commit

.bashrc

@@ -0,0 +1,2 @@
+export PATH="$PATH:/c/Users/sarah/anaconda3:/c/Users/sarah/anaconda3/Scripts"
+alias python="winpty python.exe"

app.py

@@ -1,22 +1,89 @@
 import streamlit as st
 
-import streamlit as st
-from transformers import pipeline
-from PIL import Image
+st.title("Segmentation of Beauty Products")
+
+file_name = st.file_uploader("Upload a a beauty product")
+
+
+from transformers import SegformerImageProcessor, SegformerForSemanticSegmentation
+import torch
 
-pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-st.title("Hot Dog? Or Not?")
+model_name = "nvidia/segformer-b5-finetuned-ade-640-640"
+feature_extractor = SegformerImageProcessor.from_pretrained(model_name)
+model = SegformerForSemanticSegmentation.from_pretrained(model_name)
+model.to(device)
 
-file_name = st.file_uploader("Upload a hot dog candidate image")
 
 if file_name is not None:
-    col1, col2 = st.columns(2)
+    image = Image.open(img_name)
+    image.show()
+
+    pixel_values = feature_extractor(image, return_tensors="pt").pixel_values.to(device)
+
+    outputs = model(pixel_values)
+    logits = outputs.logits
+
+    def ade_palette():
+        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]]
 
-    image = Image.open(file_name)
-    col1.image(image, use_column_width=True)
-    predictions = pipeline(image)
+    from torch import nn
+    import numpy as np
+    import matplotlib.pyplot as plt
 
-    col2.header("Probabilities")
-    for p in predictions:
-        col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")
+    logits = nn.functional.interpolate(outputs.logits.detach().cpu(), 
+                                       size=image.size[::-1],
+                                       mode='bilinear',
+                                       align_corners=False)
+    seg = logits.argmax(dim=1)[0]
+    color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8)
+    palette = np.array(ade_palette())
+    for label, color in enumerate(palette):
+        color_seg[seg == label, :] = color
+        
+        color_seg = color_seg[..., ::-1]
+        
+        img = np.array(image) * 0.5 + color_seg * 0.5
+        img = img.astype(np.uint8)
+        
+        plt.figure(figsize=(15, 10))
+        plt.imshow(img)
+        plt.show()