app.py

import spaces
# from transformers import Owlv2Processor, Owlv2ForObjectDetection, AutoProcessor, AutoModelForZeroShotObjectDetection
from transformers import Owlv2Processor, Owlv2ForObjectDetection
import torch
import gradio as gr

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

owl_model = Owlv2ForObjectDetection.from_pretrained("google/owlv2-base-patch16-ensemble").to("cuda")
owl_processor = Owlv2Processor.from_pretrained("google/owlv2-base-patch16-ensemble")

# dino_processor = AutoProcessor.from_pretrained("IDEA-Research/grounding-dino-base")
# dino_model = AutoModelForZeroShotObjectDetection.from_pretrained("IDEA-Research/grounding-dino-base").to("cuda")

@spaces.GPU
def infer(img, text_queries, score_threshold, model):
  
  if model == "dino":
    queries=""
    for query in text_queries:
      queries += f"{query}. "

    width, height = img.shape[:2]

    target_sizes=[(width, height)]
    inputs = dino_processor(text=queries, images=img, return_tensors="pt").to(device)

    with torch.no_grad():
      outputs = dino_model(**inputs)
      outputs.logits = outputs.logits.cpu()
      outputs.pred_boxes = outputs.pred_boxes.cpu()
      results = dino_processor.post_process_grounded_object_detection(outputs=outputs, input_ids=inputs.input_ids,
                                                                    box_threshold=score_threshold,
                                                                    target_sizes=target_sizes)
  elif model == "owl":
    size = max(img.shape[:2])
    target_sizes = torch.Tensor([[size, size]])
    inputs = owl_processor(text=text_queries, images=img, return_tensors="pt").to(device)

    with torch.no_grad():
      outputs = owl_model(**inputs)
      outputs.logits = outputs.logits.cpu()
      outputs.pred_boxes = outputs.pred_boxes.cpu()
      results = owl_processor.post_process_object_detection(outputs=outputs, target_sizes=target_sizes)
      
  boxes, scores, labels = results[0]["boxes"], results[0]["scores"], results[0]["labels"]
  result_labels = []

  for box, score, label in zip(boxes, scores, labels):
      box = [int(i) for i in box.tolist()]
      if score < score_threshold:
          continue
      if model == "owl":
        label = text_queries[label.cpu().item()]
        result_labels.append((box, label))
      elif model == "dino":
        if label != "":
            result_labels.append((box, label))
  return result_labels

# def query_image(img, text_queries, owl_threshold, dino_threshold):
def query_image(img, text_queries, owl_threshold):
    text_queries = text_queries
    text_queries = text_queries.split(",")
    owl_output = infer(img, text_queries, owl_threshold, "owl")
    # dino_output = infer(img, text_queries, dino_threshold, "dino")


    # return (img, owl_output), (img, dino_output)
    return (img, owl_output)

english_candidate_labels = ["hat", "sunglass", "hair band", "glove", "arm sleeve", "watch", "singlet", "t-shirts", "energy gel", "half pants", "socks", "shoes"]
korean_candidate_labels = ["모자", "썬글라스", "헤어밴드", "장갑", "팔토시", "시계", "싱글렛", "티셔츠", "에너지젤", "쇼츠바지", "양말", "신발"]
english_candidate_labels_string = ",".join(english_candidate_labels)

owl_threshold = gr.Slider(0, 1, value=0.16, label="OWL Threshold")
# dino_threshold = gr.Slider(0, 1, value=0.12, label="Grounding DINO Threshold")
owl_output = gr.AnnotatedImage(label="OWL Output")
# dino_output = gr.AnnotatedImage(label="Grounding DINO Output")
demo = gr.Interface(
    query_image,
    # inputs=[gr.Image(label="Input Image"), gr.Textbox(label="Candidate Labels"), owl_threshold, dino_threshold],
    inputs=[gr.Image(label="Input Image"), gr.Textbox(label="Candidate Labels", value=english_candidate_labels_string), owl_threshold],
    # outputs=[owl_output, dino_output],
    outputs=[owl_output],
    title="OWLv2 Demo",
    description="Compare two state-of-the-art zero-shot object detection models [OWLv2](https://huggingface.co/google/owlv2-base-patch16) . Simply enter an image and the objects you want to find with comma, or try one of the examples. Play with the threshold to filter out low confidence predictions in each model.",
    # examples=[["./bee.jpg", "bee, flower", 0.16, 0.12], ["./cats.png", "cat, fishnet", 0.16, 0.12]]
    examples=[["./rs_sample1.jpg", english_candidate_labels_string, 0.16, 0.12], ["./rs_sample2.jpg", english_candidate_labels_string, 0.16, 0.12]]
)
demo.launch(debug=True)