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import gradio as gr
from ultralytics import YOLO
import cv2
examples=[["photo/a.png","Image1"],["photo/b.png","Image2"],
["photo/c.png","Image3"],["photo/d.png","Image4"],
["photo/e.png","Image5"],["photo/f.png","Image6"],
["photo/g.png","Image7"],["photo/h.png","Image8"]]
def detect_objects_on_image(image_path):
image = cv2.imread(image_path)
model = YOLO("best.pt")
results = model.predict(image_path)
result = results[0]
output = []
class_names_mapping = {
"DPHF": "Double Person Helmet",
"DPNH": "Double Person No Helmet",
"SPHF": "Single Person Helmet",
"SPNH": "Single Person No Helmet",
"NP": "Number Plate"
}
# Add more space around the text
text_padding = 10
for box in result.boxes:
x1, y1, x2, y2 = [round(x) for x in box.xyxy[0].tolist()]
class_id = box.cls[0].item()
prob = round(box.conf[0].item(), 2)
class_name = class_names_mapping.get(result.names[class_id], result.names[class_id])
# Adjust the rectangle coordinates to add more space around the text
x1 -= text_padding
y1 -= text_padding
x2 += text_padding
y2 += text_padding
output.append([
x1, y1, x2, y2, class_name, prob
])
cv2.rectangle(
image,
(x1, y1),
(x2, y2),
color=(0, 0, 255),
thickness=1,
lineType=cv2.LINE_AA
)
cv2.putText(image, class_name, (x1, y1), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (36, 255, 12), 2)
return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
inputs_image = [
gr.components.Image(type="filepath", label="Input Image"),
]
outputs_image = [
gr.components.Image(type="numpy", label="Output Image"),
]
demo = gr.Interface(
fn=detect_objects_on_image,
inputs=inputs_image,
outputs=outputs_image,
title="Biker Helmet and Number Plate Detection",
examples=examples,
cache_examples=False,
)
if __name__ == "__main__":
demo.launch() |