圖像像素灰度處理

• 1.計算某行相鄰像素的灰度變化率

1.計算某行相鄰像素的灰度變化率

a=img[:,100] def normalization(value): # """標準化 # 公式：(原始值-均值)/方差 # :return 范圍任意，標準化后數據均值為0，標準差為1 # """new_value = (value - value.mean()) / value.std()return new_value b=normalization(a)#畫出灰度曲線 import matplotlib.pyplot as plt values = range(0,len(a),1) gray = a plt.plot(values, gray, linewidth=2) plt.title("gray Number",fontsize=14) plt.xlabel("Value", fontsize=14) plt.ylabel("gray of Value", fontsize=14) # plt.tick_params(axis='x', labelsize=10) # plt.axis([0, 6, 0, 30]) plt.show() #計算一階微分 import matplotlib.pyplot as plt list_with_diff = [] for n in range(1, len(lista)):list_with_diff.append(lista[n] - lista[n-1]) # print("Difference between adjacent elements in the list: \n", # list_with_diff) #計算一階微分變化率 import matplotlib.pyplot as plt list_with_diff = [] for n in range(1, len(lista)):list_with_diff.append(abs((lista[n] - lista[n-1])/lista[n] )) # print("Difference between adjacent elements in the list: \n", # list_with_diff)#畫出一階微分曲線 c = list_with_diff import matplotlib.pyplot as plt values = range(0,len(c),1) gray = c plt.plot(values, gray, linewidth=2) plt.title("gray Number",fontsize=14) plt.xlabel("Value", fontsize=14) plt.ylabel("gray of Value", fontsize=14) # plt.tick_params(axis='x', labelsize=10) # plt.axis([0, 6, 0, 30]) plt.show()

計算峰值

from scipy.signal import find_peaks# x is the vector from which you want to extract the peaks c = np.array(c) peaks, _ = find_peaks(c,height=0.05) plt.plot(c) plt.plot(peaks, c[peaks], "x")

總結

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