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Alex commited on
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0e3833c
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1 Parent(s): 4e5e0e7

clothes segmentation

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Files changed (3) hide show
  1. app-base64.py +0 -82
  2. app.py +63 -7
  3. response.json +1 -1
app-base64.py DELETED
@@ -1,82 +0,0 @@
1
- from fastapi import FastAPI, File, UploadFile
2
- from pydantic import BaseModel
3
- from transformers import SamModel, SamProcessor
4
- import torch
5
- from PIL import Image
6
- import numpy as np
7
- import io
8
- import base64
9
-
10
- class ImageRequest(BaseModel):
11
- image_base64: str
12
-
13
- # Inizializza l'app FastAPI
14
- app = FastAPI()
15
-
16
- # Carica il modello e il processore SAM
17
- model = SamModel.from_pretrained("facebook/sam-vit-base")
18
- processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
19
- model.to("cpu") # Usa CPU per il free tier
20
-
21
- @app.get("/health")
22
- async def health_check():
23
- return {"status": "ok"}
24
-
25
- def preprocess_image(image: Image.Image, size=(320, 320)):
26
- """Ridimensiona l'immagine per velocizzare l'inferenza"""
27
- img = image.convert("RGB")
28
- img = img.resize(size, Image.LANCZOS) # 320x320 è veloce su CPU
29
- return img
30
-
31
- # Funzione per segmentare l'immagine
32
- def segment_image(image: Image.Image):
33
- # Prepara l'input per SAM
34
- inputs = processor(image, return_tensors="pt").to("cpu")
35
-
36
- # Inferenza
37
- with torch.no_grad():
38
- outputs = model(**inputs, multimask_output=False)
39
-
40
- # Post-processa la maschera
41
- mask = processor.image_processor.post_process_masks(
42
- outputs.pred_masks, inputs["original_sizes"], inputs["reshaped_input_sizes"]
43
- )[0][0].cpu().numpy()
44
-
45
- # Converti la maschera in immagine
46
- mask_img = Image.fromarray((mask * 255).astype(np.uint8))
47
-
48
- # Converti la maschera in base64 per la risposta
49
- buffered = io.BytesIO()
50
- mask_img.save(buffered, format="PNG")
51
- mask_base64 = base64.b64encode(buffered.getvalue()).decode("utf-8")
52
-
53
- # Annotazioni
54
- annotations = {"mask": mask.tolist(), "label": "object"}
55
-
56
- return mask_base64, annotations
57
-
58
- # Endpoint API
59
- # @app.post("/segment")
60
- async def segment_endpoint(file: ImageRequest):
61
- try:
62
- # Decodifica la stringa Base64
63
- image_data = base64.b64decode(file.image_base64)
64
- image = Image.open(io.BytesIO(image_data))
65
- image = preprocess_image(image)
66
-
67
- # Segmenta l'immagine
68
- mask_base64, annotations = segment_image(image)
69
-
70
- # Restituisci la risposta
71
- return {
72
- "mask": f"data:image/png;base64,{mask_base64}",
73
- "annotations": annotations
74
- }
75
- except Exception as e:
76
- # In caso di errore (es. Base64 non valido), restituisci False
77
- return {"output": False, "error": str(e), "debug": file}
78
-
79
- # Per compatibilità con Hugging Face Spaces (Uvicorn viene gestito automaticamente)
80
- # if __name__ == "__main__":
81
- # import uvicorn
82
- # uvicorn.run(app, host="0.0.0.0", port=7860)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
app.py CHANGED
@@ -1,13 +1,9 @@
1
  from fastapi import FastAPI, File, UploadFile, HTTPException
2
- from transformers import SegformerForSemanticSegmentation, SegformerImageProcessor
3
- import torch
4
  from pydantic import BaseModel
5
  from PIL import Image
6
  import numpy as np
7
- import io
8
- import base64
9
- import logging
10
- import requests
11
  import torch.nn as nn
12
 
13
  # Inizializza l'app FastAPI
@@ -32,6 +28,18 @@ except Exception as e:
32
  logger.error(f"Errore nel caricamento del modello: {str(e)}")
33
  raise RuntimeError(f"Errore nel caricamento del modello: {str(e)}")
34
 
 
 
 
 
 
 
 
 
 
 
 
 
35
  # Funzione per segmentare l'immagine
36
  def segment_image(image: Image.Image):
37
  # Prepara l'input per SegFormer
@@ -123,7 +131,7 @@ async def segment_url_endpoint(image_data: ImageURL):
123
  return {
124
  "mask": f"data:image/png;base64,{mask_base64}",
125
  "size": image.size,
126
- "labels" : upsampled_logits
127
  }
128
 
