Create app.py

app.py

@@ -0,0 +1,59 @@
+from object_detector import DocumentObjects
+import cv2
+import gradio as gr
+import uuid,os
+
+detector = DocumentObjects()
+print('Detector ready')
+
+def showStamp(path,bounding_boxes,output_path=None):
+    image = cv2.imread(path)
+    for box in bounding_boxes:
+        x1, y1, x2, y2 = map(int, box)
+        cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
+        cv2.putText(image, 'stamp', (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
+
+    if output_path:
+        input_img = Image.fromarray(image)
+        input_img.save(output_path)
+    else:
+        return image
+
+def inference(input_img):
+
+    input_img = Image.fromarray(input_img)
+    file_id = str(uuid.uuid4())
+    file_name = f'Img_{file_id}.png'
+    input_img.save(file_name)
+
+    resp = detector.detect_objects(file_name)
+    found = resp['stamp']['found']
+    coordinates = resp['stamp']['loc']
+    annotated_img = showStamp(file_name,coordinates)
+    os.remove(file_name)
+
+    if found:
+        return annotated_img, 'Stamp found'
+    else:
+        return annotated_img, 'Stamp not found'
+
+title = "Stamp Detection usong YOLOV9"
+description = "A simple Gradio interface to infer on Yolo model trained on custom data for stamp detection"
+examples = [
+    'example1.png',
+    'example2.jpeg'
+]
+demo = gr.Interface(
+    inference,
+    inputs = [
+        gr.Image(label="Input Image"),
+    ],
+    outputs = [
+        gr.Image(label="Output Image"),
+        gr.Label()
+    ],
+    title = title,
+    description = description,
+    examples = examples,
+)
+demo.launch(debug=True)