通过覆盖多边形提取像素值

Question

我正在尝试通过覆盖多边形来提取像素值。我使用了Patrick Gray（http://patrickgray.me/open-geo-tutorial/chapter_5_classification.html）中的代码。当我用形状特征遮盖图像时，我想得到out_image。然后，下一步将是删除0，这将完全弄乱数组，因为根据频段不存在值。我尝试了多种方法来删除0并根据类保持band值的顺序。在R中，我可以毫无问题地做到这一点，当我将数据导出为CSV并训练算法时，在Python环境中一切都可以正常工作。

如何提取像素值并保持数字级别和类级别？

 X = np.array([], dtype=np.int8).reshape(0,8) # pixels for training
 y = np.array([], dtype=np.string_) # labels for training

with rasterio.open(img_fp) as src:
    band_count = src.count
    for index, geom in enumerate(geoms):
        feature = [mapping(geom)]

# the mask function returns an array of the raster pixels within this feature
out_image, out_transform = mask(src, feature, crop=True) 
# eliminate all the pixels with 0 values for all 8 bands - AKA not actually part of the shapefile
out_image_trimmed = out_image[:,~np.all(out_image == 0, axis=0)]
# eliminate all the pixels with 255 values for all 8 bands - AKA not actually part of the shapefile
out_image_trimmed = out_image_trimmed[:,~np.all(out_image_trimmed == 255, axis=0)]
# reshape the array to [pixel count, bands]
out_image_reshaped = out_image_trimmed.reshape(-1, band_count)
# append the labels to the y array
y = np.append(y,[shapefile["Classname"][index]] * out_image_reshaped.shape[0]) 
# stack the pizels onto the pixel array
X = np.vstack((X,out_image_reshaped))        

非常感谢您的提示！

Answer 1

这里是解决方案。我不得不明智地分割数据带，然后将其转置并按列堆叠。完成此步骤后，np.vstack正常运行，一切都按顺序进行。

 X = np.array([], dtype=np.int8).reshape(0,9) # pixels for training
 y = np.array([], dtype=np.int8) # labels for training

 # extract the raster values within the polygon 
with rio.open(sentinal_band_paths[7]) as src:
    band_count = src.count
    for index, geom in enumerate(geoms):
    feature = [mapping(geom)]

    # the mask function returns an array of the raster pixels within this feature
    out_image, out_transform = mask(src, feature, crop=True) 
    # eliminate all the pixels with 0 values for all 8 bands - AKA not actually part of the shapefile
    out_image_trimmed = out_image[:,~np.all(out_image == 0, axis=0)]
    # eliminate all the pixels with 255 values for all 8 bands - AKA not actually part of the shapefile
    out_image_trimmed = out_image_trimmed[:,~np.all(out_image_trimmed == 255, axis=0)]
    # reshape the array to [pixel count, bands]
    out_image_reshaped = out_image_trimmed.reshape(-1, band_count)
    # reshape the array to [pixel count, bands]
    trial = np.split(out_image_trimmed, 9)##### share it to equally after bands
    B1 = trial[0].T ####transpons columns
    B2 = trial [1].T
    B3 = trial [2].T
    B4 = trial [3].T
    B5 = trial [4].T
    B6= trial [5].T
    B7 = trial [6].T
    B8 = trial [7].T      
    B9 = trial[8].T
    colum_data = np.column_stack((B1,B2,B3,B4,B5,B6,B7,B8,B9))####concatenate data colum wise 
    # append the labels to the y array
    y = np.append(y,[shapefile["id"][index]] *  out_image_reshaped.shape[0]) 
    # stack the pizels onto the pixel array
    X = np.vstack((X,colum_data ))       

Answer 2

消除所有8个波段的0值的所有像素-AKA实际上不是shapefile的一部分：

out_image_trimmed = out_image[:,~np.all(out_image == 0, axis=0)]

消除所有8个波段的255个值的所有像素-AKA实际上不是shapefile的一部分：

out_image_trimmed = out_image_trimmed[:,~np.all(out_image_trimmed == 255, axis=0)]