129
  except Exception as e:
@@ -131,6 +139,54 @@ async def segment_url_endpoint(image_data: ImageURL):
131
  raise HTTPException(status_code=500, detail=f"Error processing image: {str(e)}")
132
 
133
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
134
  # Per compatibilità con Hugging Face Spaces
135
  if __name__ == "__main__":
136
  import uvicorn
 
1
  from fastapi import FastAPI, File, UploadFile, HTTPException
2
+ from transformers import SegformerForSemanticSegmentation, SegformerImageProcessor, AutoModelForSemanticSegmentation
 
3
  from pydantic import BaseModel
4
  from PIL import Image
5
  import numpy as np
6
+ import io, base64, logging, requests, torch
 
 
 
7
  import torch.nn as nn
8
 
9
  # Inizializza l'app FastAPI
 
28
  logger.error(f"Errore nel caricamento del modello: {str(e)}")
29
  raise RuntimeError(f"Errore nel caricamento del modello: {str(e)}")
30
 
31
+
32
+ # Add new model and processor initialization after existing ones
33
+ try:
34
+ logger.info("Loading clothes segmentation model...")
35
+ clothes_model = AutoModelForSemanticSegmentation.from_pretrained("mattmdjaga/segformer_b2_clothes")
36
+ clothes_processor = SegformerImageProcessor.from_pretrained("mattmdjaga/segformer_b2_clothes")
37
+ clothes_model.to("cpu")
38
+ logger.info("Clothes model loaded successfully.")
39
+ except Exception as e:
40
+ logger.error(f"Error loading clothes model: {str(e)}")
41
+ raise RuntimeError(f"Error loading clothes model: {str(e)}")
42
+
43
  # Funzione per segmentare l'immagine
44
  def segment_image(image: Image.Image):
45
  # Prepara l'input per SegFormer
 
131
  return {
132
  "mask": f"data:image/png;base64,{mask_base64}",
133
  "size": image.size,
134
+ "labels" : pred_seg
135
  }
136
 
137
  except Exception as e:
 
139
  raise HTTPException(status_code=500, detail=f"Error processing image: {str(e)}")
140
 
141
 
142
+
143
+ # Add new endpoint
144
+ @app.post("/segment-clothes-url")
145
+ async def segment_clothes_url_endpoint(image_data: ImageURL):
146
+ try:
147
+ logger.info("Downloading image from URL...")
148
+ response = requests.get(image_data.url, stream=True)
149
+ if response.status_code != 200:
150
+ raise HTTPException(status_code=400, detail="Could not download image from URL")
151
+
152
+ # Open image from URL
153
+ image = Image.open(response.raw).convert("RGB")
154
+
155
+ # Process image with SegFormer
156
+ logger.info("Processing image...")
157
+ inputs = clothes_processor(images=image, return_tensors="pt")
158
+ outputs = clothes_model(**inputs)
159
+ logits = outputs.logits.cpu()
160
+
161
+ # Upsample logits to match original image size
162
+ upsampled_logits = nn.functional.interpolate(
163
+ logits,
164
+ size=image.size[::-1],
165
+ mode="bilinear",
166
+ align_corners=False,
167
+ )
168
+
169
+ # Get prediction
170
+ pred_seg = upsampled_logits.argmax(dim=1)[0]
171
+
172
+ # Convert to image
173
+ mask_img = Image.fromarray((pred_seg.numpy() * 255).astype(np.uint8))
174
+
175
+ # Convert to base64
176
+ buffered = io.BytesIO()
177
+ mask_img.save(buffered, format="PNG")
178
+ mask_base64 = base64.b64encode(buffered.getvalue()).decode("utf-8")
179
+
180
+ return {
181
+ "mask": f"data:image/png;base64,{mask_base64}",
182
+ "size": image.size,
183
+ "predictions": pred_seg.numpy().tolist()
184
+ }
185
+
186
+ except Exception as e:
187
+ logger.error(f"Error processing URL: {str(e)}")
188
+ raise HTTPException(status_code=500, detail=f"Error processing image: {str(e)}")
189
+
190
  # Per compatibilità con Hugging Face Spaces
191
  if __name__ == "__main__":
192
  import uvicorn
response.json CHANGED
@@ -1 +1 @@
1
- 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","size":[1000,1500],"labels":{}}
 
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