app.py

import streamlit as st
import torch,torchvision
import detectron2
import pickle
from detectron2.utils.logger import setup_logger
logger = setup_logger()
from detectron2.engine import DefaultPredictor
from detectron2.projects import point_rend
from viz_app import plot_single_image  # Correct import
from PIL import Image
import numpy as np
from detectron2.data import MetadataCatalog, DatasetCatalog
from detectron2.utils.visualizer import Visualizer
from collections import Counter

# Define configuration parameters
output_directories = {
    
    "4 Classes": "output/pointrend_4_cls_6_nest_8k/",
}

# Function to load the configuration
def load_configuration(output_dir):
    with open(output_dir + "cfg.pickle", "rb") as f:
        return pickle.load(f)

# Function to run instance segmentation
def run_instance_segmentation(im, predictor, num_classes):
    
    outputs = plot_single_image(im, predictor,num_classes)

    masks = outputs['instances'].pred_masks.cpu().numpy()
    classes = outputs['instances'].pred_classes.cpu().numpy()

    if num_classes == 2:
        # Create masks for Nest and Inorganic Material
        nest_masks = masks[classes == 0]
        inorganic_material_masks = masks[classes == 1]
                        
        # Calculate the total pixel area of Nest masks
        total_nest_area = np.sum(nest_masks)
        
        # Calculate the total area of Inorganic Material masks
        total_inorganic_area = np.sum(inorganic_material_masks)

        # Contar la cantidad de máscaras por categoría
        mask_counts = Counter(classes)

    elif num_classes == 4:
            
        # Create masks for Nest and Inorganic Material
        plastic_masks = masks[classes == 0] 
        fishing_net_masks = masks[classes == 1]
        rope_cloth_masks = masks[classes == 2] 
        nest_masks = masks[classes == 3]
                
        
        # Calculate the total pixel area of Nest masks
        total_nest_area = np.sum(nest_masks)
        
        # Calculate the total area of Inorganic Material masks
        plastic_area = np.sum(plastic_masks)
        fishing_net_area = np.sum(fishing_net_masks)
        rope_cloth_area = np.sum(rope_cloth_masks)
        total_inorganic_area = np.sum([plastic_area, fishing_net_area, rope_cloth_area])

        # Contar la cantidad de máscaras por categoría
        mask_counts = Counter(classes)

    return total_nest_area, total_inorganic_area, mask_counts

def click_instance_segmentation(image, model_selection, predictor):
    im = np.array(image)[:, :, ::-1]

    if st.button("Run Instance Segmentation"):
        num_classes = 2 if model_selection == "2 Classes" else 4
        total_nest_area, total_inorganic_area, mask_counts = run_instance_segmentation(im, predictor, num_classes)

        # Calculate the percentage of inorganic material within the Nest
        percentage_inorganic_in_nest = (total_inorganic_area / total_nest_area) * 100

        st.write(f"Percentage of Inorganic Material in Nest: {percentage_inorganic_in_nest:.2f}%")

        category_list = ['Plastic', 'Fishing_Net', 'Rope/Cloth', "Nest"]

        # Mostrar la cantidad de máscaras por categoría
        st.write("Mask Counts by Category:")
        for category, count in mask_counts.items():
            st.write(f"Category {category_list[category]}: {count} masks")
    

def app():
    # Create a sidebar to select the model
    model_selection = st.sidebar.radio("Select Model", list(output_directories.keys()))
    output_dir = output_directories[model_selection]
    cfg = load_configuration(output_dir)

    cfg.MODEL.WEIGHTS = output_dir + "model_best.pth"
    cfg.MODEL.DEVICE = "cpu"
    cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.65
    predictor = DefaultPredictor(cfg)

    default_image_path = 'image/DJI_0228_frame_0022_object_0.jpg'
    st.header('Please upload an image')
    file = st.file_uploader('', type=['png', 'jpg', 'jpeg'])

    if file:
        image = Image.open(file)
        st.image(image, caption="Uploaded Image", use_column_width=True)

        click_instance_segmentation(image, model_selection, predictor)        

    else:
        st.write("No image uploaded. Using default example image.")
        image = Image.open(default_image_path)
        st.image(image, caption="Default Example Image", use_column_width=True)

        click_instance_segmentation(image, model_selection, predictor)

app()