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app.py

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+# 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("models/detector-model.pt") 
+CLASSIFICATION_MODEL = YOLO("models/classifier-model.pt") # 0: block, 1: checked, 2: unchecked
+
+def detect(image_path):
+    """
+    Output inference image with bounding box
+
+    Args:
+    - image: to check for checkboxes
+
+    Return: image with bounding boxes drawn 
+    """
+    image = cv2.imread(image_path)
+    if image is None:
+        return image
+    
+    # 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()

classifier-model.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f4a0af5bf0ccd1ca422775a744a80b0916ffb0a6427828b5caaa4bd954e462b4
+size 10377603

detector-model.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9c7c3b6f1858e045cec68db2fd3b2c28c6d67fa937ed34cf2a20231dc3ea2e8e
+size 87617854

requirements.txt

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+ultralytics
+opencv-python