reference files of 2020 lab

2020/Doc.py

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+import cv2
+import numpy as np 
+
+## Load image
+img = cv2.imread('Lenna.jpg') ## BGR 
+## Grayscale
+img = cv2.imread('Lenna.jpg',0)
+
+## Show image
+cv2.imshow('image',img)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
+
+## Save image
+cv2.imwrite('test.jpg',img)
+
+## Show image with matplotlib
+import matplotlib.pyplot as plt 
+plt.imshow(img[:,:,::-1]) ## To RGB
+plt.show()
+
+plt.imshow(img,cmap='gray')
+plt.show()
+
+#-----------------Lab1-----------------------
+## Resize
+cv2.resize()
+## Color space 
+cv2.cvtColor()
+## filtering
+cv2.filter2D()
+## PCA Compute
+mean,eigenvectors = cv2.PCACompute(matrix,mean=None)
+
+## min, max
+np.min(), np.max()
+## dot
+np.dot()
+## flatten
+np.flatten()
+## stack list of array
+np.stack(np_list)
+
+## KNN
+from sklearn.neighbors import KNeighborsClassifier
+# 宣告
+KNN = KNeighborsClassifier(args)
+# Feed Training Data
+KNN.fit(X_train,Y_train)
+# Predict Class
+KNN.predict(X_test)
+# 直接告訴你acc 
+KNN.score(X_test,Y_test)
+
+## Useful function
+
+def mse(raw,recon):
+    return ((raw-recon) **2).mean()
+#-----------------Lab2-----------------------
+## Numpy scipy
+# concatenate
+np.concatenate()
+# mean 
+np.mean()
+## matrix multiplication
+np.matmul()
+## create meshgrid
+np.meshgrid()
+
+
+