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import cv2
import numpy as np
import gradio as gr
import requests
# 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)
return 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)
detected_objects = detect_objects(frame)
return detected_objects
# Define the Gradio interface
iface = gr.Interface(
fn=detect_objects_in_image_url,
inputs="text",
outputs=gr.JSON(),
title="Object Detection",
description="Enter an image URL to detect objects. The detected objects will be returned as JSON."
)
# Launch the interface
iface.launch()
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