import numpy as np
from PIL import Image
# 判斷是否需要進行圖像填充
def judge(img, wi, he):
    width, height = img.size
    # 默認新圖像尺寸初始化為原圖像
    new_width, new_height = img.size
    if width % wi != 0:
        new_width = (width//wi + 1) * wi
    if height % he != 0:
        new_height = (height//he + 1) * he
    # 新建一張新尺寸的全黑圖像
    new_image = Image.new('RGB', (new_width, new_height), (0, 0, 0))
    # 將原圖像粘貼在new_image上，默認為左上角坐標對應(yīng)
    new_image.paste(img, box=None, mask=None)
    new_image.show()
    return new_image
# 按照指定尺寸進行圖片裁剪
def crop_image(image, patch_w, patch_h):
    width, height = image.size
    # 補丁計數(shù)
    cnt = 0
    for w in range(0, width, patch_w):
        for h in range(0, height, patch_h):
            cnt += 1
            # 指定原圖片的左、上、右、下
            img = image.crop((w, h, w+patch_w, h+patch_h))
            img.save("dog-%d.jpg" % cnt)
    print("圖片補丁裁剪結(jié)束，共有{}張補丁".format(cnt))
def main():
    image_path = "dog.jpg"
    img = Image.open(image_path)
    # 查看圖像形狀
    print("原始圖像形狀{}".format(np.array(img).shape))
    # 輸入指定的補丁寬高
    print("輸入補丁寬高：")
    wi, he = map(int, input().split(" "))
    # 進行圖像填充
    new_image = judge(img, wi, he)
    # 圖片補丁裁剪
    crop_image(new_image, wi, he)
if __name__ == '__main__':
    main()

def thresholdSegment(filename):
? ? gray = cv2.imread(filename, cv2.IMREAD_GRAYSCALE)
? ? ret1, th1 = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
? ? th2 = cv2.adaptiveThreshold(
? ? ? ? gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 11, 2)
? ? th3 = cv2.adaptiveThreshold(
? ? ? ? gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)
? ? ret2, th4 = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
? ? images = [th1, th2, th4, th3]
? ? imgaesTitle = ['THRESH_BINARY', 'THRESH_MEAN',
? ? ? ? ? ? ? ? ? ?'THRESH_OTSU', 'THRESH_GAUSSIAN']
? ? plt.figure()
? ? for i in range(4):
? ? ? ? plt.subplot(2, 2, i+1)
? ? ? ? plt.imshow(images[i], 'gray')
? ? ? ? plt.title(imgaesTitle[i])
? ? ? ? cv2.imwrite(imgaesTitle[i]+'.jpg', images[i])
? ? plt.show()
? ? cv2.waitKey(0)
? ? return images

