app.py

import torch
from PIL import Image, ImageDraw, ImageFont
from transformers import GroundingDinoProcessor
from modeling_grounding_dino import GroundingDinoForObjectDetection

from PIL import Image, ImageDraw, ImageFont
from itertools import cycle
import os
from datetime import datetime
import gradio as gr

# Load model and processor
model_id = "fushh7/llmdet_swin_large_hf"
model_id = "fushh7/llmdet_swin_tiny_hf"
DEVICE = "cpu"

print(f"[INFO] Using device: {DEVICE}")
print(f"[INFO] Loading model from {model_id}...")

processor = GroundingDinoProcessor.from_pretrained(model_id)
model = GroundingDinoForObjectDetection.from_pretrained(model_id).to(DEVICE)
model.eval()

print("[INFO] Model loaded successfully.")

# Pre-defined palette (extend or tweak as you like)
BOX_COLORS = [
    "deepskyblue", "red", "lime", "dodgerblue",
    "cyan", "magenta", "yellow",
    "orange", "chartreuse"
]


def save_cropped_images(original_image, boxes, labels, scores, output_dir="static/output_crops"):
    """
    Salva ogni regione ritagliata definita dalle bounding box in file separati.

    :param original_image: Immagine PIL originale
    :param boxes: Lista di bounding box [x_min, y_min, x_max, y_max]
    :param labels: Lista di etichette per ogni box
    :param scores: Lista di punteggi di confidenza
    :param output_dir: Directory base dove salvare le immagini
    :return: Lista dei percorsi dei file salvati
    """
    # Crea una directory con timestamp per evitare sovrascritture
    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
    output_path = os.path.join(output_dir, f"detections_{timestamp}")
    os.makedirs(output_path, exist_ok=True)

    saved_paths = []

    for i, (box, label, score) in enumerate(zip(boxes, labels, scores)):
        # Pulisci il label per usarlo nel nome del file
        clean_label = "".join(c if c.isalnum() else "_" for c in label)

        # Ritaglia la regione dall'immagine originale
        cropped_img = original_image.crop(box)

        # Crea il nome del file
        filename = f"crop_{i}_{clean_label}_{score:.2f}.jpg"
        filepath = os.path.join(output_path, filename)

        # Salva l'immagine ritagliata
        cropped_img.save(filepath)
        saved_paths.append(filepath)

    return saved_paths


def draw_boxes(image, boxes, labels, scores, colors=BOX_COLORS, font_path="arial.ttf", font_size=16):
    """
    Draw bounding boxes and labels on a PIL Image.

    :param image: PIL Image object
    :param boxes: Iterable of [x_min, y_min, x_max, y_max]
    :param labels: Iterable of label strings
    :param scores: Iterable of scalar confidences (0-1)
    :param colors: List/tuple of colour names or RGB tuples
    :param font_path: Path to a TTF font for labels
    :param font_size: Int size of font to use, default 16
    :return: PIL Image with drawn boxes
    """
    # Ensure we can iterate colours indefinitely
    colour_cycle = cycle(colors)
    draw = ImageDraw.Draw(image)

    # Pick a font (fallback to default if missing)
    try:
        font = ImageFont.truetype(font_path, size=font_size)
    except IOError:
        font = ImageFont.load_default(size=font_size)

    # Assign a consistent colour per label (optional)
    label_to_colour = {}

    for box, label, score in zip(boxes, labels, scores):
        # Reuse colour if label seen before, else take next from cycle
        colour = label_to_colour.setdefault(label, next(colour_cycle))

        x_min, y_min, x_max, y_max = map(int, box)

        # Draw rectangle
        draw.rectangle([x_min, y_min, x_max, y_max], outline=colour, width=2)

        # Compose text
        text = f"{label} ({score:.3f})"
        text_size = draw.textbbox((0, 0), text, font=font)[2:]

        # Draw text background for legibility
        bg_coords = [x_min, y_min - text_size[1] - 4,
                     x_min + text_size[0] + 4, y_min]
        draw.rectangle(bg_coords, fill=colour)

        # Draw text
        draw.text((x_min + 2, y_min - text_size[1] - 2),
                  text, fill="black", font=font)

    return image

def resize_image_max_dimension(image, max_size=4096):
    """
    Resize an image so that the longest side is at most max_size pixels,
    while maintaining the aspect ratio.

