + cv.THRESH_OTSU)
+kernelSize = 40
+kernel = cv.getStructuringElement(cv.MORPH_ELLIPSE, (kernelSize, kernelSize))
+img_open = cv.morphologyEx(img_binary, cv.MORPH_OPEN, kernel)
+img_close = cv.morphologyEx(img_binary, cv.MORPH_CLOSE, kernel)
+plt.figure()
+plt.subplot(131)
+plt.axis("off")
+plt.imshow(img_binary, cmap="gray")
+plt.title("待处理的原图像")
+plt.subplot(132)
+plt.axis("off")
+plt.imshow(img_open, cmap="gray")
+plt.title("开运算的结果")
+plt.subplot(133)
+plt.axis("off")
+plt.imshow(img_close, cmap="gray")
+plt.title("闭运算的结果")
+plt.tight_layout()
+plt.show()

6/3.py

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+import cv2 as cv
+import matplotlib.pyplot as plt
+
+plt.rcParams["font.sans-serif"] = ["SimSun"]
+
+img = cv.imread(r"6\3.png", cv.IMREAD_GRAYSCALE)
+_, img_binary = cv.threshold(img, 128, 255, cv.THRESH_BINARY + cv.THRESH_OTSU)
+kernelSize = 130
+kernel = cv.getStructuringElement(cv.MORPH_ELLIPSE, (kernelSize, kernelSize))
+img_dilate = cv.dilate(img_binary, kernel, iterations=1)
+img_erode = cv.erode(img_binary, kernel, iterations=1)
+img_open = cv.morphologyEx(img_binary, cv.MORPH_OPEN, kernel)
+img_close = cv.morphologyEx(img_binary, cv.MORPH_CLOSE, kernel)
+plt.figure()
+plt.subplot(151)
+plt.axis("off")
+plt.imshow(img_binary, cmap="gray")
+plt.title("二值图像")
+plt.subplot(152)
+plt.axis("off")
+plt.imshow(img_dilate, cmap="gray")
+plt.title("膨胀运算结果")
+plt.subplot(153)
+plt.axis("off")
+plt.imshow(img_erode, cmap="gray")
+plt.title("腐蚀运算结果")
+plt.subplot(154)
+plt.axis("off")
+plt.imshow(img_open, cmap="gray")
+plt.title("开运算结果")
+plt.subplot(155)
+plt.axis("off")
+plt.imshow(img_close, cmap="gray")
+plt.title("闭运算结果")
+plt.tight_layout()
+plt.show()

6/4.py

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+import cv2 as cv
+import matplotlib.pyplot as plt
+
+plt.rcParams["font.sans-serif"] = ["SimSun"]
+
+img = cv.imread(r"img\mapleleaf.tif", cv.IMREAD_GRAYSCALE)
+_, img_binary = cv.threshold(img, 128, 255, cv.THRESH_BINARY + cv.THRESH_OTSU)
+plt.figure()
+plt.subplot(141)
+plt.axis("off")
+plt.imshow(img_binary, cmap="gray")
+plt.title("原图像")
+i = 1
+for kernelSize in [3, 9, 15]:
+    kernel = cv.getStructuringElement(cv.MORPH_RECT, (kernelSize, kernelSize))
+    img_result = cv.morphologyEx(img_binary, cv.MORPH_GRADIENT, kernel)
+    i += 1
+    plt.subplot(140 + i)
+    plt.axis("off")
+    plt.imshow(img_result, cmap="gray")
+    plt.title(f"{kernelSize}×{kernelSize}")
+plt.tight_layout()
+plt.show()