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| import gradio as gr | |
| import torch | |
| import cv2 | |
| import os | |
| import torch.nn as nn | |
| import numpy as np | |
| import torchvision | |
| from torchvision.ops import box_iou | |
| from PIL import Image | |
| import albumentations as A | |
| from albumentations.pytorch import ToTensorV2 | |
| from timeit import default_timer as timer | |
| from typing import Tuple, Dict | |
| # 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) | |
| def predict(img) -> Tuple[Dict, float]: | |
| # Start a timer | |
| start_time = timer() | |
| # Transform the target image and add a batch dimension | |
| #image_transformed = test_transforms() | |
| transformed = test_transforms(image= np.array(img)) | |
| 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(img, nms_prediction) | |
| pred = np.array(Image.open("pred.jpg")) | |
| # Calculate the prediction time | |
| pred_time = round(timer() - start_time, 5) | |
| # Return the prediction dictionary and prediction time | |
| return pred,pred_time | |
| ### 4. Gradio app ### | |
| # Create title, description and article strings | |
| title = "🌴Palm trees detection🌴" | |
| description = "Faster r-cnn model to detect oil palm trees in drones images." | |
| article = "Created by data354." | |
| # Create examples list from "examples/" directory | |
| example_list = [["examples/" + example] for example in os.listdir("examples")] | |
| # Create the Gradio demo | |
| demo = gr.Interface(fn=predict, # mapping function from input to output | |
| inputs=gr.Image(type="pil"), # what are the inputs? | |
| outputs=[gr.Label(label="Predictions"), # what are the outputs? | |
| gr.Number(label="Prediction time (s)")], # our fn has two outputs, therefore we have two outputs | |
| # Create examples list from "examples/" directory | |
| examples=example_list, | |
| title=title, | |
| description=description, | |
| article=article | |
| ) | |
| # Launch the demo! | |
| demo.launch(debug = False) |