Update utils.py

utils.py

@@ -1,20 +1,15 @@
 from PIL import Image
-from transformers import BlipProcessor, BlipForConditionalGeneration
-import torch
 from config import Config
 import cv2
 import numpy as np
 import math
 
-# Simple global caching for the captioner
-captioner_processor = None
-captioner_model = None
 
 def resize_image_to_1mp(image):
     """Resizes image to approx 1MP (e.g., 1024x1024) preserving aspect ratio."""
     image = image.convert("RGB")
     w, h = image.size
-    target_pixels = 1024 * 1024
+    target_pixels = 768 * 768
     aspect_ratio = w / h
     
     # Calculate new dimensions
@@ -22,56 +17,10 @@ def resize_image_to_1mp(image):
     new_w = int(new_h * aspect_ratio)
     
     # Ensure divisibility by 48 for efficiency
-    new_w = (new_w // 48) * 48
-    new_h = (new_h // 48) * 48
+    new_w = (new_w // 64) * 64
+    new_h = (new_h // 64) * 64
     
     if new_w == 0 or new_h == 0:
-        new_w, new_h = 1024, 1024 # Fallback
+        new_w, new_h = 768, 768  # Fallback
         
-    return image.resize((new_w, new_h), Image.LANCZOS)
-
-def get_caption(image):
-    """Generates a caption for the image if one isn't provided."""
-    global captioner_processor, captioner_model
-    
-    if captioner_model is None:
-        print("Loading Captioner (BLIP)...")
-        captioner_processor = BlipProcessor.from_pretrained(Config.CAPTIONER_REPO)
-        captioner_model = BlipForConditionalGeneration.from_pretrained(Config.CAPTIONER_REPO).to(Config.DEVICE)
-
-    inputs = captioner_processor(image, return_tensors="pt").to(Config.DEVICE)
-    out = captioner_model.generate(**inputs)
-    caption = captioner_processor.decode(out[0], skip_special_tokens=True)
-    return caption
-
-# --- ADDED: Function from your provided file ---
-def draw_kps(image_pil, kps, color_list=[(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0), (255, 0, 255)]):
-    stickwidth = 4
-    limbSeq = np.array([[0, 2], [1, 2], [3, 2], [4, 2]])
-    kps = np.array(kps)
-
-    w, h = image_pil.size
-    out_img = np.zeros([h, w, 3])
-
-    for i in range(len(limbSeq)):
-        index = limbSeq[i]
-        color = color_list[index[0]]
-
-        x = kps[index][:, 0]
-        y = kps[index][:, 1]
-        length = ((x[0] - x[1]) ** 2 + (y[0] - y[1]) ** 2) ** 0.5
-        angle = math.degrees(math.atan2(y[0] - y[1], x[0] - x[1]))
-        polygon = cv2.ellipse2Poly(
-            (int(np.mean(x)), int(np.mean(y))), (int(length / 2), stickwidth), int(angle), 0, 360, 1
-        )
-        out_img = cv2.fillConvexPoly(out_img.copy(), polygon, color)
-    out_img = (out_img * 0.6).astype(np.uint8)
-
-    for idx_kp, kp in enumerate(kps):
-        color = color_list[idx_kp]
-        x, y = kp
-        out_img = cv2.circle(out_img.copy(), (int(x), int(y)), 10, color, -1)
-
-    out_img_pil = Image.fromarray(out_img.astype(np.uint8))
-    return out_img_pil
-# --- END ADDED ---
+    return image.resize((new_w, new_h), Image.LANCZOS)