Create app.py

app.py

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+import gradio as gr
+import cv2
+import numpy as np
+
+# Function to perform object detection on the input image
+def detect_objects(input_image):
+    # Preprocess the image (if required) and perform object detection
+    # Replace this with your own object detection code
+    processed_image = preprocess_image(input_image)
+    bounding_boxes = perform_object_detection(processed_image)
+    detections = extract_detections(bounding_boxes)
+    return detections
+
+# Helper function to preprocess the input image
+def preprocess_image(image):
+    # Replace this with your own image preprocessing code
+    processed_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    return processed_image
+
+# Helper function to perform object detection on the preprocessed image
+def perform_object_detection(image):
+    # Replace this with your own object detection code
+    # Make sure the bounding boxes are in (xmin, ymin, xmax, ymax) format
+    bounding_boxes = [((100, 100, 300, 300), 'object1'), ((400, 200, 600, 400), 'object2')]
+    return bounding_boxes
+
+# Helper function to extract the detected objects from the bounding boxes
+def extract_detections(bounding_boxes):
+    detections = [{'box': box, 'label': label} for box, label in bounding_boxes]
+    return detections
+
+# Create a function for deleting a detection based on its index
+def delete_detection(index):
+    del detections[index]
+
+# Create a function for saving the detections
+def save_detections():
+    # Implement the saving logic here
+    print("Detections saved.")
+
+# Define the Gradio input and output interfaces
+image_input = gr.inputs.Image()
+bounding_box_list_output = gr.outputs.Label(num_top_classes=0, label_type='list', type='box', box_data=lambda img: img['box'])
+
+# Create the delete button interface
+delete_button = gr.outputs.Button(label="Delete")
+
+# Create the interfaces for saving the detections and deleting the bounding boxes
+save_button = gr.outputs.Button(label="Save Detections")
+delete_button = gr.outputs.Button(label="Delete")
+
+# Create the function for deleting a detection based on button click
+def delete_box(inputs):
+    delete_detection(inputs["delete_button"])
+    return detections
+
+# Create the function for saving the detections based on button click
+def save_boxes():
+    save_detections()
+
+# Combine the inputs, outputs, and functions into a Gradio interface
+interface = gr.Interface(
+    fn=detect_objects,
+    inputs=image_input,
+    outputs=[bounding_box_list_output, delete_button, save_button],
+    title="Object Detection Interface",
+    description="Upload an image and detect objects",
+    allow_flagging=False,
+    theme="default"
+)
+
+# Run the interface
+interface.launch()