2020 lab

2019/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()
+## smoothing
+cv2.blur(),cv2.GaussianBlur(),cv2.medianBlur()
+## 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 psnr(target, ref):
+    target_data = np.array(target, dtype=np.float64)
+    ref_data = np.array(ref,dtype=np.float64)
+
+    diff = ref_data - target_data
+    diff = diff.flatten()
+    rmse = math.sqrt(np.mean(diff ** 2.))
+    return 20 * math.log10(255 / (rmse+1e-7))
+def noisy(noise_typ,image):
+    if noise_typ == "gauss":
+        # TODO
+        noisy = image + gauss
+        return noisy
+
+    elif noise_typ == "s&p":
+        row,col = image.shape
+        s_vs_p = 0.5
+        amount = 0.01
+        out = np.copy(image)
+        # Salt mode
+        num_salt = np.ceil(amount * image.size * s_vs_p)
+        coords = # TODO  #Use np.random.randint to generate the noise coordinate
+        out[coords] = 255
+
+        # Pepper mode
+        num_pepper = np.ceil(amount* image.size * (1. - s_vs_p))
+        coords = # TODO
+        out[coords] = 0
+        return out
+def mse(raw,recon):
+    return ((raw-recon) **2).mean()
+#-----------------Lab2-----------------------
+## Opencv
+# ColorMap
+cv2.applyColorMap()
+# Padding
+cv2.copyMakeBorder() #use cv2.BORDER_REFLECT
+# SURF
+cv2.xfeatures2d.SURF_create()
+cv2.drawKeyPoints()
+
+## Numpy scipy
+# Save & load
+np.save(),np.load()
+# concatenate
+np.concatenate()
+# mean 
+np.mean()
+# reciprocal
+np.reciprocal()
+# load .mat file 
+import scipy.io as sio 
+sio.loadmat()
+# Euclidean distance
+from scipy.spatial import distance
+distance.euclidean()
+
+## Sklearn 
+# Kmeans
+from sklearn.cluster import KMeans
+Kmeans = KMeans(args)
+Kmeans.fit_predict(X)
+
+## Matplotlib
+import matplotlib.pyplot as plt
+plt.figure()
+plt.bar(x,y)
+plt.title()
+plt.xlabel()
+plt.ylabel()
+plt.savefig()
+
+