实现水体含量反演程序。

find_severe_glint_area.py

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+from util import *
+from osgeo import gdal
+import argparse
+
+
+@timeit
+def otsu(img, max_value, data_water_mask, ignore_value=0, foreground=1, background=0):
+    height = img.shape[0]
+    width = img.shape[1]
+
+    hist = np.zeros([max_value], np.float32)
+
+    # 计算直方图
+    invalid_counter = 0
+    for i in range(height):
+        for j in range(width):
+            if img[i, j] == ignore_value or img[i, j] < 0 or data_water_mask[i, j] == 0:
+                invalid_counter = invalid_counter + 1
+                continue
+
+            hist[img[i, j]] += 1
+
+    hist /= (height * width - invalid_counter)
+
+    threshold = 0
+    deltaMax = 0
+    # 遍历像素值，计算最大类间方差
+    for i in range(max_value):
+        wA = 0
+        wB = 0
+        uAtmp = 0
+        uBtmp = 0
+        uA = 0
+        uB = 0
+        u = 0
+        for j in range(max_value):
+            if j <= i:
+                wA += hist[j]
+                uAtmp += j * hist[j]
+            else:
+                wB += hist[j]
+                uBtmp += j * hist[j]
+        if wA == 0:
+            wA = 1e-10
+        if wB == 0:
+            wB = 1e-10
+        uA = uAtmp / wA
+        uB = uBtmp / wB
+        u = uAtmp + uBtmp
+
+        # 计算类间方差
+        deltaTmp = wA * ((uA - u)**2) + wB * ((uB - u)**2)
+        # 找出最大类间方差以及阈值
+        if deltaTmp > deltaMax:
+            deltaMax = deltaTmp
+            threshold = i
+
+    # 二值化
+    det_img = img.copy()
+    det_img[img > threshold] = foreground
+    det_img[img <= threshold] = background
+    det_img[np.where(data_water_mask == 0)] = background
+    return det_img
+
+
+def find_overexposure_area(img_path, threhold=4095):
+    # 第一步通过某个像素的光谱找到信号最强的波段
+
+    # 根据上步所得的波段号检测过曝区域
+    pass
+
+
+@timeit
+def find_severe_glint_area(img_path, water_mask, glint_wave=750, output_path=None):
+    if output_path is None:
+        output_path = append2filename(img_path, "_severe_glint_area")
+
+    glint_band_number = find_band_number(glint_wave, img_path)
+
+    dataset = gdal.Open(img_path)
+    num_bands = dataset.RasterCount
+    im_geotrans = dataset.GetGeoTransform()
+    im_proj = dataset.GetProjection()
+
+    tmp = dataset.GetRasterBand(glint_band_number + 1)  # 波段计数从1开始
+    band_flare = tmp.ReadAsArray().astype(np.int16)
+    band_flare_stretch = (band_flare / band_flare.max() * 255).astype(np.int32)
+
+    dataset_water_mask = gdal.Open(water_mask)
+    data_water_mask = dataset_water_mask.GetRasterBand(1).ReadAsArray()
+    del dataset_water_mask
+
+    # 不加1报错：IndexError: index 73 is out of bounds for axis 0 with size 73
+    flare_binary = otsu(band_flare_stretch, band_flare_stretch.max() + 1, data_water_mask)
+
+    write_bands(img_path, output_path, flare_binary)
+
+    del dataset
+
+    return output_path
+
+
+# Press the green button in the gutter to run the script.
+if __name__ == '__main__':
+    img_path = r"D:\PycharmProjects\0water_rlx\test_data\ref_mosaic_1m_bsq"
+
+    parser = argparse.ArgumentParser(description="此程序通过大律法分割图像，提取耀斑最严重的区域，输出的栅格和输入的影像具有相同的行列数。")
+
+    parser.add_argument('-i1', '--input', type=str, required=True, help='输入影像文件的路径')
+    parser.add_argument('-i2', '--input_water_mask', type=str, required=True, help='输入水域掩膜文件的路径')
+    parser.add_argument('-gw', '--glint_wave', type=float, default=750.0, help='用于提取耀斑严重区域的波段.')
+    parser.add_argument('-o', '--output', type=str, help='输出文件的路径')
+
+    parser.add_argument('-v', '--verbose', action='store_true', help='启用详细模式')
+
+    args = parser.parse_args()
+
+    find_severe_glint_area(args.input, args.input_water_mask, args.glint_wave, args.output)