Update app.py

app.py

@@ -5,9 +5,6 @@ from typing import Union, List
 from pathlib import Path
 from PIL import Image
 import torch
-from ultralytics.utils.plotting import save_one_box
-from ultralytics.engine.results import Results
-from ultralytics import YOLOWorld
 
 # Function to resize images
 
@@ -36,90 +33,15 @@ def stitch_images(image_paths, scale_percent=50):
         print(f"Stitching failed with status code: {status}")
         return None
 
-# YOLO detection function
-
-def yolo_detect(
-    image: Union[str, Path, int, Image.Image, list, tuple, np.ndarray, torch.Tensor],
-    classes: List[str],
-) -> np.ndarray:
-    detector = YOLOWorld("yolov8x-worldv2.pt")
-    detector.set_classes(classes)
-    results: List[Results] = detector.predict(image)
-
-    boxes = []
-    for result in results:
-        boxes.append(
-            save_one_box(result.cpu().boxes.xyxy, im=result.orig_img, save=False)
-        )
-
-    del detector
-    return boxes[0]
-
-# Function to predict and process image
-
-def predict(image, offset_inches):
-    try:
-        drawer_img = yolo_detect(image, ["box"])
-        shrunked_img = make_square(shrink_bbox(drawer_img, 0.8))
-        return shrunked_img
-    except:
-        raise Exception("Unable to DETECT DRAWER, please take another picture with different magnification level!")
-
-# Function to shrink bounding box
-
-def shrink_bbox(image: np.ndarray, shrink_factor: float):
-    height, width = image.shape[:2]
-    center_x, center_y = width // 2, height // 2
-
-    new_width = int(width * shrink_factor)
-    new_height = int(height * shrink_factor)
-
-    x1 = max(center_x - new_width // 2, 0)
-    y1 = max(center_y - new_height // 2, 0)
-    x2 = min(center_x + new_width // 2, width)
-    y2 = min(center_y + new_height // 2, height)
-
-    cropped_image = image[y1:y2, x1:x2]
-    return cropped_image
-
-# Function to make image square
-
-def make_square(img: np.ndarray):
-    height, width = img.shape[:2]
-    max_dim = max(height, width)
-    pad_height = (max_dim - height) // 2
-    pad_width = (max_dim - width) // 2
-    pad_height_extra = max_dim - height - 2 * pad_height
-    pad_width_extra = max_dim - width - 2 * pad_width
-
-    if len(img.shape) == 3:
-        padded = np.pad(
-            img,
-            ((pad_height, pad_height + pad_height_extra),
-             (pad_width, pad_width + pad_width_extra),
-             (0, 0)),
-            mode="edge"
-        )
-    else:
-        padded = np.pad(
-            img,
-            ((pad_height, pad_height + pad_height_extra),
-             (pad_width, pad_width + pad_width_extra)),
-            mode="edge"
-        )
-
-    return padded
-
 # Main image processing function
 
-def process_image(image_paths, scale_percent=50, offset_inches=1):
+def process_image(image_paths, scale_percent=50):
     stitched_image = stitch_images(image_paths, scale_percent)
 
     if stitched_image is not None:
         try:
             stitched_image_rgb = cv2.cvtColor(stitched_image, cv2.COLOR_BGR2RGB)
-            final_image = predict(stitched_image_rgb, offset_inches)
-            return final_image
+            return stitched_image_rgb
         except Exception as e:
             print(str(e))
             return stitched_image