app.py

import gradio as gr
import torch
from PIL import Image, ImageDraw, ImageFont

from transformers import AutoImageProcessor, AutoModelForObjectDetection

model_save_path = "matt0017/rt_detrv2_finetuned_trashify_box_detector_v1"

image_processor = AutoImageProcessor.from_pretrained(model_save_path)
model = AutoModelForObjectDetection.from_pretrained(model_save_path)

device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device)

id2label = model.config.id2label

color_dict = {
    "bin": "green",
    "trash": "blue",
    "hand": "purple",
    "trash_arm": "yellow",
    "not_trash": "red",
    "not_bin": "red",
    "not_hand": "red",
}

def predict_on_image(image, conf_threshold):
  model.eval()

  with torch.no_grad():
    inputs = image_processor(images=[image], return_tensors="pt")
    model_outputs = model(**inputs.to(device))

    target_sizes = torch.tensor([[image.size[1], image.size[0]]])

    results = image_processor.post_process_object_detection(model_outputs,
                                                            threshold=conf_threshold,
                                                            target_sizes=target_sizes)[0]
  for key, value in results.items():
    try:
      results[key] = value.item().cpu()
    except:
      results[key] = value.cpu()

  draw = ImageDraw.Draw(image)

  font = ImageFont.load_default(size=20)

  detected_class_name_text_labels = []

  for box, score, label in zip(results["boxes"], results["scores"], results["labels"]):
    x, y, x2, y2 = tuple(box.tolist())

    label_name = id2label[label.item()]
    targ_color = color_dict[label_name]
    detected_class_name_text_labels.append(label_name)

    draw.rectangle(xy=(x, y, x2, y2),
                   outline=targ_color,
                   width=3)

    text_string_to_show = f"{label_name} ({round(score.item(), 3)})"

    draw.text(xy=(x, y),
              text=text_string_to_show,
              fill="white",
              font=font)
  del draw

  target_items = {"trash", "bin", "hand"}
  detected_items = set(detected_class_name_text_labels)

  if not detected_items & target_items:
    return_string = (
        f"No trash, bin, or hand detected at confidence threshold {conf_threshold}."
        "Try another image or lowering the confidence threshold."
    )
    print(return_string)
    return image, return_string
  missing_items = target_items - detected_items
  if missing_items:
    return_string = (
        f"Detected the following items: {sorted(detected_items & target_items)}. But missing the following in order to get +1: {sorted(missing_items)}. "
        "If this is an error, try another image or altering the confidence threshold. "
        "Otherwise, the model may need to be updated with better data."
    )
    print(return_string)
    return image, return_string
  
  return_string = f"+1! Found the following items: {sorted(detected_items)}, thank you for cleaning up the area!"
  print(return_string)
  return image, return_string

description = """
Help clean up your local area! Upload an image and get +1 if there is all of the following items detected: trash, bin, hand.

Model is a fine-tuned version of [RT-DETRv2](https://huggingface.co/docs/transformers/main/en/model_doc/rt_detr_v2#transformers.RTDetrV2Config) on the [Trashify dataset](https://huggingface.co/datasets/mrdbourke/trashify_manual_labelled_images).

See the full data loading and training code on [learnhuggingface.com](https://www.learnhuggingface.com/notebooks/hugging_face_object_detection_tutorial).

This version is v4 because the first three versions were using a different model and did not perform as well, see the [README](https://huggingface.co/spaces/mrdbourke/trashify_demo_v4/blob/main/README.md) for more.
"""

demo = gr.Interface(
    fn=predict_on_image,
    inputs=[
        gr.Image(type="pil", label="Target Image"),
        gr.Slider(minimum=0, maximum=1, value=0.3, label="Confidence Threshold")
        ],
    outputs=[
        gr.Image(type="pil", label="Image Output"),
        gr.Text(label="Text Output")
        ],
    title="🚮 Trashify Object Detection Demo V4",
    description=description,
    examples=[
        ["trashify_examples/trashify_example_1.jpeg", 0.3],
        ["trashify_examples/trashify_example_2.jpeg", 0.3],
        ["trashify_examples/trashify_example_3.jpeg", 0.3],
    ],
    cache_examples=True
)

demo.launch()