使用python转换和裁剪平铺中的图像

我尝试用python平铺JPG图像。我通常使用imageMagick。。所以我看到比似乎做这项工作

但我不会翻译

 convert -crop 256x256 +repage big_image.jpg tiles_%d.jpg

---

有人能帮我吗？

Python的魔杖库提供了一个独特的使用

wand.image.image[左：右，上：下]

可以对新的虚拟像素图像进行子切片

从wand.image导入图像
使用图像（filename=“big_Image.jpg”）作为img：
i=0
对于范围内的h（0，img.height，256）：
对于范围内的w（0，img.width，256）：
w_end=w+256
h_端=h+256
将img[w:w_end，h:h_end]作为块：
chunk.save（filename='tiles_{0}.jpg'.格式（i））
i+=1

上面将从以下位置生成许多与

+repage

选项匹配的平铺图像：

convert -crop 256x256 +repage big_image.jpg tiles_%d.jpg

---

Python的魔杖库提供了一个独特的使用

wand.image.image[左：右，上：下]

可以对新的虚拟像素图像进行子切片

从wand.image导入图像
使用图像（filename=“big_Image.jpg”）作为img：
i=0
对于范围内的h（0，img.height，256）：
对于范围内的w（0，img.width，256）：
w_end=w+256
h_端=h+256
将img[w:w_end，h:h_end]作为块：
chunk.save（filename='tiles_{0}.jpg'.格式（i））
i+=1

上面将从以下位置生成许多与

+repage

选项匹配的平铺图像：

convert -crop 256x256 +repage big_image.jpg tiles_%d.jpg

---

用于构建要素金字塔样式的网络

def image_to_square_tiles(img, SQUARE_SIZE = 256, plot=False, save=False):
"""
Function that splits multi channel channel images into overlapping square tiles (divisible by 128)
:param img: image: multi channel image (NxMxC matrix)
:param number_of_tiles: squared number
:param plot: whether to plot an aimage
:return tiles: named tuple of tiled images (.img) and coordinates (.coords)

------------------------
Examples usage:
_ = image_to_square_tiles(img, SQUARE_SIZE = 512, plot=True)
--------------------------
"""
def get_overlap(l, SQUARE_SIZE):
    N_squares =  np.ceil(l/SQUARE_SIZE)
    pixel_padding = np.remainder(l,SQUARE_SIZE)
    if pixel_padding!=0:
        overlap = int((SQUARE_SIZE-pixel_padding)//(N_squares-1))
    else:
        overlap = 0
    return overlap
def get_tuples(l, overlap, SQUARE_SIZE):
    r = np.arange(0, l-overlap, (SQUARE_SIZE-overlap))
    tuples = [(i, i+SQUARE_SIZE) for i in r]
    return tuples

[w, h] = img.shape[:2]
assert SQUARE_SIZE%128==0, "has to be a multiple of 128 . i.e. [128,256,384,512,640,768,896,1024]"

w_overlap = get_overlap(w, SQUARE_SIZE)
w_tuples = get_tuples(w, w_overlap, SQUARE_SIZE)
h_overlap = get_overlap(h, SQUARE_SIZE)
h_tuples = get_tuples(h, h_overlap, SQUARE_SIZE)

tile_record = namedtuple("info", "img coords")
tiles = []
for row in range(len(w_tuples)):
    for column in range(len(h_tuples)):
        #print(row,column)
        x1, x2, y1, y2 = *w_tuples[row], *h_tuples[column] 
        record = tile_record(img[x1:x2, y1:y2], (x1, y1, x2, y2))
        tiles.append(record)

if plot:
    c = 0
    fig, axes = plt.subplots(len(w_tuples), len(h_tuples), figsize=(15,8))
    for row in range(len(w_tuples)):
        for column in range(len(h_tuples)):
            axes[row, column].imshow(tiles[c].img)
            #axes[row,column].set_title("ave: {:.3f}".format(np.average(tiles[c].img)))
            axes[row,column].axis('off')
            c+=1
    if save:
        plt.savefig("{}.png".format(SQUARE_SIZE), bbox_inches = "tight")
print("h overlap: {}\t w overlap: {}".format(w_overlap, h_overlap))
return tiles

---

用于构建要素金字塔样式的网络

def image_to_square_tiles(img, SQUARE_SIZE = 256, plot=False, save=False):
"""
Function that splits multi channel channel images into overlapping square tiles (divisible by 128)
:param img: image: multi channel image (NxMxC matrix)
:param number_of_tiles: squared number
:param plot: whether to plot an aimage
:return tiles: named tuple of tiled images (.img) and coordinates (.coords)

