pip install pillow numpy matplotlib rasterio

from PIL import Image
import matplotlib.pyplot as plt

# 打開TIFF文件
img = Image.open('example.tif')

# 顯示基本信息
print(f"格式: {img.format}")
print(f"大小: {img.size}")
print(f"模式: {img.mode}")

# 顯示圖像
plt.imshow(img)
plt.show()

import numpy as np

# 將TIFF圖像轉換為NumPy數組
img_array = np.array(img)

# 打印數組信息
print(f"數組形狀: {img_array.shape}")
print(f"數據類型: {img_array.dtype}")
print(f"最小值: {img_array.min()}, 最大值: {img_array.max()}")

# 簡單處理 - 反轉圖像
inverted_img = 255 - img_array  # 假設是8位圖像
plt.imshow(inverted_img, cmap='gray')
plt.show()

import rasterio

# 打開地理TIFF文件
with rasterio.open('geospatial.tif') as src:
    # 讀取所有波段數據
    data = src.read()
    
    # 顯示元數據
    print(f"波段數: {src.count}")
    print(f"寬度: {src.width}, 高度: {src.height}")
    print(f"坐標參考系統(tǒng)(CRS): {src.crs}")
    print(f"地理變換: {src.transform}")
    
    # 顯示第一個波段
    plt.imshow(data[0], cmap='viridis')
    plt.colorbar()
    plt.show()

with rasterio.open('multiband.tif') as src:
    # 讀取各個波段
    red = src.read(1)   # 紅波段
    green = src.read(2) # 綠波段
    blue = src.read(3)  # 藍波段
    
    # 計算NDVI (假設第4波段是近紅外)
    nir = src.read(4)
    ndvi = (nir - red) / (nir + red + 1e-10)  # 避免除以0
    
    # 顯示NDVI
    plt.imshow(ndvi, cmap='RdYlGn', vmin=-1, vmax=1)
    plt.colorbar(label='NDVI')
    plt.title('NDVI Map')
    plt.show()

from rasterio.warp import calculate_default_transform, reproject, Resampling

# 裁剪示例
with rasterio.open('large_image.tif') as src:
    # 定義裁剪窗口 (左上x, 左上y, 右下x, 右下y)
    window = rasterio.windows.Window(1000, 1000, 500, 500)
    clipped = src.read(window=window)
    
    # 顯示裁剪結果
    plt.imshow(clipped[0])
    plt.show()

# 重投影示例
dst_crs = 'EPSG:3857'  # Web墨卡托投影
with rasterio.open('input.tif') as src:
    transform, width, height = calculate_default_transform(
        src.crs, dst_crs, src.width, src.height, *src.bounds)
    kwargs = src.meta.copy()
    kwargs.update({
        'crs': dst_crs,
        'transform': transform,
        'width': width,
        'height': height
    })
    
    with rasterio.open('reprojected.tif', 'w', **kwargs) as dst:
        for i in range(1, src.count + 1):
            reproject(
                source=rasterio.band(src, i),
                destination=rasterio.band(dst, i),
                src_transform=src.transform,
                src_crs=src.crs,
                dst_transform=transform,
                dst_crs=dst_crs,
                resampling=Resampling.nearest)

# 使用rasterio的分塊讀取功能
with rasterio.open('very_large.tif') as src:
    # 獲取推薦的塊大小
    block_shapes = src.block_shapes
    print(f"塊大小: {block_shapes}")
    
    # 迭代處理每個塊
    for ji, window in src.block_windows(1):
        # 讀取當前塊
        block = src.read(window=window)
        
        # 處理塊數據 (示例: 計算平均值)
        block_mean = np.mean(block)
        print(f"塊 {ji} 的平均值: {block_mean}")
        
        # 這里可以添加你的處理邏輯
        # processed_block = some_processing(block)
        
        # 寫入輸出文件 (如果需要)
        # dst.write(processed_block, window=window)

import dask.array as da
import rasterio
from rasterio.windows import Window

# 創(chuàng)建延遲加載的dask數組
with rasterio.open('huge.tif') as src:
    # 定義塊大小 (例如 1024x1024)
    chunk_size = (1, 1024, 1024)  # (波段, 高, 寬)
    
    # 創(chuàng)建dask數組
    dask_array = da.from_array(src, chunks=chunk_size)
    
    # 現在可以對dask數組執(zhí)行各種操作 (延遲執(zhí)行)
    mean_computation = dask_array.mean()
    
    # 實際計算結果 (觸發(fā)計算)
    print(f"整個圖像的平均值: {mean_computation.compute()}")

from concurrent.futures import ThreadPoolExecutor
import glob

def process_tif(file_path):
    """處理單個TIFF文件的函數"""
    with rasterio.open(file_path) as src:
        data = src.read(1)
        # 這里添加你的處理邏輯
        result = np.mean(data)
        return file_path, result

# 獲取所有TIFF文件
tif_files = glob.glob('data/*.tif')

# 使用線程池并行處理
with ThreadPoolExecutor(max_workers=4) as executor:
    results = list(executor.map(process_tif, tif_files))

# 打印結果
for file_path, mean_val in results:
    print(f"{file_path}: {mean_val}")