Create app.py

app.py

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+import cv2
+import numpy as np
+import gradio as gr
+import requests
+from io import BytesIO
+
+# Configuration files
+config_file = "ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt"
+frozen_model = "frozen_inference_graph.pb"
+
+# Load model and set it to use the GPU
+model = cv2.dnn.DetectionModel(frozen_model, config_file)
+model.setInputSize(320, 320)
+model.setInputScale(1.0 / 127.5)
+model.setInputMean((127.5, 127.5, 127.5))
+model.setInputSwapRB(True)
+model.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)
+model.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)
+
+# Load class labels
+classLabels = []
+with open('labels.txt', 'rt') as f:
+    classLabels = f.read().rstrip('\n').split('\n')
+
+def detect_objects(frame):
+    """
+    Detect objects in a single frame and return their coordinates and names.
+    :param frame: Input image/frame
+    :return: List of detected objects with coordinates and names
+    """
+    detections = []
+    
+    # Detect objects in the frame
+    ClassIndex, confidence, bbox = model.detect(frame, confThreshold=0.55)
+    
+    if len(ClassIndex) != 0:
+        for ClassInd, conf, boxes in zip(ClassIndex.flatten(), confidence.flatten(), bbox):
+            if ClassInd <= 80:
+                x, y, w, h = boxes
+                detected_object = {
+                    "name": classLabels[ClassInd - 1],
+                    "coordinates": {
+                        "x": int(x),
+                        "y": int(y),
+                        "width": int(w),
+                        "height": int(h)
+                    }
+                }
+                detections.append(detected_object)
+                
+                # Draw bounding box and label on the frame
+                cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
+                cv2.putText(frame, classLabels[ClassInd - 1].upper(), (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
+                
+    return frame, detections
+
+def get_image_from_url(url):
+    response = requests.get(url)
+    image = np.asarray(bytearray(response.content), dtype="uint8")
+    image = cv2.imdecode(image, cv2.IMREAD_COLOR)
+    return image
+
+def detect_objects_in_image_url(url):
+    frame = get_image_from_url(url)
+    result_frame, detected_objects = detect_objects(frame)
+    return result_frame, detected_objects
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=detect_objects_in_image_url,
+    inputs="text",
+    outputs=[gr.Image(type="numpy"), gr.JSON()],
+    title="Object Detection",
+    description="Enter an image URL to detect objects. The bounding boxes and labels will be drawn on the image, and the detected objects will be returned as JSON."
+)
+
+# Launch the interface
+iface.launch()