Python 将多个图像连接为一个图像
此函数用于接收由图像裁剪部分组成的numpy数组列表。作物的大小都相同,除了最右边和最下面的图像可能较小Python 将多个图像连接为一个图像,python,python-3.x,image,numpy,Python,Python 3.x,Image,Numpy,此函数用于接收由图像裁剪部分组成的numpy数组列表。作物的大小都相同,除了最右边和最下面的图像可能较小 预测[2]将返回从原始图像裁剪的第三个子图像。每个作物都是一个numpy数组。有WxH作物,从左到右,从上到下枚举(因此,如果有4个子图像构成宽度,预测中的第5个图像将是第2行子图像左侧的第一个子图像) crops包含查找将构成重建图像的水平和垂直图像数量所需的信息裁剪[2][3]将包含从顶部开始裁剪的第三幅图像,从左侧裁剪的第四幅图像 作物中包含的图像比预测中包含的图像尺寸更小(我基本上是
预测[2]
将返回从原始图像裁剪的第三个子图像。每个作物都是一个numpy数组。有WxH作物,从左到右,从上到下枚举(因此,如果有4个子图像构成宽度,预测中的第5个图像将是第2行子图像左侧的第一个子图像)
crops
包含查找将构成重建图像的水平和垂直图像数量所需的信息<代码>裁剪[2][3]
将包含从顶部开始裁剪的第三幅图像,从左侧裁剪的第四幅图像
作物
中包含的图像比预测
中包含的图像尺寸更小(我基本上是在制作一个模型,以提高图像的分辨率)。根据预测
中的图像重建的图像,按照与作物
中的图像相同的顺序排列
def reconstruct(predictions, crops):
if len(crops) != 0:
print("use crops")
# TODO: properly extract the size of the full image
width_length = 0
height_length = 0
full_image = np.empty(shape=(height_length, width_length))
print(full_image.shape)
# TODO: properly merge the crops back into a single image
for height in range(len(predictions[0])):
for width in range(len(predictions)):
# concatenate here
print(height, width)
return full_image
我本来打算使用,但根据我在上看到的其他答案,这不是一种有效的方法(显然,numpy只会在内存中重新创建一个新变量,复制旧变量,然后添加新数据,等等)。所以现在我想知道如何正确地将我的多个图像合并成一个图像。我目前的想法是创建一个具有适当形状的python列表,并逐步用每个numpy数组的数据填充它,但即使如此,我也不确定这是否是正确的想法
下面是我试图连接到单个图像中的一组图像:
以下是预期结果:
为了帮助您了解更多可用的功能,下面是一些代码:
def predict(args):
model = load_model(save_dir + '/' + args.model)
image = skimage.io.imread(tests_path + args.image)
predictions = []
images = []
crops = seq_crop(image) # crops into multiple sub-parts the image based on 'input_' constants
for i in range(len(crops)): # amount of vertical crops
for j in range(len(crops[0])): # amount of horizontal crops
current_image = crops[i][j]
images.append(current_image)
# Hack because GPU can only handle one image at a time
input_img = (np.expand_dims(images[p], 0)) # Add the image to a batch where it's the only member
predictions.append(model.predict(input_img)[0]) # returns a list of lists, one for each image in the batch
return predictions, image, crops
# adapted from: https://stackoverflow.com/a/52463034/9768291
def seq_crop(img):
"""
To crop the whole image in a list of sub-images of the same size.
Size comes from "input_" variables in the 'constants' (Evaluation).
Padding with 0 the Bottom and Right image.
:param img: input image
:return: list of sub-images with defined size
"""
width_shape = ceildiv(img.shape[1], input_width)
height_shape = ceildiv(img.shape[0], input_height)
sub_images = [] # will contain all the cropped sub-parts of the image
for j in range(height_shape):
horizontal = []
for i in range(width_shape):
horizontal.append(crop_precise(img, i*input_width, j*input_height, input_width, input_height))
sub_images.append(horizontal)
return sub_images
def crop_precise(img, coord_x, coord_y, width_length, height_length):
"""
To crop a precise portion of an image.
When trying to crop outside of the boundaries, the input to padded with zeros.
:param img: image to crop
:param coord_x: width coordinate (top left point)
:param coord_y: height coordinate (top left point)
:param width_length: width of the cropped portion starting from coord_x
:param height_length: height of the cropped portion starting from coord_y
:return: the cropped part of the image
"""
tmp_img = img[coord_y:coord_y + height_length, coord_x:coord_x + width_length]
return float_im(tmp_img) # From [0,255] to [0.,1.]
