pipeline/logic.py

import numpy as np
import requests
import cv2
from skimage import feature
from io import BytesIO
from PIL import Image
import torch
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True

def get_canonical_label(object_name_phrase: str) -> str:
    print(f"\n [Label] Extracting label for: '{object_name_phrase}'")
    label = object_name_phrase.strip().lower().split()[-1]
    label = ''.join(filter(str.isalpha, label))
    print(f" [Label] ✅ Extracted label: '{label}'")
    return label if label else "unknown"

def download_image_from_url(image_url: str) -> Image.Image:
    print(f" [Download] Downloading image from: {image_url[:80]}...")
    response = requests.get(image_url)
    response.raise_for_status()
    image = Image.open(BytesIO(response.content))
    image_rgb = image.convert("RGB")
    print(" [Download] ✅ Image downloaded and standardized to RGB.")
    return image_rgb

def detect_and_crop(image: Image.Image, object_name: str, models: dict) -> Image.Image:
    print(f"\n [Detect & Crop] Starting detection for object: '{object_name}'")
    image_np = np.array(image.convert("RGB"))
    height, width = image_np.shape[:2]
    prompt = [[f"a {object_name}"]]
    inputs = models['processor_gnd'](images=image, text=prompt, return_tensors="pt").to(models['device'])

    with torch.no_grad():
        outputs = models['model_gnd'](
            **inputs,
            box_threshold=0.4,
            text_threshold=0.3
        )

    results = models['processor_gnd'].post_process_grounded_object_detection(
        outputs=outputs,
        input_ids=inputs.input_ids,
        target_sizes=[(height, width)]
    )

    if not results or len(results[0]['boxes']) == 0:
        print(" [Detect & Crop] ⚠ Warning: Grounding DINO did not detect the object. Using full image.")
        return image
    result = results[0]
    scores = result['scores']
    max_idx = int(torch.argmax(scores))
    box = result['boxes'][max_idx].cpu().numpy().astype(int)
    print(f" [Detect & Crop] ✅ Object detected with confidence: {scores[max_idx]:.2f}, Box: {box}")
    x1, y1, x2, y2 = box
    models['predictor'].set_image(image_np)
    box_prompt = np.array([[x1, y1, x2, y2]])
    masks, _, _ = models['predictor'].predict(box=box_prompt, multimask_output=False)
    mask = masks[0]
    mask_bool = mask > 0
    cropped_img_rgba = np.zeros((height, width, 4), dtype=np.uint8)
    cropped_img_rgba[:, :, :3] = image_np
    cropped_img_rgba[:, :, 3] = mask_bool * 255
    cropped_img_rgba = cropped_img_rgba[y1:y2, x1:x2]
    return Image.fromarray(cropped_img_rgba, 'RGBA')

def extract_features(segmented_image: Image.Image) -> dict:
    image_rgba = np.array(segmented_image)
    if image_rgba.shape[2] != 4: raise ValueError("Segmented image must be RGBA")
    b, g, r, a = cv2.split(image_rgba)
    image_rgb = cv2.merge((b, g, r))
    mask = a
    gray = cv2.cvtColor(image_rgb, cv2.COLOR_BGR2GRAY)
    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    if not contours:
        # If no contours are found, return zero-filled features
        print(" [Features] ⚠ Warning: No contours found in segmented image. Returning zero features.")
        return {
            "shape_features": [0.0] * 7,
            "color_features": [0.0] * 512, # 8*8*8
            "texture_features": [0.0] * 26
        }
    hu_moments = cv2.HuMoments(cv2.moments(contours[0])).flatten()
    color_hist = cv2.calcHist([image_rgb], [0, 1, 2], mask, [8, 8, 8], [0, 256, 0, 256, 0, 256])
    cv2.normalize(color_hist, color_hist)
    color_hist = color_hist.flatten()
    gray_masked = cv2.bitwise_and(gray, gray, mask=mask)
    lbp = feature.local_binary_pattern(gray_masked, P=24, R=3, method="uniform")
    (texture_hist, _) = np.histogram(lbp.ravel(), bins=np.arange(0, 27), range=(0, 26))
    texture_hist = texture_hist.astype("float32")
    texture_hist /= (texture_hist.sum() + 1e-6)
    return {
        "shape_features": hu_moments.tolist(),
        "color_features": color_hist.tolist(),
        "texture_features": texture_hist.tolist()
    }

def get_text_embedding(text: str, models: dict) -> list:
    print(f" [Embedding] Generating text embedding for: '{text[:50]}...'")
    text_with_instruction = f"Represent this description of a lost item for similarity search: {text}"
    inputs = models['tokenizer_text'](text_with_instruction, return_tensors='pt', padding=True, truncation=True, max_length=512).to(models['device'])
    with torch.no_grad():
        outputs = models['model_text'](**inputs)
        embedding = outputs.last_hidden_state[:, 0, :]
        embedding = torch.nn.functional.normalize(embedding, p=2, dim=1)
    print(" [Embedding] ✅ Text embedding generated.")
    return embedding.cpu().numpy()[0].tolist()

def cosine_similarity(vec1: np.ndarray, vec2: np.ndarray) -> float:
    return float(np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2)))

def stretch_image_score(score):
    if score < 0.4 or score == 1.0: return score
    return 0.7 + (score - 0.4) * (0.99 - 0.7) / (1.0 - 0.4)