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app.py

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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)

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6c4b614dd7c3d31f93433a971217e7f9cbff05fbd2b1db3cf12f530ce529689a
+size 22539865

requirements.txt

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+streamlit
+opencv-python-headless
+numpy
+Pillow
+torch
+torchvision
+ultralytics