使用Python，可以将.tiff文件转换为.gpkg文件，以便不加区别地保留所有栅格单元吗？

Question

我想将 .tiff 文件转换为 .gpkg 文件，以便考虑每个栅格单元。

当我使用 numpy 将 .tiff 转换为图像数组时，我得到的长度返回值为 350403。

当我尝试使用 gpd.GeoDataFrame.from_features(img_src) 进行相同操作时，我得到的值为 343003。

有没有一种方法可以确保 GeoDataFrame 保留所有单元格/像素？

这是我的代码，很抱歉它不是最好的。

def tiff_to_csv（tiff_file：str，csv_file：str）：

'''

Description:

    - This function converts a TIFF file to a CSV file.

Parameters:

        tiff_file:
            - the path to the TIFF file to be converted

        csv_file:
            - the path to save the CSV file

Returns:

            None

'''



# Open the TIFF file
tif_image = Image.open(tiff_file)

# Convert my image to a numpy array
image_array = np.array(tif_image)

# Flatten the array to convert it to a 1D vector
vector_data = image_array.flatten()

print(len(vector_data))

# Save the pixel values to a CSV file
np.savetxt(csv_file, vector_data, delimiter=",")

with rasterio.open(tiff_file) as src:
    
    band1 = src.read(1)
    
    no_data = src.nodata
    print(f'no_data: {no_data}')

    # Generate shapes  (polygons)  from the raster values,
    results = (
        {'properties': {'raster_val': v}, 'geometry': shape(s)}
        for i, (s, v) in enumerate(shapes(band1, transform=src.transform))
    )

    # Convert my shapes to a GeoDataFrame
    gdf = gpd.GeoDataFrame.from_features(band1)
    total_shapes = len(gdf)
    print(f"Number of shapes (including nodata): {total_shapes}")

有趣的是，没有“no_data”值，但长度输出肯定不匹配。

Answer 1

当您使用光栅进行矢量化时，具有相同值的相邻像素会分解为一个多边形，这就是为什么您得到的多边形比光栅像素少。

您可以向栅格数组中的每个值添加一个介于 0 和 1 之间的随机浮点，以使每个像素值唯一（希望如此，除非您非常不走运），然后矢量化为 Geopandas 数据帧，并将值平铺回原始值价值观：

import rasterio
from rasterio.features import shapes
import geopandas as gpd
import numpy as np

raster_file = r"C:\Users\bera\Desktop\gistest\random_raster.tif"

#Vectorize the raster into a Geopandas dataframe
with rasterio.open(raster_file) as src:
    array = src.read(1)
    print(array.shape)
    #(26, 33). There are 858 pixels in my raster
    results = (
      {'properties': {'raster_val': v}, 'geometry': s}
      for i, (s, v) in enumerate(shapes(source=array, mask=None, transform=src.transform)))
    geoms  = list(results)
    df = gpd.GeoDataFrame.from_features(geoms)

print(df.shape)
#(679, 2)
#So 26*33 - 679 = 179 cells are missing
df.to_file(r"C:\gistest\vectorized.gpkg")

#Add a random float between 0-1 to each array vaklue
random_floats = np.random.rand(*array.shape)
array_with_random_decimals = (array + random_floats).astype("float32")

#Vectorize
results = (
      {'properties': {'raster_val': v}, 'geometry': s}
      for i, (s, v) in enumerate(shapes(source=array_with_random_decimals, mask=None, transform=src.transform)))
geoms  = list(results)
df2 = gpd.GeoDataFrame.from_features(geoms)

df2.shape[0]
#858. Which equals the number of values in the input raster.
#Floor the values back to the original values
df2["raster_val"] = np.floor(df2["raster_val"])
df2.to_file(r"C:\gistest\vectorized_with_random_addition.gpkg")