added a different color palette

.gitignore

@@ -1,3 +1,5 @@
 scrap*
 .DS_Store
 requirements_conda.txt
+app_0.py
+test.py

app.py

@@ -12,6 +12,13 @@ https://arxiv.org/abs/2005.12872
 Additions
 - add shown labels as strings
 - show only animal masks (ask an nlp model?)
+
+For next time
+- for diff 'confidence' the high conf masks should change....
+- colors are not great and should be constant per class? add text?
+- Im getting core dumped (segmentation fault) when loading hugging face model.. :()
+    https://github.com/huggingface/transformers/issues/16939 
+- cap slider to 95?
 """
 
 from transformers import DetrFeatureExtractor, DetrForSegmentation
@@ -21,163 +28,8 @@ import numpy as np
 import torch
 import torchvision
 
-# Returns a list with a color per ADE class (150 classes)
-# from https://huggingface.co/spaces/chansung/segformer-tf-transformers/blob/main/app.py
-def ade_palette():
-    """ADE20K palette that maps each class to RGB values."""
-    return [
-        [120, 120, 120],
-        [180, 120, 120],
-        [6, 230, 230],
-        [80, 50, 50],
-        [4, 200, 3],
-        [120, 120, 80],
-        [140, 140, 140],
-        [204, 5, 255],
-        [230, 230, 230],
-        [4, 250, 7],
-        [224, 5, 255],
-        [235, 255, 7],
-        [150, 5, 61],
-        [120, 120, 70],
-        [8, 255, 51],
-        [255, 6, 82],
-        [143, 255, 140],
-        [204, 255, 4],
-        [255, 51, 7],
-        [204, 70, 3],
-        [0, 102, 200],
-        [61, 230, 250],
-        [255, 6, 51],
-        [11, 102, 255],
-        [255, 7, 71],
-        [255, 9, 224],
-        [9, 7, 230],
-        [220, 220, 220],
-        [255, 9, 92],
-        [112, 9, 255],
-        [8, 255, 214],
-        [7, 255, 224],
-        [255, 184, 6],
-        [10, 255, 71],
-        [255, 41, 10],
-        [7, 255, 255],
-        [224, 255, 8],
-        [102, 8, 255],
-        [255, 61, 6],
-        [255, 194, 7],
-        [255, 122, 8],
-        [0, 255, 20],
-        [255, 8, 41],
-        [255, 5, 153],
-        [6, 51, 255],
-        [235, 12, 255],
-        [160, 150, 20],
-        [0, 163, 255],
-        [140, 140, 140],
-        [250, 10, 15],
-        [20, 255, 0],
-        [31, 255, 0],
-        [255, 31, 0],
-        [255, 224, 0],
-        [153, 255, 0],
-        [0, 0, 255],
-        [255, 71, 0],
-        [0, 235, 255],
-        [0, 173, 255],
-        [31, 0, 255],
-        [11, 200, 200],
-        [255, 82, 0],
-        [0, 255, 245],
-        [0, 61, 255],
-        [0, 255, 112],
-        [0, 255, 133],
-        [255, 0, 0],
-        [255, 163, 0],
-        [255, 102, 0],
-        [194, 255, 0],
-        [0, 143, 255],
-        [51, 255, 0],
-        [0, 82, 255],
-        [0, 255, 41],
-        [0, 255, 173],
-        [10, 0, 255],
-        [173, 255, 0],
-        [0, 255, 153],
-        [255, 92, 0],
-        [255, 0, 255],
-        [255, 0, 245],
-        [255, 0, 102],
-        [255, 173, 0],
-        [255, 0, 20],
-        [255, 184, 184],
-        [0, 31, 255],
-        [0, 255, 61],
-        [0, 71, 255],
-        [255, 0, 204],
-        [0, 255, 194],
-        [0, 255, 82],
-        [0, 10, 255],
-        [0, 112, 255],
-        [51, 0, 255],
-        [0, 194, 255],
-        [0, 122, 255],
-        [0, 255, 163],
-        [255, 153, 0],
-        [0, 255, 10],
-        [255, 112, 0],
-        [143, 255, 0],
-        [82, 0, 255],
-        [163, 255, 0],
-        [255, 235, 0],
-        [8, 184, 170],
-        [133, 0, 255],
-        [0, 255, 92],
-        [184, 0, 255],
-        [255, 0, 31],
-        [0, 184, 255],
-        [0, 214, 255],
-        [255, 0, 112],
-        [92, 255, 0],
-        [0, 224, 255],
-        [112, 224, 255],
-        [70, 184, 160],
-        [163, 0, 255],
-        [153, 0, 255],
-        [71, 255, 0],
-        [255, 0, 163],
-        [255, 204, 0],
-        [255, 0, 143],
-        [0, 255, 235],
-        [133, 255, 0],
-        [255, 0, 235],
-        [245, 0, 255],
-        [255, 0, 122],
-        [255, 245, 0],
-        [10, 190, 212],
-        [214, 255, 0],
-        [0, 204, 255],
-        [20, 0, 255],
-        [255, 255, 0],
-        [0, 153, 255],
-        [0, 41, 255],
-        [0, 255, 204],
-        [41, 0, 255],
-        [41, 255, 0],
-        [173, 0, 255],
-        [0, 245, 255],
-        [71, 0, 255],
-        [122, 0, 255],
-        [0, 255, 184],
-        [0, 92, 255],
-        [184, 255, 0],
-        [0, 133, 255],
-        [255, 214, 0],
-        [25, 194, 194],
-        [102, 255, 0],
-        [92, 0, 255],
-    ]
-                                           
+import itertools
+import seaborn as sns
 
