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Browse files- app.py +33 -0
- requirements.txt +8 -0
app.py
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import cv2
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import gradio as gr
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import numpy as np
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import spaces
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import supervision as sv
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from autodistill.detection import CaptionOntology
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from autodistill.utils import plot
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from autodistill_grounded_sam_2 import GroundedSAM2
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@spaces.GPU
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def greet(image):
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base_model = GroundedSAM2(
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ontology=CaptionOntology({"container id": "container number", "logo": "logo"}),
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model="Grounding DINO",
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grounding_dino_box_threshold=0.25,
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)
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results = base_model.predict("container1.jpg").with_nms()
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results = results[results.confidence > 0.3]
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# print(results)
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image = cv2.imread("container1.jpg")
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mask_annotator = sv.BoxAnnotator()
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annotated_image = mask_annotator.annotate(image.copy(), detections=results)
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return annotated_image
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demo = gr.Interface(fn=greet, inputs="image", outputs="image")
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demo.launch()
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requirements.txt
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torch
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autodistill
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numpy>=1.20.0
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opencv-python>=4.6.0
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supervision
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roboflow
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autodistill_florence_2
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rf_groundingdino
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