from PIL import Image
def produceImage(file_in, width, height, file_out):
    image = Image.open(file_in)
    resized_image = image.resize((height, width), Image.ANTIALIAS)
    resized_image.save(file_out)
if __name__ == '__main__':
    file_in = 'right2.png'#輸入文件的文件名
    width = 500#文件大小
    height = 500
    file_out = 'right11.png'#生成文件的文件名
    produceImage(file_in, width, height, file_out)

import cv2
import numpy as np
class MyStitcher:
    # 拼接函數
    def stitch(self, images, ratio=0.75, reprojThresh=4.0, showMatches=False):
        # 獲取輸入圖片
        (imageB, imageA) = images
        # 檢測A、B圖片的SIFT關鍵特征點，并計算特征描述子
        (kpsA, featuresA) = self.detectAndDescribe(imageA)
        (kpsB, featuresB) = self.detectAndDescribe(imageB)
        # 匹配兩張圖片的所有特征點，返回匹配結果
        M = self.matchKeypoints(kpsA, kpsB, featuresA, featuresB, ratio, reprojThresh)
        # 如果返回結果為空，沒有匹配成功的特征點，退出算法
        if M is None:
            return None
        # 否則，提取匹配結果
        # H是3x3視角變換矩陣
        (matches, H, status) = M
        # 將圖片A進行視角變換，result是變換后圖片
        result = cv2.warpPerspective(imageA, H, (imageA.shape[1] + imageB.shape[1], imageA.shape[0]))
        # 將圖片B傳入result圖片最左端
        result[0:imageB.shape[0], 0:imageB.shape[1]] = imageB
        # 檢測是否需要顯示圖片匹配
        if showMatches:
            # 生成匹配圖片
            vis = self.drawMatches(imageA, imageB, kpsA, kpsB, matches, status)
            # 返回結果
            return (result, vis)
        # 返回匹配結果
        return result
    def detectAndDescribe(self, image):
        # 將彩色圖片轉換成灰度圖
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        # 建立SIFT生成器
        descriptor = cv2.xfeatures2d.SIFT_create()
        # 檢測SIFT特征點，并計算描述子
        (kps, features) = descriptor.detectAndCompute(image, None)
        # 將結果轉換成NumPy數組
        kps = np.float32([kp.pt for kp in kps])
        # 返回特征點集，及對應的描述特征
        return (kps, features)
    def matchKeypoints(self, kpsA, kpsB, featuresA, featuresB, ratio, reprojThresh):
        # 建立暴力匹配器
        matcher = cv2.DescriptorMatcher_create("BruteForce")
        # 使用KNN檢測來自A、B圖的SIFT特征匹配對，K=2
        rawMatches = matcher.knnMatch(featuresA, featuresB, 2)
        matches = []
        for m in rawMatches:
            # 當最近距離跟次近距離的比值小于ratio值時，保留此匹配對
            if len(m) == 2 and m[0].distance < m[1].distance * ratio:
                # 存儲兩個點在featuresA, featuresB中的索引值
                matches.append((m[0].trainIdx, m[0].queryIdx))
        # 當篩選后的匹配對大于4時，計算視角變換矩陣
        if len(matches) > 4:
            # 獲取匹配對的點坐標
            ptsA = np.float32([kpsA[i] for (_, i) in matches])
            ptsB = np.float32([kpsB[i] for (i, _) in matches])
            # 計算視角變換矩陣
            (H, status) = cv2.findHomography(ptsA, ptsB, cv2.RANSAC, reprojThresh)
            # 返回結果
            return (matches, H, status)
        # 如果匹配對小于4時，返回None
        return None
    def drawMatches(self, imageA, imageB, kpsA, kpsB, matches, status):
        # 初始化可視化圖片，將A、B圖左右連接到一起
        (hA, wA) = imageA.shape[:2]
        (hB, wB) = imageB.shape[:2]
        vis = np.zeros((max(hA, hB), wA + wB, 3), dtype="uint8")
        vis[0:hA, 0:wA] = imageA
        vis[0:hB, wA:] = imageB
        # 聯合遍歷，畫出匹配對
        for ((trainIdx, queryIdx), s) in zip(matches, status):
            # 當點對匹配成功時，畫到可視化圖上
            if s == 1:
                # 畫出匹配對
                ptA = (int(kpsA[queryIdx][0]), int(kpsA[queryIdx][1]))
                ptB = (int(kpsB[trainIdx][0]) + wA, int(kpsB[trainIdx][1]))
                cv2.line(vis, ptA, ptB, (0, 255, 0), 1)
        # 返回可視化結果
        return vis
# 讀取拼接圖片
imageA = cv2.imread("left11.png")
imageB = cv2.imread("right11.png")
# 把圖片拼接成全景圖
mystitcher = MyStitcher()
(result, vis) = mystitcher.stitch([imageA, imageB], showMatches=True)
# 顯示所有圖片
cv2.imshow("Image A", imageA)
cv2.imshow("Image B", imageB)
cv2.imshow("Keypoint Matches", vis)
cv2.imshow("Result", result)
cv2.waitKey(0)
cv2.destroyAllWindows()