app.py

# 1. Import the required dependencies
import gradio as gr
import torch 
import spaces # for GPU usage

from PIL import Image, ImageDraw, ImageFont 

from transformers import AutoImageProcessor, AutoModelForObjectDetection

# Model path = mrdbourke/rt_detrv2_finetuned_trashify_box_detector_v1 

# 2. Setup preprocessing and model functions - mrdbourke/rt_detrv2_finetuned_trashify_box_detector_v1 
model_save_path = "mrdbourke/rt_detrv2_finetuned_trashify_box_detector_v1"

# Load image processor
image_processor = AutoImageProcessor.from_pretrained(model_save_path)

# Default size to 640x640 for simplicity, also handles strange shaped images 
image_processor.size = {"height": 640, 
                        "width": 640}

# Load the model
model = AutoModelForObjectDetection.from_pretrained(model_save_path)

# Setup the target device (use GPU if it's accessible)
# Note: if you want to use a GPU in your Space, you can use ZeroGPU: https://huggingface.co/docs/hub/en/spaces-zerogpu 
device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device)

# Get the id2label dictionary from the model
id2label = model.config.id2label

# Setup a color dictionary for pretty drawings
color_dict = {
    "bin": "green",
    "trash": "blue",
    "hand": "purple",
    "trash_arm": "yellow",
    "not_trash": "red",
    "not_bin": "red",
    "not_hand": "red"
}

# 3. Create a function predict_on_image
# Use a GPU on a target function
@spaces.GPU
def predict_on_image(image, conf_threshold):
  model.eval()

  # Make a prediction on target image
  with torch.no_grad():
    inputs = image_processor(images=[image], 
                             return_tensors="pt")
    model_outputs = model(**inputs.to(device))

    # Get original size of image
    # PIL.Image.size = width, height
    # But post_process_object_detection requires height, width
    target_sizes = torch.tensor([[image.size[1], image.size[0]]]) # -> [batch_size, height, width]

    print(target_sizes)

    # Post process the raw outputs from the model
    results = image_processor.post_process_object_detection(model_outputs,
                                                            threshold=conf_threshold,
                                                            target_sizes=target_sizes)[0]
  
  # Return all data items/objects to the CPU if they aren't already there
  for key, value in results.items():
    try:
      results[key] = value.item().cpu() # can't get scalars as .item() so add try/except block
    except:
      results[key] = value.cpu()
  
  ### 4. Draw the predictions on the target image ###
  draw = ImageDraw.Draw(image)

  # Get a font to write on our image
  font = ImageFont.load_default(size=20)

  # Get a list of the detect class names
  detected_class_names_text_labels = []

  # Iterate through the predictions of the model and draw them on the target image
  for box, score, label in zip(results["boxes"], results["scores"], results["labels"]):
    # Create the coordinates
    x, y, x2, y2 = tuple(box.tolist()) # XYXY 

    # Get the text-based label
    label_name = id2label[label.item()]
    targ_color = color_dict[label_name]
    detected_class_names_text_labels.append(label_name)

    # Draw the rectangle
    draw.rectangle(xy=(x, y, x2, y2),
                   outline=targ_color,
                   width=3)
    
    # Create the text to display on the box
    text_string_to_show = f"{label_name} ({round(score.item(), 4)})"

    # Draw the text on the image
    draw.text(xy=(x, y),
              text=text_string_to_show,
              fill="white",
              font=font)

  # Remove the draw each time to make sure it doesn't get caught in memory
  del draw

  ### 5. Create logic for outputting information message
  
  # Setup set of target items to discover
  target_items = {"trash", "bin", "hand"}
  detected_items = set(detected_class_names_text_labels)

  # If no items detected or bin, trash, hand not in detected items, return notification
  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

  # If there are items missing, output what's missing for +1 point
  missing_items = target_items - detected_items
  if missing_items:
    return_string =  (
        f"Detected the following items: {sorted(detected_items & target_items)}. "
        f"Missing the following: {missing_items}. "
        "In order to get +1 point, all target items must be detected."
    )
    print(return_string)
    return image, return_string
  
  # Final case, all items are detected
  return_string = f"+1! Found the following items: {sorted(detected_items)}, thank you for cleaning up your local area!"
  print(return_string)
  return image, return_string

### 6. Setup the demo application to take in image/conf threshold, pass it through our function, show the output image/text

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.
"""

# Create the Gradio interface
demo = gr.Interface(
    fn=predict_on_image,
    inputs=[
        gr.Image(type="pil", label="Target Input Image"),
        gr.Slider(minimum=0, maximum=1, value=0.3, label="Confidence Threshold (set higher for more confident boxes)")
    ],
    outputs=[
        gr.Image(type="pil", label="Target Image Output"),
        gr.Text(label="Text Output")
    ],
    description=description,
    title="🚮 Trashify Object Detection Demo V4 - Video", 
    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
)

# Launch demo
# demo.launch(debug=True) # run with debug=True to see errors in Google Colab
demo.launch()