機器學習——影象訓練

摘要： 圖片經過處理後圖片會變成黑白無色彩的影象，但可以大概觀察到圖片中主體的輪廓資訊，而還原後的圖片的主體物件會被保留，圖片中其他內容會變模糊，，主體物件得以突出，通過機器學習完成對圖片的資訊的提取，圖片資訊可以儲存到本地畫素查詢本或資料庫中 匯入類庫 1 import numpy as n...

圖片經過處理後圖片會變成黑白無色彩的影象，但可以大概觀察到圖片中主體的輪廓資訊，而還原後的圖片的主體物件會被保留，圖片中其他內容會變模糊，，主體物件得以突出，通過機器學習完成對圖片的資訊的提取，圖片資訊可以儲存到本地畫素查詢本或資料庫中

匯入類庫

1 import numpy as np
2 import cv2
3 import matplotlib.pyplot as plt
4 from sklearn.cluster import KMeans
5 from sklearn.utils import shuffle
6 from time import time
7 from skimage import io

提取和儲存影象資料

 1 n_colors = 64
 2 # 讀取圖片畫素資料
 3 tiger = cv2.imread("tiger.jpg")
 4 # print('tiger >>>>',tiger)
 5 login = cv2.imread('login.png')
 6 # 將圖片畫素資料標準化為0-1的資料並儲存至陣列中
 7 china = np.array(tiger, dtype=np.float64) / 255
 8 # print('china >>>>',china)
 9 w, h, d = original_shape = tuple(china.shape)
10 print('original_shape >>>>', original_shape)
11 print('w,h,d >>>>', w, h, d)# w：層數，h行數，d列數
12 
13 image_array = np.reshape(china, (w * h, d))
14 # 每個點作為一個樣本，維數為3，將三維陣列china化為二維陣列，列數不變，行數變為原行數乘層數
15 # print('image_array >>>>', image_array)
16 print('image_array shape>>>>', image_array.shape)

訓練影象資料

1 t0 = time()
2 # 將所有點打亂順序，取前1000個點
3 # 不使用所有點主要是為了訓練模型的速度
4 image_array_sample = shuffle(image_array, random_state=0)[:1000]
5 
6 # 訓練圖片畫素資料，將畫素資料分為64類
7 # random_state = 0用來重現同樣的結果，不設定則每次都是不同的隨機結果
8 kmeans = KMeans(n_clusters=n_colors, random_state=0).fit(image_array_sample)
9 print("done in %0.3fs." % (time() - t0))# 檢視訓練時間

預測

 1 print("Predicting color indices on the full image (k-means)")
 2 t0 = time()
 3 # 預測資料分類,image_array(921600,3)二維陣列預測完畢後的結果labels是一維陣列
 4 labels = kmeans.predict(image_array)
 5 print("done in %0.3fs." % (time() - t0))# 檢視預測時間
 6 print(labels)
 7 print(labels.shape)
 8 # 將labels從一維陣列化為二維陣列
 9 labels = labels.reshape(w, h)
10 print(labels)
11 print(labels.shape)

儲存畫素查詢本和處理後的影象

1 def save_compress_data():
2np.save('codebook_tiger.npy', kmeans.cluster_centers_)
3io.imsave('compressed_tiger.png', labels)

還原影象

 1 def recreate_image(codebook, labels, w, h):
 2# Recreate the (compressed) image from the code book & labels
 3# 每個畫素查詢碼本（對應0~63），取得其對應的畫素值
 4d = codebook.shape[1]
 5image = np.zeros((w, h, d))
 6label_idx = 0
 7for i in range(w):
 8for j in range(h):
 9image[i][j] = codebook[labels[i, j]]
10label_idx += 1
11print('還原出的影象 >>>>', image)
12return image

執行程式碼

 1 # save_compress_data()
 2 centers = np.load('codebook_tiger.npy')# 畫素查詢碼本
 3 c_image = io.imread('compressed_tiger.png')# 這張圖片裡的畫素已經過分類
 4 print('畫素查詢本 >>>>', centers)
 5 print(centers.shape)
 6 print(centers.shape[1])# 0是行數，1是列數
 7 print('壓縮的影象 >>>>', c_image)
 8 print(c_image.shape)
 9 
10 cv2.imshow("new", recreate_image(centers, c_image, w, h))
11 cv2.waitKey(0)