# from https://stackoverflow.com/a/17511341/9768291
def ceildiv(a, b):
"""
To get the ceiling of a division
:param a:
:param b:
:return:
"""
return -(-a // b)
if __name__ == '__main__':
preds, original, crops = predict(args) # returns the predictions along with the original
# TODO: reconstruct image
enhanced = reconstruct(preds, crops) # reconstructs the enhanced image from predictions
编辑:
答案奏效了。以下是我使用的版本:
# adapted from https://stackoverflow.com/a/52733370/9768291
def reconstruct(predictions, crops):
# unflatten predictions
def nest(data, template):
data = iter(data)
return [[next(data) for _ in row] for row in template]
predictions = nest(predictions, crops)
H = np.cumsum([x[0].shape[0] for x in predictions])
W = np.cumsum([x.shape[1] for x in predictions[0]])
D = predictions[0][0]
recon = np.empty((H[-1], W[-1], D.shape[2]), D.dtype)
for rd, rs in zip(np.split(recon, H[:-1], 0), predictions):
for d, s in zip(np.split(rd, W[:-1], 1), rs):
d[...] = s
return recon
最方便的可能是
np.block
import numpy as np
from scipy import misc
import Image
# get example picture
data = misc.face()
# chop it up
I, J = map(np.arange, (200, 200), data.shape[:2], (200, 200))
chops = [np.split(row, J, axis=1) for row in np.split(data, I, axis=0)]
# do something with the bits
predictions = [chop-(i+j)*(chop>>3) for j, row in enumerate(chops) for i, chop in enumerate(row)]
# unflatten predictions
def nest(data, template):
data = iter(data)
return [[next(data) for _ in row] for row in template]
pred_lol = nest(predictions, chops)
# almost builtin reconstruction
def np_block_2D(chops):
return np.block([[[x] for x in row] for row in chops])
recon = np_block_2D(pred_lol)
Image.fromarray(recon).save('demo.png')
重建的操纵图像:
但我们可以通过避免中间数组来加快速度。相反,我们复制到一个预先分配的数组中:
def speed_block_2D(chops):
H = np.cumsum([x[0].shape[0] for x in chops])
W = np.cumsum([x.shape[1] for x in chops[0]])
D = chops[0][0]
recon = np.empty((H[-1], W[-1], D.shape[2]), D.dtype)
for rd, rs in zip(np.split(recon, H[:-1], 0), chops):
for d, s in zip(np.split(rd, W[:-1], 1), rs):
d[...] = s
return recon
计时,还包括每个方法的通用ND ready变体:
numpy 2D: 0.991 ms
prealloc 2D: 0.389 ms
numpy general: 1.021 ms
prealloc general: 0.448 ms
一般情况和时间的代码:
def np_block(chops):
d = 0
tl = chops
while isinstance(tl, list):
tl = tl[0]
d += 1
if d < tl.ndim:
def adjust_depth(L):
if isinstance(L, list):
return [adjust_depth(l) for l in L]
else:
ret = L
for j in range(d, tl.ndim):
ret = [ret]
return ret
chops = adjust_depth(chops)
return np.block(chops)
def speed_block(chops):
def line(src, i):
while isinstance(src, list):
src = src[0]
return src.shape[i]
def hyper(src, i):
src = iter(src)
fst = next(src)
if isinstance(fst, list):
res, dtype, szs = hyper(fst, i+1)
szs.append([res[i], *(line(s, i) for s in src)])
res[i] = sum(szs[-1])
return res, dtype, szs
res = np.array(fst.shape)
szs = [res[i], *(s.shape[i] for s in src)]
res[i] = sum(szs)
return res, fst.dtype, [szs]
shape, dtype, szs = hyper(chops, 0)
recon = np.empty(shape, dtype)
def cpchp(dst, src, i, szs=None):
szs = np.array(hyper(src, i)[2]) if szs is None else szs
dst = np.split(dst, np.cumsum(szs[-1][:-1]), i)
if isinstance(src[0], list):
szs = szs[:-1]
for ds, sr in zip(dst, src):
cpchp(ds, sr, i+1, szs)
szs = None
else:
for ds, sr in zip(dst, src):
ds[...] = sr
cpchp(recon, chops, 0, np.array(szs))
return recon
from timeit import timeit
T = (timeit(lambda: speed_block(pred_lol), number=1000),
timeit(lambda: np_block(pred_lol), number=1000),
timeit(lambda: speed_block_2D(pred_lol), number=1000),
timeit(lambda: np_block_2D(pred_lol), number=1000))
assert (np.all(speed_block(pred_lol)==np_block(pred_lol)) and
np.all(speed_block_2D(pred_lol)==np_block(pred_lol)) and
np.all(speed_block(pred_lol)==np_block_2D(pred_lol)))
print(f"""
numpy 2D: {T[3]:10.3f} ms
prealloc 2D: {T[2]:10.3f} ms
numpy general: {T[1]:10.3f} ms
prealloc general: {T[0]:10.3f} ms
""")
def np_块(印章):
d=0
tl=排骨
当isinstance(tl,列表)时:
tl=tl[0]
d+=1
如果d
漂亮的答案。但是,我确实从这一行得到了一个错误:recon=np.empty((H[-1],W[-1],D.shape[2]),D.dtype)
。错误为:索引器错误:元组索引超出范围
。更具体地说,我认为它来自D.shape[2]
。我会尝试修复它,然后再回来找你。好吧,我不能在5分钟后编辑评论。我将其更改为D.shape[1]
,因此得到3
(因为我的图像是RGB)。但是,d[…]=s
行给了我这个错误:ValueError:无法将输入数组从形状(512,3)广播到形状(512,3,3)
。我调试时遇到了问题,因为我不太懂那种符号。我刚刚意识到我在操作中犯了一个错误:我编辑了它<代码>预测是一个数组列表,其中包含从原始图像裁剪的多个子图像。。。我忘记了第一个索引,它是裁剪的\u图像
编号(因此,如果原始图像被裁剪为12个图像,那么预测的第一个索引范围从0到11)。它是原始图像的所有裁剪部分(都是numpy数组)的列表。因此,如果原始图像被裁剪成12个较小的图像,那就是12个元素的列表。@payne好的,最快的修复方法是以与修订代码中的chops/crops---functionnest
相同的方式构造预测。让我知道这是否适合你。