 def predict_animal_mask(im,
                         gr_slider_confidence):
@@ -187,9 +39,9 @@ def predict_animal_mask(im,
     # encoding is a dict with pixel_values and pixel_mask
     encoding = feature_extractor(images=image, return_tensors="pt") #pt=Pytorch, tf=TensorFlow
     outputs = model(**encoding) # odict with keys: ['logits', 'pred_boxes', 'pred_masks', 'last_hidden_state', 'encoder_last_hidden_state']
-    logits = outputs.logits # torch.Size([1, 100, 251]); why 251?
+    logits = outputs.logits # torch.Size([1, 100, 251]); class logits? but  why 251?
     bboxes = outputs.pred_boxes
-    masks = outputs.pred_masks # torch.Size([1, 100, 200, 200]); for every pixel, score in each of the 100 classes? there is a mask per class
+    masks = outputs.pred_masks # torch.Size([1, 100, 200, 200]); mask logits? for every pixel, score in each of the 100 classes? there is a mask per class
 
     # keep only the masks with high confidence?--------------------------------
     # compute the prob per mask (i.e., class), excluding the "no-object" class (the last one)
@@ -200,8 +52,13 @@ def predict_animal_mask(im,
     # postprocess the mask (numpy arrays)
     label_per_pixel = torch.argmax(masks[keep].squeeze(),dim=0).detach().numpy() # from the masks per class, select the highest per pixel
     color_mask = np.zeros(image.size+(3,))
-    for lbl, color in enumerate(ade_palette()):
-        color_mask[label_per_pixel==lbl,:] = color
+    palette = itertools.cycle(sns.color_palette())
+    for lbl in np.unique(label_per_pixel): #enumerate(palette()):
+        color_mask[label_per_pixel==lbl,:] = np.asarray(next(palette))*255 #color
+
+    # color_mask = np.zeros(image.size+(3,))
+    # for lbl, color in enumerate(ade_palette()):
+    #     color_mask[label_per_pixel==lbl,:] = color
 
     # Show image + mask
     pred_img = np.array(image.convert('RGB'))*0.5 + color_mask*0.5
@@ -227,7 +84,7 @@ gr.Interface(predict_animal_mask,
                 inputs = [gr_image_input,gr_slider_confidence],
                 outputs = gr_image_output,
                 title = 'Image segmentation with varying confidence',
-                description = "An image segmentation webapp using DETR (End-to-End Object Detection) model with ResNet-50 backbone").launch()
+                description = "A panoptic (semantic+instance) segmentation webapp using DETR (End-to-End Object Detection) model with ResNet-50 backbone").launch()
 
 
 ####################################