snapshot...

modules/deeplearning/esrgan_exp.py

@@ -12,19 +12,18 @@ from util.setup import home_dir
 target_param = 'cld_opd_dcomp'
 
 # SAVED_MODEL_PATH = "https://tfhub.dev/captain-pool/esrgan-tf2/1"
-# SAVED_MODEL_PATH = '/Users/tomrink/Downloads/esrgan-tf2_1'
-SAVED_MODEL_PATH = home_dir+'/esrgan-tf2_1'
+SAVED_MODEL_PATH = home_dir + '/esrgan-tf2_1'
 
 model = hub.load(SAVED_MODEL_PATH)
 
 
-def get_image(in_file):
+def get_image_from_file(in_file):
     h5f = h5py.File(in_file, 'r')
 
     # s_x = slice(2622, 3134)
     # s_y = slice(2622, 3134)
-    s_x = slice(2238, 3518)
-    s_y = slice(2238, 3518)
+    s_x = slice(2110, 3646)
+    s_y = slice(2110, 3646)
     # s_x = slice(1854, 3902)
     # s_y = slice(1854, 3902)
 
@@ -51,6 +50,24 @@ def get_image(in_file):
     return data
 
 
+def get_image(data):
+    shape = data.shape
+    data = data.flatten()
+
+    lo = 0.0
+    hi = 160.0
+
+    data -= lo
+    data /= (hi - lo)
+
+    not_valid = np.isnan(data)
+    data[not_valid] = 0
+
+    data = np.reshape(data, shape)
+
+    return data
+
+
 def preprocess_image(hr_image):
   """ Loads image from path and preprocesses to make it model ready
       Args:
@@ -79,7 +96,8 @@ def preprocess_image(hr_image):
 def run(in_file, out_file):
     # model = hub.load(SAVED_MODEL_PATH)
     t0 = time.time()
-    hr_image = get_image(in_file)
+    hr_image = get_image_from_file(in_file)
+    # hr_image = get_image(in_file)
     hr_image = preprocess_image(hr_image)
     t1 = time.time()
     print('processing time: ', (t1-t0))
@@ -93,4 +111,4 @@ def run(in_file, out_file):
     if out_file is not None:
         np.save(out_file, fake_image)
     else:
-        return fake_image
+        return fake_image, hr_image