    :param image: PIL Image object
    :param max_size: Maximum dimension in pixels (default: 1024)
    :return: PIL Image object (resized)
    """
    width, height = image.size

    # Check if resizing is needed
    if max(width, height) <= max_size:
        return image

    # Calculate new dimensions maintaining aspect ratio
    ratio = max_size / max(width, height)
    new_width = int(width * ratio)
    new_height = int(height * ratio)

    # Resize the image using high-quality resampling
    return image.resize((new_width, new_height), Image.Resampling.LANCZOS)

def detect_and_draw(
    img: Image.Image,
    text_query: str,
    box_threshold: float = 0.14,
    text_threshold: float = 0.13,
    save_crops: bool = True
) -> Image.Image:
    """
    Detect objects described in `text_query`, draw boxes, return the image.
    Note: `text_query` must be lowercase and each concept ends with a dot
          (e.g. 'a cat. a remote control.')
    """

    # Make sure text is lowered
    text_query = text_query.lower()

    # If the image size is too large, we make it smaller
    img = resize_image_max_dimension(img, max_size=4096)

    # Preprocess the image
    inputs = processor(images=img, text=text_query, return_tensors="pt").to(DEVICE)

    with torch.no_grad():
        outputs = model(**inputs)

    results = processor.post_process_grounded_object_detection(
        outputs,
        inputs.input_ids,
        box_threshold=box_threshold,
        text_threshold=text_threshold,
        target_sizes=[img.size[::-1]]
    )[0]

    img_out = img.copy()
    img_out = draw_boxes(
        img_out,
        boxes  = results["boxes"].cpu().numpy(),
        labels = results.get("text_labels", results.get("labels", [])),
        scores = results["scores"]
    )
    if save_crops:
        saved_paths = save_cropped_images(
            img,
            boxes=results["boxes"].cpu().numpy(),
            labels=results.get("text_labels", results.get("labels", [])),
            scores=results["scores"]
        )
        print(f"Saved {len(saved_paths)} cropped images to: {os.path.dirname(saved_paths[0])}")

    return img_out

# Create example list
examples = [
    ["examples/stickers(1).jpg", "stickers. labels.", 0.24, 0.23],
#    ["examples/IMG_8920.jpeg", "bin. water bottle. hand. shoe.", 0.4, 0.3],
#    ["examples/IMG_9435.jpeg", "lettuce. orange slices (group). eggs (group). cheese (group). red cabbage. pear slices (group).", 0.4, 0.3],
]

# Create Gradio demo
app = gr.Interface(
    fn      = detect_and_draw,
    inputs  = [
        gr.Image(type="pil", label="Image"),
        gr.Textbox(value="stickers",
                   label="Text Query (lowercase, end each with '.', for example 'a bird. a tree.')"),
        gr.Slider(0.0, 1.0, 0.14, 0.05, label="Box Threshold"),
        gr.Slider(0.0, 1.0, 0.13, 0.05, label="Text Threshold")
    ],
    outputs = gr.Image(type="pil", label="Detections"),
    title   = "Sticker Geo Tagger",
    description = f"""Upload an image containings stickers and adjust thresholds to see detections.
    <a href='/output_crops/' target='crops'>output_crops</a>
    """,
    examples = examples,
    cache_examples = True,
)

#app.launch(server_name="0.0.0.0", server_port=22590, root_path="/stikkiers2", share=False)
app.launch(server_name="0.0.0.0", share=False)