Image_Segmentation.py

import os
HOME = os.getcwd()
import ultralytics
from ultralytics import YOLO
import matplotlib.pyplot as plt

import cv2
import numpy as np
import matplotlib.pyplot as plt
from skimage.transform import resize
from skimage.filters import threshold_otsu
from PIL import Image
import pdb
import torchvision.transforms as T

def load_yolo_model(weights_path, image_size=(512, 512)):
    model = YOLO(weights_path)
    model.img_size = image_size
    return model

def detect_object(model, image_path, confidence=0.128):
    results = list(model(image_path, conf=confidence))
    return results[0] if results else None

def preprocess_mask(mask, target_size=(512, 512)):
    mask_pil = T.ToPILImage()(mask)
    mask_np = np.array(mask_pil)
    resized_mask = resize(mask_np, target_size, mode='constant')
    return resized_mask

def generate_binary_mask(mask, target_size=(512, 512)):
    resized_mask = cv2.resize(mask, target_size)
    # Check if the mask has two channels
    print(resized_mask.shape)
    if len(resized_mask.shape) == 3:
        # Convert the mask to grayscale
        gray_mask = resized_mask[:, :, 0]
    else:
        gray_mask = resized_mask
    
    _, binary_mask = cv2.threshold(np.uint8(gray_mask * 255), 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
    binary_mask_np = np.array(binary_mask) / 255
    return binary_mask_np

def overlay_mask_on_image(binary_mask, image_path):
    image = cv2.imread(image_path)
    image_np = np.array(image)
    binary_mask_rgb = binary_mask.astype(image_np.dtype)
    binary_mask_rgb = np.repeat(binary_mask_rgb[:, :, np.newaxis], 3, axis=2)
    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    overlayed_image = binary_mask_rgb * image_rgb
    pil_image = Image.fromarray(overlayed_image.astype(np.uint8))
    return pil_image

def save_overlayed_image(image_array, output_path):
    image_array.save(output_path)

##Import dataset from Roboflow Universe

# from roboflow import Roboflow
# rf = Roboflow(api_key="UaKV836fheN9wUixlhVq")
# project = rf.workspace("lab-ply6t").project("plant-phenotypes")
# dataset = project.version(1).download("yolov8")

from ultralytics import YOLO
train = False

if train:
    model = YOLO("yolov8s-seg.pt")
    results = model.train(
            batch=8,
            device="cpu",
            data="C:/Users/aksha/Peeples_Lab/AgriLife_Data_Analysis-main/Segmentation/datasets/data.yaml",
            epochs=10,
            imgsz=640,
        )

    #model validation
    results = model.val()

        # # # Create a figure with multiple subplots
        # fig, axes = plt.subplots(1, 3, figsize=(15, 5))  # 1 row, 3 columns

        # # Display the red band in the first subplot
        # im0 = axes[0].imshow(green.squeeze())
        # axes[0].set_title('Green Band')
        # divider0 = make_axes_locatable(axes[0])
        # cax0 = divider0.append_axes("right", size="5%", pad=0.05)
        # fig.colorbar(im0, cax=cax0)
        # axes[0].set_xticks([])
        # axes[0].set_yticks([])

        # # Display the green band in the second subplot
        # im1 = axes[1].imshow(red_edge.squeeze())
        # axes[1].set_title('Red_edge Band')
        # divider1 = make_axes_locatable(axes[1])
        # cax1 = divider1.append_axes("right", size="5%", pad=0.05)
        # fig.colorbar(im1, cax=cax1)  # Add color bar to the second subplot
        # axes[1].set_xticks([])
        # axes[1].set_yticks([])

        # # Display the red_edge band in the third subplot
        # im2 = axes[2].imshow(red.squeeze())
        # axes[2].set_title('Red Band')
        # divider2 = make_axes_locatable(axes[2])
        # cax2 = divider2.append_axes("right", size="5%", pad=0.05)
        # fig.colorbar(im2, cax=cax2)  # Add color bar to the third subplot
        # axes[2].set_xticks([])
        # axes[2].set_yticks([])

        # # Adjust spacing between subplots
        # plt.tight_layout()

        # # Display the plot
        # plt.show()