Martin Tomov commited on
Commit
b3f552a
·
verified ·
1 Parent(s): d722625

Update app.py

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Files changed (1) hide show
  1. app.py +0 -18
app.py CHANGED
@@ -13,8 +13,6 @@ import matplotlib.pyplot as plt
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  from transformers import AutoModelForMaskGeneration, AutoProcessor, pipeline
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  import gradio as gr
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  import spaces
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- import time
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- import httpx
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  @dataclass
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  class BoundingBox:
@@ -106,26 +104,10 @@ def refine_masks(masks: torch.BoolTensor, polygon_refinement: bool = False) -> L
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  masks[idx] = cv2.fillPoly(np.zeros(shape, dtype=np.uint8), [polygon], 1)
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  return list(masks)
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- def startup_report_with_retries(client, retries=5, delay=2):
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- for i in range(retries):
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- try:
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- client.startup_report()
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- return
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- except httpx.ConnectTimeout:
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- if i < retries - 1:
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- time.sleep(delay)
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- else:
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- raise
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-
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  @spaces.GPU
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  def detect(image: Image.Image, labels: List[str], threshold: float = 0.3, detector_id: Optional[str] = None) -> List[Dict[str, Any]]:
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  detector_id = detector_id if detector_id else "IDEA-Research/grounding-dino-base"
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  object_detector = pipeline(model=detector_id, task="zero-shot-object-detection", device="cuda")
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-
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- # Initialize and call startup report with retries
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- client = httpx.Client()
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- startup_report_with_retries(client)
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-
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  labels = [label if label.endswith(".") else label+"." for label in labels]
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  results = object_detector(image, candidate_labels=labels, threshold=threshold)
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  return [DetectionResult.from_dict(result) for result in results]
 
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  from transformers import AutoModelForMaskGeneration, AutoProcessor, pipeline
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  import gradio as gr
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  import spaces
 
 
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  @dataclass
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  class BoundingBox:
 
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  masks[idx] = cv2.fillPoly(np.zeros(shape, dtype=np.uint8), [polygon], 1)
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  return list(masks)
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  @spaces.GPU
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  def detect(image: Image.Image, labels: List[str], threshold: float = 0.3, detector_id: Optional[str] = None) -> List[Dict[str, Any]]:
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  detector_id = detector_id if detector_id else "IDEA-Research/grounding-dino-base"
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  object_detector = pipeline(model=detector_id, task="zero-shot-object-detection", device="cuda")
 
 
 
 
 
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  labels = [label if label.endswith(".") else label+"." for label in labels]
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  results = object_detector(image, candidate_labels=labels, threshold=threshold)
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  return [DetectionResult.from_dict(result) for result in results]