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import cv2
import numpy as np
import gradio as gr
import os
# ✅ Load YOLO model files (Ensure these files are uploaded to the Space)
yolo_config = "yolov3.cfg"
yolo_weights = "yolov3.weights"
yolo_classes = "coco.names"
# ✅ Load class labels
with open(yolo_classes, "r") as f:
classes = [line.strip() for line in f.readlines()]
# ✅ Load YOLO model
net = cv2.dnn.readNet(yolo_weights, yolo_config)
layer_names = net.getLayerNames()
output_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()]
# ✅ Object Detection Function
def detect_objects(image):
img = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) # Convert from RGB to BGR
height, width, _ = img.shape
# Convert image to blob
blob = cv2.dnn.blobFromImage(img, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
net.setInput(blob)
outs = net.forward(output_layers)
# Process detected objects
class_ids, confidences, boxes = [], [], []
for out in outs:
for detection in out:
scores = detection[5:]
class_id = np.argmax(scores)
confidence = scores[class_id]
if confidence > 0.5:
center_x, center_y, w, h = (detection[0:4] * [width, height, width, height]).astype("int")
x = int(center_x - w / 2)
y = int(center_y - h / 2)
boxes.append([x, y, w, h])
confidences.append(float(confidence))
class_ids.append(class_id)
# Non-maximum suppression
indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
colors = np.random.uniform(0, 255, size=(len(classes), 3))
# Draw bounding boxes
for i in indexes.flatten():
x, y, w, h = boxes[i]
label = f"{classes[class_ids[i]]}: {confidences[i]:.2f}"
color = colors[class_ids[i]]
cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)
cv2.putText(img, label, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # Convert back to RGB
return img_rgb
# ✅ Gradio Interface
demo = gr.Interface(
fn=detect_objects,
inputs=gr.Image(type="numpy"),
outputs=gr.Image(type="numpy"),
title="YOLOv3 Object Detection",
description="Upload an image to detect objects using YOLOv3.",
)
# ✅ Launch Gradio App
demo.launch() |