def edgeSegmentation(filename):
? ? # 讀取圖片
? ? img = cv2.imread(filename)
? ? # 灰度化
? ? gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
? ? # 高斯模糊處理:去噪(效果最好)
? ? blur = cv2.GaussianBlur(gray, (9, 9), 0)
? ? # Sobel計算XY方向梯度
? ? gradX = cv2.Sobel(gray, ddepth=cv2.CV_32F, dx=1, dy=0)
? ? gradY = cv2.Sobel(gray, ddepth=cv2.CV_32F, dx=0, dy=1)
? ? # 計算梯度差
? ? gradient = cv2.subtract(gradX, gradY)
? ? # 絕對值
? ? gradient = cv2.convertScaleAbs(gradient)
? ? # 高斯模糊處理:去噪(效果最好)
? ? blured = cv2.GaussianBlur(gradient, (9, 9), 0)
? ? # 二值化
? ? _, dst = cv2.threshold(blured, 90, 255, cv2.THRESH_BINARY)
? ? # 滑動窗口
? ? kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (107, 76))
? ? # 形態(tài)學(xué)處理:形態(tài)閉處理(腐蝕)
? ? closed = cv2.morphologyEx(dst, cv2.MORPH_CLOSE, kernel)
? ? # 腐蝕與膨脹迭代
? ? closed = cv2.erode(closed, None, iterations=4)
? ? closed = cv2.dilate(closed, None, iterations=4)
? ? # 獲取輪廓
? ? _, cnts, _ = cv2.findContours(
? ? ? ? closed.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
? ? c = sorted(cnts, key=cv2.contourArea, reverse=True)[0]
? ? rect = cv2.minAreaRect(c)
? ? box = np.int0(cv2.boxPoints(rect))
? ? draw_img = cv2.drawContours(img.copy(), [box], -1, (0, 0, 255), 3)
? ? #cv2.imshow("Box", draw_img)
? ? #cv2.imwrite('./test/monkey.png', draw_img)
? ? images = [blured, dst, closed, draw_img]
? ? imgaesTitle = ['blured', 'dst', 'closed', 'draw_img']
? ? plt.figure()
? ? for i in range(4):
? ? ? ? plt.subplot(2, 2, i+1)
? ? ? ? plt.imshow(images[i], 'gray')
? ? ? ? plt.title(imgaesTitle[i])
? ? ? ? #cv2.imwrite(imgaesTitle[i]+'.jpg', images[i])
? ? plt.show()
? ? cv2.waitKey(0)

def regionSegmentation(filename):
? ? # 讀取圖片
? ? img = cv2.imread(filename)
? ? # 圖片寬度
? ? img_x = img.shape[1]
? ? # 圖片高度
? ? img_y = img.shape[0]
? ? # 分割的矩形區(qū)域
? ? rect = (0, 0, img_x-1, img_y-1)
? ? # 背景模式,必須為1行,13x5列
? ? bgModel = np.zeros((1, 65), np.float64)
? ? # 前景模式,必須為1行,13x5列
? ? fgModel = np.zeros((1, 65), np.float64)
? ? # 圖像掩模,取值有0,1,2,3
? ? mask = np.zeros(img.shape[:2], np.uint8)
? ? # grabCut處理,GC_INIT_WITH_RECT模式
? ? cv2.grabCut(img, mask, rect, bgModel, fgModel, 4, cv2.GC_INIT_WITH_RECT)
? ? # grabCut處理,GC_INIT_WITH_MASK模式
? ? #cv2.grabCut(img, mask, rect, bgModel, fgModel, 4, cv2.GC_INIT_WITH_MASK)
? ? # 將背景0,2設(shè)成0,其余設(shè)成1
? ? mask2 = np.where((mask == 2) | (mask == 0), 0, 1).astype('uint8')
? ? # 重新計算圖像著色,對應(yīng)元素相乘
? ? img = img*mask2[:, :, np.newaxis]
? ? cv2.imshow("Result", img)
? ? cv2.waitKey(0)

