通过2种比值法的差值识别云

cloudage.py

@@ -3,7 +3,8 @@ import cv2
 import numpy as np
 
 
-def f1(image_path):
+# r / b
+def ratio_method1(image_path):
     # Load an image
     img = cv2.imread(image_path)
     # img = cv2.imread(image_path, 0)  # Load an image in grayscale
@@ -21,11 +22,14 @@ def f1(image_path):
     cv2.imwrite(out, cloud_BINARY)
 
     # cv2.imshow('image', cloud_BINARY)
-    cv2.waitKey(0)
+    # cv2.waitKey(0)
-    cv2.destroyAllWindows()
+    # cv2.destroyAllWindows()
+
+    return cloud_BINARY
 
 
-def f2(image_path):
+# (b-r) / (b+r)
+def ratio_method2(image_path):
     img = cv2.imread(image_path)
 
     b, g, r = cv2.split(img)
@@ -41,11 +45,34 @@ def f2(image_path):
     cv2.imwrite(out, cloud_BINARY)
 
     # cv2.imshow('image', cloud_BINARY)
-    cv2.waitKey(0)
+    # cv2.waitKey(0)
-    cv2.destroyAllWindows()
+    # cv2.destroyAllWindows()
+
+    return cloud_BINARY
 
 
-def f3(image_path):
+# r / b - (b-r) / (b+r)
+def ratio_method1_subtract_ratio_method2(image_path):
+    result1 = ratio_method1(image_path)
+    result2 = ratio_method2(image_path)
+
+    light_cloud_BINARY = np.zeros_like(result1)
+
+    for x in range(result1.shape[0]):
+        for y in range(result1.shape[1]):
+            if (result1[x, y] != 0 and result2[x, y] == 0):
+                light_cloud_BINARY[x, y] = 255
+
+    tmp = image_path.split('.')
+    out = tmp[0] + "_subtract." + tmp[1]
+    cv2.imwrite(out, light_cloud_BINARY)
+
+    # cv2.imshow('image', light_cloud_BINARY)
+    # cv2.waitKey(0)
+    # cv2.destroyAllWindows()
+
+
+def saturability_method(image_path):
     img = cv2.imread(image_path)
     b, g, r = cv2.split(img)
 
@@ -70,15 +97,28 @@ def f3(image_path):
     hls = cv2.cvtColor(img, cv2.COLOR_BGR2HLS)  # 这就是ihs颜色空间：
     h, l, s = cv2.split(hls)
 
-    μ_Clouds = 12.7
+    # # 文献中的阈值
-    σ_Clouds = 3.7
+    # μ_Clouds = 12.7
+    # σ_Clouds = 3.7
+    #
+    # μ_sky = 45.3
+    # σ_sky = 4.4
 
-    μ_sky = 45.3
+    # # np.where用法：https://www.zhihu.com/question/62844162
-    σ_sky = 4.4
+    # clouds_loc = np.where(s < μ_Clouds + 3 * σ_Clouds)  # 厚云
+    # sky_loc = np.where(s > μ_sky - 3 * σ_sky)  # 天空
+    # unknow_loc = np.where((μ_Clouds + 3 * σ_Clouds <= s) & (s <= μ_sky - 3 * σ_sky))  # 薄云
+
+    # 自己通过样本测得的阈值
+    μ_Clouds = 170.9
+    σ_Clouds = 2.7
+
+    μ_sky = 111.25
+    σ_sky = 3.13
 
     # np.where用法：https://www.zhihu.com/question/62844162
-    clouds_loc = np.where(s < μ_Clouds + 3 * σ_Clouds)  # 厚云
+    clouds_loc = np.where(s > μ_Clouds - 3 * σ_Clouds)  # 厚云
-    sky_loc = np.where(s > μ_sky - 3 * σ_sky)  # 天空
+    sky_loc = np.where(s < μ_sky + 3 * σ_sky)  # 天空
     unknow_loc = np.where((μ_Clouds + 3 * σ_Clouds <= s) & (s <= μ_sky - 3 * σ_sky))  # 薄云
 
     s[sky_loc] = 1
@@ -94,7 +134,7 @@ def f3(image_path):
     cv2.destroyAllWindows()
 
 
-def jisuan(image_path):
+def calculate_mean_var(image_path):
     img = cv2.imread(image_path)
 
     hls = cv2.cvtColor(img, cv2.COLOR_BGR2HLS)  # 这就是ihs颜色空间：
@@ -103,6 +143,8 @@ def jisuan(image_path):
     mean = np.mean(s)
     var = np.var(s)
 
+    print(image_path)
+    print("\n")
     print("均值为：%f\n" % mean)
     print("方差为：%f\n" % var)
 
@@ -111,12 +153,21 @@ if __name__ == '__main__':
     train_image_path1 = r'D:\PycharmProjects\cloudage\train\sky.jpg'
     train_image_path2 = r'D:\PycharmProjects\cloudage\train\cloude.jpg'
     train_image_path3 = r'D:\PycharmProjects\cloudage\train\light cloud.jpg'
-    jisuan(train_image_path1)
+    train_image_path4 = r'D:\PycharmProjects\cloudage\train\s61.jpg'
-    jisuan(train_image_path2)
+    train_image_path5 = r'D:\PycharmProjects\cloudage\train\s96.jpg'
-    jisuan(train_image_path3)
+    # calculate_mean_var(train_image_path1)
+    # calculate_mean_var(train_image_path2)
+    # calculate_mean_var(train_image_path3)
 
-    # image_path = r'D:\PycharmProjects\cloudage\photo\photo12.jpg'
+    # 证明python-opencv的饱和度值域范围未0-255
-    # # image_path = r'D:\PycharmProjects\cloudage\photo\cloud1_perfect.jpg'  # f3全为天空
+    calculate_mean_var(train_image_path4)
-    # # f1(image_path)
+    calculate_mean_var(train_image_path5)
-    # # f2(image_path)
+
-    # f3(image_path)
+
+    image_path = r'D:\PycharmProjects\cloudage\photo\photo12.jpg'
+    # image_path = r'D:\PycharmProjects\cloudage\photo\cloud1_perfect.jpg'
+    # ratio_method1(image_path)
+    # ratio_method2(image_path)
+    # ratio_method1_subtract_ratio_method2(image_path)
+
+    # saturability_method(image_path)