python 實現圖像識別

之前PHP完成的 圖像識別

# -*- coding: utf-8 -*-  
# 利用python實現多種方法來實現圖像識別  
  
import cv2  
import numpy as np  
from matplotlib import pyplot as plt  
  
# 最簡單的以灰度直方圖作為相似比較的實現  
def classify_gray_hist(image1,image2,size = (256,256)):  
    # 先計算直方圖  
    # 幾個參數必須用方括號括起來  
    # 這裏直接用灰度圖計算直方圖，所以是使用第一個通道，  
    # 也可以進行通道分離後，得到多個通道的直方圖  
    # bins 取為16  
    image1 = cv2.resize(image1,size)  
    image2 = cv2.resize(image2,size)  
    hist1 = cv2.calcHist([image1],[0],None,[256],[0.0,255.0])  
    hist2 = cv2.calcHist([image2],[0],None,[256],[0.0,255.0])  
    # 可以比較下直方圖  
    plt.plot(range(256),hist1,'r')  
    plt.plot(range(256),hist2,'b')  
    plt.show()  
    # 計算直方圖的重合度  
    degree = 0  
    for i in range(len(hist1)):  
        if hist1[i] != hist2[i]:  
            degree = degree + (1 - abs(hist1[i]-hist2[i])/max(hist1[i],hist2[i]))  
        else:  
            degree = degree + 1  
    degree = degree/len(hist1)  
    return degree  
  
# 計算單通道的直方圖的相似值  
def calculate(image1,image2):  
    hist1 = cv2.calcHist([image1],[0],None,[256],[0.0,255.0])  
    hist2 = cv2.calcHist([image2],[0],None,[256],[0.0,255.0])  
     # 計算直方圖的重合度  
    degree = 0  
    for i in range(len(hist1)):  
        if hist1[i] != hist2[i]:  
            degree = degree + (1 - abs(hist1[i]-hist2[i])/max(hist1[i],hist2[i]))  
        else:  
            degree = degree + 1  
    degree = degree/len(hist1)  
    return degree  
  
# 通過得到每個通道的直方圖來計算相似度  
def classify_hist_with_split(image1,image2,size = (256,256)):  
    # 將圖像resize後，分離為三個通道，再計算每個通道的相似值  
    image1 = cv2.resize(image1,size)  
    image2 = cv2.resize(image2,size)  
    sub_image1 = cv2.split(image1)  
    sub_image2 = cv2.split(image2)  
    sub_data = http://ju.outofmemory.cn/entry/0  
    for im1,im2 in zip(sub_image1,sub_image2):  
        sub_data += calculate(im1,im2)  
    sub_data = sub_data/3  
    return sub_data  
  
# 平均哈希算法計算  
def classify_aHash(image1,image2):  
    image1 = cv2.resize(image1,(8,8))  
    image2 = cv2.resize(image2,(8,8))  
    gray1 = cv2.cvtColor(image1,cv2.COLOR_BGR2GRAY)  
    gray2 = cv2.cvtColor(image2,cv2.COLOR_BGR2GRAY)  
    hash1 = getHash(gray1)  
    hash2 = getHash(gray2)  
    return Hamming_distance(hash1,hash2)  
  
def classify_pHash(image1,image2):  
    image1 = cv2.resize(image1,(32,32))  
    image2 = cv2.resize(image2,(32,32))  
    gray1 = cv2.cvtColor(image1,cv2.COLOR_BGR2GRAY)  
    gray2 = cv2.cvtColor(image2,cv2.COLOR_BGR2GRAY)  
    # 將灰度圖轉為浮點型，再進行dct變換  
    dct1 = cv2.dct(np.float32(gray1))  
    dct2 = cv2.dct(np.float32(gray2))  
    # 取左上角的8*8，這些代表圖片的最低頻率  
    # 這個操作等價於c++中利用opencv實現的掩碼操作  
    # 在python中進行掩碼操作，可以直接這樣取出圖像矩陣的某一部分  
    dct1_roi = dct1[0:8,0:8]  
    dct2_roi = dct2[0:8,0:8]  
    hash1 = getHash(dct1_roi)  
    hash2 = getHash(dct2_roi)  
    return Hamming_distance(hash1,hash2)  
  
# 輸入灰度圖，返回hash  
def getHash(image):  
    avreage = np.mean(image)  
    hash = []  
    for i in range(image.shape[0]):  
        for j in range(image.shape[1]):  
            if image[i,j] > avreage:  
                hash.append(1)  
            else:  
                hash.append(0)  
    return hash  
  
  
# 計算漢明距離  
def Hamming_distance(hash1,hash2):  
    num = 0  
    for index in range(len(hash1)):  
        if hash1[index] != hash2[index]:  
            num += 1  
    return num  
  
  
if __name__ =='__main__':  
    img1 = cv2.imread('1.jpg')  
    cv2.imshow('img1',img1)  
    img2 = cv2.imread('2.jpg')  
    cv2.imshow('img2',img2)  
    degree = classify_gray_hist(img1,img2)  
    #degree = classify_hist_with_split(img1,img2)  
    #degree = classify_aHash(img1,img2)  
    #degree = classify_pHash(img1,img2)  
    print degree  
    cv2.waitKey(0)

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