pierre-catie
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Parent(s):
ec283a9
Upload 10 files
Browse files- README.md +5 -5
- Vaniila(1).png +0 -0
- animos(1).jpeg +0 -0
- app.py +98 -0
- assets_astronaut(1).png +0 -0
- catie(3).png +0 -0
- dej(1).jpeg +0 -0
- rayure(1).jpeg +0 -0
- requirements.txt +8 -0
- voitures.jpg +0 -0
README.md
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---
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title: Nestor
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emoji:
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colorFrom:
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sdk: gradio
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sdk_version: 4.
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app_file: app.py
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pinned: false
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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---
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title: Nestor
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emoji: 👁
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colorFrom: red
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colorTo: gray
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sdk: gradio
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sdk_version: 4.32.1
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app_file: app.py
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pinned: false
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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Vaniila(1).png
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animos(1).jpeg
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app.py
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import torch
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import gradio as gr
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from transformers import Owlv2Processor, Owlv2ForObjectDetection, pipeline
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import spaces
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from pathlib import Path
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# Use GPU if available
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if torch.cuda.is_available():
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device = torch.device("cuda")
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else:
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device = torch.device("cpu")
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model = Owlv2ForObjectDetection.from_pretrained("google/owlv2-base-patch16-ensemble").to(device)
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processor = Owlv2Processor.from_pretrained("google/owlv2-base-patch16-ensemble")
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translator = pipeline("translation", model="Helsinki-NLP/opus-mt-fr-en")
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@spaces.GPU
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def query_image(img, description, score_threshold):
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description=description
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translation_result = translator(description ,
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src_lang="fr",
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tgt_lang="en")
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description = translation_result[0]['translation_text']
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description = description.split(",")
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size = max(img.shape[:2])
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target_sizes = torch.Tensor([[size, size]])
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inputs = processor(text=description, images=img, return_tensors="pt").to(device)
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with torch.no_grad():
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outputs = model(**inputs)
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outputs.logits = outputs.logits.cpu()
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outputs.pred_boxes = outputs.pred_boxes.cpu()
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results = processor.post_process_object_detection(outputs=outputs, target_sizes=target_sizes)
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boxes, scores, labels = results[0]["boxes"], results[0]["scores"], results[0]["labels"]
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result_labels = []
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for box, score, label in zip(boxes, scores, labels):
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box = [int(i) for i in box.tolist()]
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if score < score_threshold:
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continue
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result_labels.append((box, description[label.item()]))
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return img, result_labels
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logo = r"""
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<center><img src='https://doubiiu.github.io/projects/ToonCrafter/static/logo2.png' alt='ToonCrafter logo' style="width:280px; margin-bottom:2px"></center>
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"""
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gr.Markdown(logo)
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description = """
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***logo du catie***.
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Détecter des objets en vocabulaire ouvert est une tâche difficile, étant donné la grande variété d'images possibles et de sujets possibles, cependant de récents modèles permettent de faire cela.
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En voici notre adaptation en français.
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Utilisez ce démonstrateur pour requêter n'importe quelle image avec la description textuelle d'un objet.
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Pour s'en servir, chargez votre image et entrez des descritions séparées par des virgules.
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Vous pouvez utiliser le seuil pour filtrer les détections avec une faible probabilité.
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Si vous avez besoin d'un tel modèle, qui fonctionnera en temps réel, sera spécialisé sur vos propres données, de petite taille donc économe en énergie, contactez-nous : [email protected]
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"""
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#image_path="./Vaniila.png"
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#absolute_path = Path(image_path).resolve()
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#gr.Image('Vaniila.png',
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# height=500,width=176,
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# show_download_button=False)
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demo = gr.Interface(
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query_image,
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inputs=[gr.Image(), "text", gr.Slider(0, 1, value=0.1)],
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outputs="annotatedimage",
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title="Détection d'objets en vocabulaire ouvert.",
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description=description,
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#css=".gradio-container {background: url('file=Vaniila.png')}",
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examples=[
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["voitures.jpg", "voiture orange, roues", 0.21],
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["animos.jpeg", "lion, chimpanzé", 0.37],
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["dej.jpeg", "jambon, assiette, croissant", 0.3],
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],
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)
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demo.launch()
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assets_astronaut(1).png
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catie(3).png
ADDED
dej(1).jpeg
ADDED
rayure(1).jpeg
ADDED
requirements.txt
ADDED
@@ -0,0 +1,8 @@
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numpy>=1.18.5
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torch>=1.7.0
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torchvision>=0.8.1
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git+https://github.com/huggingface/transformers.git
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scipy
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spaces
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sentencepiece
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pathlib
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voitures.jpg
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