'''
取出壓縮後圖像的每一個數據即畫素分類id(labels[i, j])，在畫素查詢本中查詢該分類對應的三位畫素一行資料(codebook[labels[i, j]])，
賦予新的image物件(無需指定列數，三位畫素即3列)
original_shape >>>> (720, 1280, 3)
w,h,d >>>> 720 1280 3
image_array shape>>>> (921600, 3)
done in 0.583s.
Predicting color indices on the full image (k-means)
done in 0.740s.
[10 10 10 ... 60 60 60]
(921600,)
[[10 10 10 ... 428 42]
 [10 10 10 ... 42 428]
 [10 10 10 ... 42 428]
 ...
 [ 33 36 ... 60 60 60]
 [ 33 36 ... 60 60 60]
 [ 33 36 ... 60 60 60]]
(720, 1280)
畫素查詢本 >>>> [[0.26901961 0.27607843 0.35686275]
 [0.67189542 0.66887883 0.66747109]
 [0.09971989 0.09271709 0.10028011]
 [0.38901961 0.38184874 0.39383754]
 [0.83529412 0.82852050.83333333]
 [0.44028520.57468806 0.71764706]
 [0.46849673 0.49947712 0.47712418]
 [0.19622926 0.26651584 0.24494721]
 [0.30539216 0.450.60441176]
 [0.14444444 0.50359477 0.22581699]
 [0.64325609 0.63850267 0.62923351]
 [0.75148874 0.74524328 0.7405955 ]
 [0.18221289 0.18585434 0.20770308]
 [0.55294118 0.69233512 0.8631016 ]
 [0.09656863 0.28039216 0.43823529]
 [0.59155354 0.58924082 0.57797888]
 [0.53202614 0.38039216 0.7124183 ]
 [0.90756303 0.90364146 0.90140056]
 [0.37135550.46683717 0.54339301]
 [0.31215686 0.54352941 0.43372549]
 [0.23504902 0.32254902 0.30563725]
 [0.29694989 0.37342048 0.42461874]
 [0.19117647 0.32622549 0.4495098 ]
 [0.18431373 0.55757576 0.30516934]
 [0.70718954 0.76601307 0.83529412]
 [0.51328976 0.50544662 0.51568627]
 [0.36705882 0.23921569 0.61098039]
 [0.44416027 0.42983802 0.43631714]
 [0.10053476 0.16363636 0.23600713]
 [0.15022624 0.14434389 0.15806938]
 [0.40452489 0.50497738 0.59457014]
 [0.51265597 0.63030303 0.78324421]
 [0.29215686 0.30392157 0.30056022]
 [0.06876751 0.10294118 0.15644258]
 [0.04552430.05268542 0.06479113]
 [0.95294118 0.95294118 0.95803922]
 [0.34232026 0.35065359 0.33970588]
 [0.56254902 0.55431373 0.54196078]
 [0.27450980.49063181 0.27973856]
 [0.78676471 0.78149510.78112745]
 [0.21411765 0.20627451 0.43176471]
 [0.34196078 0.55843137 0.35803922]
 [0.36470588 0.50274510.66352941]
 [0.47189542 0.67973856 0.56078431]
 [0.49084967 0.55294118 0.63300654]
 [0.10889894 0.20301659 0.32488688]
 [0.65228758 0.78823529 0.92222222]
 [0.33725490.53411765 0.7427451 ]
 [0.71036415 0.70672269 0.69677871]
 [0.17019608 0.26431373 0.3696732 ]
 [0.39063181 0.44814815 0.40217865]
 [0.57303922 0.34656863 0.81617647]
 [0.28039216 0.37385621 0.49477124]
 [0.33440285 0.42816399 0.49411765]
 [0.43137255 0.61019608 0.81882353]
 [0.25743945 0.24705882 0.24313725]
 [0.27088989 0.34901961 0.36651584]
 [0.52990196 0.59215686 0.54166667]
 [0.39607843 0.55196078 0.51470588]
 [0.87511312 0.87179487 0.87722474]
 [0.41137255 0.46196078 0.46745098]
 [0.23627451 0.26470588 0.29362745]
 [0.12549020.44248366 0.18431373]
 [0.61265597 0.61978610.60463458]]
(64, 3)
3
壓縮的影象 >>>> [[10 10 10 ... 428 42]
 [10 10 10 ... 42 428]
 [10 10 10 ... 42 428]
 ...
 [ 33 36 ... 60 60 60]
 [ 33 36 ... 60 60 60]
 [ 33 36 ... 60 60 60]]
(720, 1280)
還原出的影象 >>>> [[[0.64325609 0.63850267 0.62923351]
[0.64325609 0.63850267 0.62923351]
[0.64325609 0.63850267 0.62923351]
...
[0.36470588 0.50274510.66352941]
[0.30539216 0.450.60441176]
[0.36470588 0.50274510.66352941]]

 [[0.64325609 0.63850267 0.62923351]
[0.64325609 0.63850267 0.62923351]
[0.64325609 0.63850267 0.62923351]
...
[0.36470588 0.50274510.66352941]
[0.36470588 0.50274510.66352941]
[0.30539216 0.450.60441176]]

 [[0.64325609 0.63850267 0.62923351]
[0.64325609 0.63850267 0.62923351]
[0.64325609 0.63850267 0.62923351]
...
[0.36470588 0.50274510.66352941]
[0.36470588 0.50274510.66352941]
[0.30539216 0.450.60441176]]

 ...

 [[0.38901961 0.38184874 0.39383754]
[0.38901961 0.38184874 0.39383754]
[0.34232026 0.35065359 0.33970588]
...
[0.41137255 0.46196078 0.46745098]
[0.41137255 0.46196078 0.46745098]
[0.41137255 0.46196078 0.46745098]]

 [[0.38901961 0.38184874 0.39383754]
[0.38901961 0.38184874 0.39383754]
[0.34232026 0.35065359 0.33970588]
...
[0.41137255 0.46196078 0.46745098]
[0.41137255 0.46196078 0.46745098]
[0.41137255 0.46196078 0.46745098]]

 [[0.38901961 0.38184874 0.39383754]
[0.38901961 0.38184874 0.39383754]
[0.34232026 0.35065359 0.33970588]
...
[0.41137255 0.46196078 0.46745098]
[0.41137255 0.46196078 0.46745098]
[0.41137255 0.46196078 0.46745098]]]

'''