app.py

import streamlit as st
from PIL import Image
import cv2
from ultralytics import YOLO


with st.sidebar:
    st.title("Control panel")
    file = st.file_uploader("Choose an image or a video", type=["png", "jpg", "jpeg", "mp4"])
    radio_button1 = st.radio("Model", ["model_train_17", "model_train_15"])
    radio_button2=st.radio("Visualize",["No","Yes"])

st.header("Palm Tree Detection") 
st.write(
    '<p style="font-family: Arial, sans-serif; font-size: px; color: black; font-style: italic;">Counting the number of palm and coconut trees</p>',
    unsafe_allow_html=True
)

status_placeholder = st.empty()
if radio_button1 == "model_train_17":
    model = YOLO('train_17_best.pt')
elif radio_button1 == "model_train_15":
    model = YOLO('train_15_best.pt')
    



def count_objects(results, class_names):
    """Count objects detected for each class."""
    class_counts = {name: 0 for name in class_names.values()} 
    for box in results[0].boxes:
        cls_idx = int(box.cls[0]) 
        class_name = class_names.get(cls_idx, None) 

        if class_name:  
            class_counts[class_name] += 1
        else:
            st.warning(f"Unknown class index detected: {cls_idx}")  
    return class_counts


def run_inference(file):
    file_type = file.type.split('/')[0]

    if file_type == 'image':
        image = Image.open(file)
        st.image(image, caption="Uploaded Image", use_container_width=True)
        status_placeholder.write("Processing...Please wait....")
        results = model.predict(source=image, save=False)

        class_names = model.names 
        counts = count_objects(results, class_names)
        st.write("Detected objects:")
        for obj, count in counts.items():
            st.write(f"{obj}: {count}")
        status_placeholder.empty()
            
        if(radio_button2=="Yes"):  
            status_placeholder.write("Processing...")
            st.image(results[0].plot(), caption="Detected Objects", use_container_width=True)
            status_placeholder.empty()

        

    # elif file_type == 'video':
    #     temp_file = f"temp_{file.name}"
    #     compressed_file = f"compressed_{file.name}"

    #     # Save the uploaded video to a temporary file
    #     with open(temp_file, "wb") as f:
    #         f.write(file.getbuffer())

    #     # Compress the video
    #     st.write("Compressing video...")
    #     clip = VideoFileClip(temp_file)
    #     clip.write_videofile(compressed_file, codec="libx264", audio_codec="aac")
    #     clip.close()
    #     st.write("Compression complete. Processing video...")

    #     # Process the compressed video
    #     cap = cv2.VideoCapture(compressed_file)
    #     stframe = st.empty()
    #     total_counts = {name: 0 for name in model.names}

    #     while cap.isOpened():
    #         ret, frame = cap.read()
    #         if not ret:
    #             break

    #         # Perform inference on each video frame
    #         results = model.predict(source=frame, save=False)

    #         # Count the objects in the frame
    #         frame_counts = {model.names[int(box.cls[0])]: 0 for box in results[0].boxes}
    #         for box in results[0].boxes:
    #             class_name = model.names[int(box.cls[0])]
    #             frame_counts[class_name] += 1
    #         for obj, count in frame_counts.items():
    #             total_counts[obj] += count

    #         # Display the processed video frame
    #         stframe.image(results[0].plot(), channels="BGR", use_container_width=True)

    #     cap.release()
    #     st.write("Video processing complete.")

    #     # Display total counts
    #     st.write("Total detected objects in the video:")
    #     for obj, count in total_counts.items():
    #         st.write(f"{obj}: {count}")



if file is not None:
    run_inference(file)