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import os
import streamlit as st
import cv2
import numpy as np
from ultralytics import YOLO
from PIL import Image
import tempfile
# Directly set the path for the model
MODEL_PATH = 'best.pt'
# Initialize YOLO model with custom trained weights
model = YOLO(MODEL_PATH)
def detect_rhino_image(image):
image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
results = model(image)[0]
for box in results.boxes.data.tolist():
x1, y1, x2, y2, score, class_id = box
if score > 0.5:
cv2.rectangle(image, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 4)
cv2.putText(image, results.names[int(class_id)].upper(), (int(x1), int(y1 - 10)), cv2.FONT_HERSHEY_SIMPLEX, 1.3, (0, 255, 0), 3, cv2.LINE_AA)
return image
def detect_rhino_video(video_file):
cap = cv2.VideoCapture(video_file.name)
ret, frame = cap.read()
H, W, _ = frame.shape
out = cv2.VideoWriter(video_file.name + '_output.mp4', cv2.VideoWriter_fourcc(*'MP4V'), int(cap.get(cv2.CAP_PROP_FPS)), (W, H))
while ret:
results = model(frame)[0]
for box in results.boxes.data.tolist():
x1, y1, x2, y2, score, class_id = box
if score > 0.5:
cv2.rectangle(frame, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 4)
cv2.putText(frame, results.names[int(class_id)].upper(), (int(x1), int(y1 - 10)), cv2.FONT_HERSHEY_SIMPLEX, 1.3, (0, 255, 0), 3, cv2.LINE_AA)
out.write(frame)
ret, frame = cap.read()
cap.release()
out.release()
cv2.destroyAllWindows()
return video_file.name + '_output.mp4'
st.title('Rhinoceros Detection App')
st.write("Upload an image or video of rhinoceroses for detection.")
file = st.file_uploader("Choose a file...", type=["jpg", "jpeg", "png", "mp4"])
if file is not None:
if file.type.split('/')[0] == 'image':
image = Image.open(file)
st.image(detect_rhino_image(image), caption='Processed Image', use_column_width=True)
elif file.type.split('/')[0] == 'video':
tfile = tempfile.NamedTemporaryFile(delete=False)
tfile.write(file.read())
processed_video = detect_rhino_video(tfile)
st.video(processed_video) |