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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) |