------------------------
Examples usage:
_ = image_to_square_tiles(img, SQUARE_SIZE = 512, plot=True)
--------------------------
"""
def get_overlap(l, SQUARE_SIZE):
    N_squares =  np.ceil(l/SQUARE_SIZE)
    pixel_padding = np.remainder(l,SQUARE_SIZE)
    if pixel_padding!=0:
        overlap = int((SQUARE_SIZE-pixel_padding)//(N_squares-1))
    else:
        overlap = 0
    return overlap
def get_tuples(l, overlap, SQUARE_SIZE):
    r = np.arange(0, l-overlap, (SQUARE_SIZE-overlap))
    tuples = [(i, i+SQUARE_SIZE) for i in r]
    return tuples

[w, h] = img.shape[:2]
assert SQUARE_SIZE%128==0, "has to be a multiple of 128 . i.e. [128,256,384,512,640,768,896,1024]"

w_overlap = get_overlap(w, SQUARE_SIZE)
w_tuples = get_tuples(w, w_overlap, SQUARE_SIZE)
h_overlap = get_overlap(h, SQUARE_SIZE)
h_tuples = get_tuples(h, h_overlap, SQUARE_SIZE)

tile_record = namedtuple("info", "img coords")
tiles = []
for row in range(len(w_tuples)):
    for column in range(len(h_tuples)):
        #print(row,column)
        x1, x2, y1, y2 = *w_tuples[row], *h_tuples[column] 
        record = tile_record(img[x1:x2, y1:y2], (x1, y1, x2, y2))
        tiles.append(record)

if plot:
    c = 0
    fig, axes = plt.subplots(len(w_tuples), len(h_tuples), figsize=(15,8))
    for row in range(len(w_tuples)):
        for column in range(len(h_tuples)):
            axes[row, column].imshow(tiles[c].img)
            #axes[row,column].set_title("ave: {:.3f}".format(np.average(tiles[c].img)))
            axes[row,column].axis('off')
            c+=1
    if save:
        plt.savefig("{}.png".format(SQUARE_SIZE), bbox_inches = "tight")
print("h overlap: {}\t w overlap: {}".format(w_overlap, h_overlap))
return tiles

---

如果要基于rowsXcols平铺图像：可以使用以下方法：

def TileImage(image,rows,cols):
imagename = image
im = Image.open(imagename) 
width, height = im.size
indexrow = 0
indexcolum = 0
left = 0
top = 0
right = width/col
buttom = 0
while(right<=width):    

    buttom = height/rows
    top = 0
    indexrow=0  

    while(top<height):
        print(f"h : {height}, w : {width}, left : {left},top : {top},right : {right}, buttom   :  {buttom}")
        cropimg= im.crop((left, top, right, buttom)) 
        cropimg.save(imagename + str(indexrow) + str(indexcolum) +".jpg")
        top = buttom
        indexrow += 1
        buttom += height/rows   

    indexcolum+=1
    left = right
    right += width/col  

---

如果要基于rowsXcols平铺图像：可以使用以下方法：

def TileImage(image,rows,cols):
imagename = image
im = Image.open(imagename) 
width, height = im.size
indexrow = 0
indexcolum = 0
left = 0
top = 0
right = width/col
buttom = 0
while(right<=width):    

    buttom = height/rows
    top = 0
    indexrow=0  

    while(top<height):
        print(f"h : {height}, w : {width}, left : {left},top : {top},right : {right}, buttom   :  {buttom}")
        cropimg= im.crop((left, top, right, buttom)) 
        cropimg.save(imagename + str(indexrow) + str(indexcolum) +".jpg")
        top = buttom
        indexrow += 1
        buttom += height/rows   

    indexcolum+=1
    left = right
    right += width/col  

---

谢谢@emcconville。。。如果我想在磁片名称中添加line和clone位置？类似于

chunk.save（filename=myfilename+'tiles.+chr（h）+'.+chr（w）+'.jpg.format（i））

我想是吧？但不像，我有个错误…是的。只需要重新查看一些字符串格式。比如：

“{}tiles{}{{}.jpg.”格式（myfilename，h，w）

，但这取决于您是否感谢@emcconville。。。如果我想在磁片名称中添加line和clone位置？类似于

chunk.save（filename=myfilename+'tiles.+chr（h）+'.+chr（w）+'.jpg.format（i））

我想是吧？但不像，我有个错误…是的。只需要重新查看一些字符串格式。比如：

“{}{}{}{}.jpg.format（myfilename，h，w）

，但这取决于您