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| import streamlit as st | |
| import torch | |
| import torchvision | |
| import cv2 | |
| import numpy as np | |
| import torch.nn as nn | |
| from torchvision.ops import box_iou | |
| from PIL import Image | |
| import albumentations as A | |
| from albumentations.pytorch import ToTensorV2 | |
| # apply nms algorithm | |
| def apply_nms(orig_prediction, iou_thresh=0.3): | |
| # torchvision returns the indices of the bboxes to keep | |
| keep = torchvision.ops.nms(orig_prediction['boxes'], orig_prediction['scores'], iou_thresh) | |
| final_prediction = orig_prediction | |
| final_prediction['boxes'] = final_prediction['boxes'][keep] | |
| final_prediction['scores'] = final_prediction['scores'][keep] | |
| final_prediction['labels'] = final_prediction['labels'][keep] | |
| return final_prediction | |
| # Draw the bounding box | |
| def plot_img_bbox(img, target): | |
| for box in (target['boxes']): | |
| xmin, ymin, xmax, ymax = int(box[0].cpu()), int(box[1].cpu()), int(box[2].cpu()),int(box[3].cpu()) | |
| cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 0, 255), 2) | |
| label = "palm" | |
| # Add the label and confidence score | |
| label = f'{label}' | |
| cv2.putText(img, label, (xmin, ymin - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2) | |
| # Display the image with detections | |
| filename = 'pred.jpg' | |
| cv2.imwrite(filename, img) | |
| # transform image | |
| test_transforms = A.Compose([ | |
| A.Resize(height=1024, width=1024, always_apply=True), | |
| A.Normalize(always_apply=True), | |
| ToTensorV2(always_apply=True),]) | |
| # select device (whether GPU or CPU) | |
| device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') | |
| # model loading | |
| model = torch.load('pickel.pth',map_location=torch.device('cpu')) | |
| model = model.to(device) | |
| st.title("Palm trees detection") | |
| file_name = st.file_uploader("Upload oil palm tree image") | |
| if file_name is not None: | |
| col1, col2 = st.columns(2) | |
| image = np.array(Image.open(file_name)) | |
| col1.image(image, use_column_width=True) | |
| transformed = test_transforms(image= image) | |
| image_transformed = transformed["image"] | |
| image_transformed = image_transformed.unsqueeze(0) | |
| image_transformed = image_transformed.to(device) | |
| # inference | |
| model.eval() | |
| with torch.no_grad(): | |
| predictions = model(image_transformed)[0] | |
| nms_prediction = apply_nms(predictions, iou_thresh=0.1) | |
| plot_img_bbox(image, nms_prediction) | |
| pred = np.array(Image.open("pred.jpg")) | |
| col2.image(pred, use_column_width=True) | |