Each channel of Image for segmentation

madhavkhoslaa · Jul 22, 2019 · 3a0fc3b · 3a0fc3b
1 parent ce4bc75
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Showing 32 changed files with 6 additions and 20 deletions.
diff --git a/Ch/0.jpeg b/Ch/0.jpeg
diff --git a/Ch/000035_10 copy.tif b/Ch/000035_10 copy.tif
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diff --git a/test.py b/test.py
@@ -1,24 +1,10 @@
 from utils.one_hot_encoder import HotEncoder
 import  numpy as np
+import torch
 import skimage
-img__= skimage.io.imread("/Users/madhav/Desktop/Screenshot 2019-07-17 at 11.27.51 PM.png")
-encoder= HotEncoder(dir= "/Users/madhav/Desktop/", extension="png", is_binary= False)
+from torch.nn.functional import one_hot
+img__= skimage.io.imread("/Users/madhav/DataSets/data_semantics/training/semantic_rgb/000035_10.tif")
+encoder= HotEncoder(dir= '/Users/madhav/DataSets/data_semantics/training/semantic_rgb', extension="tif", is_binary= False, n_classes= 30)
 color_dict= encoder.gen_colors()
-
-def rgb_to_onehot(rgb_arr, color_dict):
-    num_classes = len(color_dict)
-    shape = rgb_arr.shape[:2]+(num_classes,)
-    arr = np.zeros( shape, dtype=np.int8 )
-    for i, cls in enumerate(color_dict):
-        arr[:,:,i] = np.all(rgb_arr.reshape( (-1,3) ) == color_dict[i], axis=1).reshape(shape[:2])
-    return arr
-
-
-def onehot_to_rgb(onehot, color_dict):
-    single_layer = np.argmax(onehot, axis=-1)
-    output = np.zeros( onehot.shape[:2]+(3,) )
-    for k in color_dict.keys():
-        output[single_layer==k] = color_dict[k]
-    return np.uint8(output)
-class_mat= encoder.PerPixelClassMatrix(img__)
-print(class_mat)
+onehottt= encoder.PerPixelClassMatrix(img__)
+encoded= encoder.HotEncode(onehottt)