data354/Palm_counting

1import streamlit as st2import torch3import torchvision4import cv25import numpy as np6import torch.nn as nn7from torchvision.ops import box_iou8from PIL import Image9import albumentations as A10from albumentations.pytorch import ToTensorV211 12# apply nms algorithm13def apply_nms(orig_prediction, iou_thresh=0.3):14        # torchvision returns the indices of the bboxes to keep15        keep = torchvision.ops.nms(orig_prediction['boxes'], orig_prediction['scores'], iou_thresh)16        final_prediction = orig_prediction17        final_prediction['boxes'] = final_prediction['boxes'][keep]18        final_prediction['scores'] = final_prediction['scores'][keep]19        final_prediction['labels'] = final_prediction['labels'][keep]20 21        return final_prediction22 23# Draw the bounding box24def plot_img_bbox(img, target):25        h,w,c = img.shape26        for box in (target['boxes']):27            xmin, ymin, xmax, ymax  = int((box[0].cpu()/1024)*w), int((box[1].cpu()/1024)*h), int((box[2].cpu()/1024)*w),int((box[3].cpu()/1024)*h)28            cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 0, 255), 2)29            label = "palm"30            # Add the label and confidence score31            label = f'{label}'32            cv2.putText(img, label, (xmin, ymin - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)33 34        # Display the image with detections35        filename = 'pred.jpg'36        cv2.imwrite(filename, img)37 38# transform image39test_transforms = A.Compose([40        A.Resize(height=1024, width=1024, always_apply=True),41        A.Normalize(always_apply=True),42        ToTensorV2(always_apply=True),])43 44# select device (whether GPU or CPU)45device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')46 47# model loading48model = torch.load('pickel.pth',map_location=torch.device('cpu'))49model = model.to(device)50 51st.title("🌴Palm trees detection🌴")52 53file_name = st.file_uploader("Upload oil palm tree image")54 55if file_name is not None:56    col1, col2 = st.columns(2)57 58    image = np.array(Image.open(file_name))59    col1.image(image, use_column_width=True)60    transformed = test_transforms(image= image)61    image_transformed = transformed["image"]62    image_transformed = image_transformed.unsqueeze(0)63    image_transformed = image_transformed.to(device)64    # inference65    model.eval()66    with torch.no_grad():67        predictions = model(image_transformed)[0]68 69    nms_prediction = apply_nms(predictions, iou_thresh=0.1)70 71    plot_img_bbox(image, nms_prediction)72    pred = np.array(Image.open("pred.jpg"))73    col2.image(pred, use_column_width=True)74    word = "Number of palm trees detected : "+str(len(nms_prediction["boxes"]))75    st.write(word)76    77 78 79 80 81 82 83 84 85