def svmSegment(pic):
? ? img = Image.open(pic)
? ? img.show() ?# 顯示原始圖像
? ? img_arr = np.asarray(img, np.float64)
? #選取圖像上的關(guān)鍵點RGB值(10個)
? ? lake_RGB = np.array(
? ? [[147, 168, 125], [151, 173, 124], [143, 159, 112], [150, 168, 126], [146, 165, 120],
? ? ?[145, 161, 116], [150, 171, 130], [146, 112, 137], [149, 169, 120], [144, 160, 111]])
# 選取待分割目標上的關(guān)鍵點RGB值(10個)
? ? duck_RGB = np.array(
? ? [[81, 76, 82], [212, 202, 193], [177, 159, 157], [129, 112, 105], [167, 147, 136],
? ? ?[237, 207, 145], [226, 207, 192], [95, 81, 68], [198, 216, 218], [197, 180, 128]] )
? ? RGB_arr = np.concatenate((lake_RGB, duck_RGB), axis=0) ?# 按列拼接
? ? # lake 用 0標記，duck用1標記
? ? label = np.append(np.zeros(lake_RGB.shape[0]), np.ones(duck_RGB.shape[0]))
? ? # 原本 img_arr 形狀為(m,n,k),現(xiàn)在轉(zhuǎn)化為(m*n,k)
? ? img_reshape = img_arr.reshape(
? ? [img_arr.shape[0]*img_arr.shape[1], img_arr.shape[2]])
? ? svc = SVC(kernel='poly', degree=3) ?# 使用多項式核，次數(shù)為3
? ? svc.fit(RGB_arr, label) ?# SVM 訓(xùn)練樣本
? ? predict = svc.predict(img_reshape) ?# 預(yù)測測試點
? ? lake_bool = predict == 0. ?
? ? lake_bool = lake_bool[:, np.newaxis] ?# 增加一列(一維變二維)
? ? lake_bool_3col = np.concatenate(
? ? (lake_bool, lake_bool, lake_bool), axis=1) ?# 變?yōu)槿?
? ? lake_bool_3d = lake_bool_3col.reshape(
? ? (img_arr.shape[0], img_arr.shape[1], img_arr.shape[2])) ?# 變回三維數(shù)組(邏輯數(shù)組)
? ? img_arr[lake_bool_3d] = 255. ?
? ? img_split = Image.fromarray(img_arr.astype('uint8')) ?# 數(shù)組轉(zhuǎn)image
? ? img_split.show() ?# 顯示分割之后的圖像
? ? img_split.save('split_duck.jpg') ?# 保存

def watershedSegment(filename):
? ? img = cv2.imread(filename)
? ? gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
? ? ret, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
? ? # noise removal
? ? kernel = np.ones((3,3),np.uint8)
? ? opening = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel, iterations = 2)
? ? # sure background area
? ? sure_bg = cv2.dilate(opening,kernel,iterations=3)
? ? # Finding sure foreground area
? ? dist_transform = cv2.distanceTransform(opening,cv2.DIST_L2,5)
? ? ret, sure_fg = cv2.threshold(dist_transform,0.7*dist_transform.max(),255,0)
? ? # Finding unknown region
? ? sure_fg = np.uint8(sure_fg)
? ? unknown = cv2.subtract(sure_bg,sure_fg)?
? ? # Marker labelling
? ? ret, markers = cv2.connectedComponents(sure_fg)
? ? # Add one to all labels so that sure background is not 0, but 1
? ? markers = markers+1
? ? # Now, mark the region of unknown with zero
? ? markers[unknown==255]=0?
? ? markers = cv2.watershed(img,markers)
? ? img[markers == -1] = [255,0,0]

def kmeansSegment(filename,k):
? ? f = open(filename,'rb') #二進制打開
? ? data = []
? ? img = Image.open(f) #以列表形式返回圖片像素值
? ? m,n = img.size #圖片大小
? ? for i in range(m):
? ? ? ? for j in range(n): ?#將每個像素點RGB顏色處理到0-1范圍內(nèi)并存放data
? ? ? ? ? ? x,y,z = img.getpixel((i,j))
? ? ? ? ? ? data.append([x/256.0,y/256.0,z/256.0])
? ? f.close()
? ? img_data=np.mat(data)
? ? row=m
? ? col=n
? ? label = KMeans(n_clusters=k).fit_predict(img_data) ?#聚類中心的個數(shù)為3
? ? label = label.reshape([row,col]) ? ?#聚類獲得每個像素所屬的類別
? ? pic_new = Image.new("L",(row,col)) ?#創(chuàng)建一張新的灰度圖保存聚類后的結(jié)果
? ? for i in range(row): ? ?#根據(jù)所屬類別向圖片中添加灰度值
? ? ? ? for j in range(col):
? ? ? ? ? ? pic_new.putpixel((i,j),int(256/(label[i][j]+1)))
? ? pic_new.save('keans_'+str(k)+'.jpg')
? ? plt.imshow(pic_new)
? ? plt.show()