Update pipeline/logic.py

pipeline/logic.py

@@ -1,17 +1,13 @@
-from PIL import ImageFile
-ImageFile.LOAD_TRUNCATED_IMAGES = True
-
 import numpy as np
 import requests
 import cv2
 from skimage import feature
 from io import BytesIO
-from PIL import Image, ImageFile
+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]
@@ -19,7 +15,6 @@ def get_canonical_label(object_name_phrase: str) -> str:
     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)
@@ -29,112 +24,74 @@ def download_image_from_url(image_url: str) -> Image.Image:
     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'])
-
+    inputs = models['processor_gnd'](images=image, text=prompt, return_tensors="pt").to(models['device'])
     with torch.no_grad():
         outputs = models['model_gnd'](**inputs)
-        results = models['processor_gnd'].post_process_grounded_object_detection(
-            outputs,
-            inputs.input_ids,
-            box_threshold=0.4,
-            text_threshold=0.3,
-            target_sizes=[(height, width)]
-        )
-
+    results = models['processor_gnd'].post_process_grounded_object_detection(
+        outputs, inputs.input_ids, box_threshold=0.4, text_threshold=0.3, 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")
-
+    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)
     hu_moments = cv2.HuMoments(cv2.moments(contours[0])).flatten() if contours else np.zeros(7)
-
-    color_hist = cv2.calcHist([image_rgb], [0, 1, 2], mask, [8, 8, 8],
-                               [0, 256, 0, 256, 0, 256])
+    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 sentence for searching relevant passages: {text}"
-
-    inputs = models['tokenizer_text'](
-        text_with_instruction,
-        return_tensors='pt',
-        padding=True,
-        truncation=True,
-        max_length=512
-    ).to(models['device'])
-
+    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)
+    if score < 0.4 or score == 1.0: return score
+    return 0.7 + (score - 0.4) * (0.99 - 0.7) / (1.0 - 0.4)