Delete app.py

app.py

@@ -1,95 +0,0 @@
-# Import libraries
-import cv2 # for reading images, draw bounding boxes
-from ultralytics import YOLO 
-import gradio as gr
-
-# Define constants
-BOX_COLORS = {
-    "unchecked": (242, 48, 48),
-    "checked": (38, 115, 101),
-    "block": (242, 159, 5)
-}
-BOX_PADDING = 2
-
-# Load models
-DETECTION_MODEL = YOLO("runs/detect/train_all_classes/best_yolov8l_640_with_background_mixup50.pt") 
-CLASSIFICATION_MODEL = YOLO("runs/classify/train/weights/best.pt") # 0: block, 1: checked, 2: unchecked
-
-def detect(image):
-    """
-    Output inference image with bounding box
-
-    Args:
-    - image: to check for checkboxes
-
-    Return: image with bounding boxes drawn 
-    """
-    # Predict on image
-    results = DETECTION_MODEL.predict(source=image, conf=0.2, iou=0.8) # Predict on image
-    boxes = results[0].boxes # Get bounding boxes
-
-    if len(boxes) == 0:
-        return image
-
-    # Get bounding boxes
-    for box in boxes:
-        detection_class_conf = round(box.conf.item(), 2)
-        detection_class = list(BOX_COLORS)[int(box.cls)]
-        # Get start and end points of the current box
-        start_box = (int(box.xyxy[0][0]), int(box.xyxy[0][1]))
-        end_box = (int(box.xyxy[0][2]), int(box.xyxy[0][3]))
-        box = image[start_box[1]:end_box[1], start_box[0]: end_box[0], :]
-
-        # Determine the class of the box using classification model
-        cls_results = CLASSIFICATION_MODEL.predict(source=box, conf=0.5)
-        probs = cls_results[0].probs  # cls prob, (num_class, )
-        classification_class = list(BOX_COLORS)[2 - int(probs.top1)] 
-        classification_class_conf = round(probs.top1conf.item(), 2)
-
-        cls = classification_class if classification_class_conf > 0.9 else detection_class
-        
-        # 01. DRAW BOUNDING BOX OF OBJECT
-        line_thickness = round(0.002 * (image.shape[0] + image.shape[1]) / 2) + 1
-        image = cv2.rectangle(img=image, 
-                              pt1=start_box, 
-                              pt2=end_box,
-                              color=BOX_COLORS[cls], 
-                              thickness = line_thickness) # Draw the box with predefined colors
-        
-        # 02. DRAW LABEL
-        text = cls + " " + str(detection_class_conf)
-        # Get text dimensions to draw wrapping box
-        font_thickness =  max(line_thickness - 1, 1)
-        (text_w, text_h), _ = cv2.getTextSize(text=text, fontFace=2, fontScale=line_thickness/3, thickness=font_thickness)
-        # Draw wrapping box for text
-        image = cv2.rectangle(img=image,
-                              pt1=(start_box[0], start_box[1] - text_h - BOX_PADDING*2),
-                              pt2=(start_box[0] + text_w + BOX_PADDING * 2, start_box[1]),
-                              color=BOX_COLORS[cls],
-                              thickness=-1)
-        # Put class name on image
-        start_text = (start_box[0] + BOX_PADDING, start_box[1] - BOX_PADDING)
-        image = cv2.putText(img=image, text=text, org=start_text, fontFace=0, color=(255,255,255), fontScale=line_thickness/3, thickness=font_thickness)
-        
-    return image
-
-iface = gr.Interface(fn=detect, 
-                     inputs=gr.inputs.Image(label="Upload scanned document", type="filepath"), 
-                     outputs="image")
-iface.launch()
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