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app.py

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+import gradio as gr
+import torch
+import cv2
+import os
+import torch.nn as nn
+import numpy as np
+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(np.array(img))
+    
+    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)

requirements.txt

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+torch
+torchvision
+opencv-python
+numpy
+albumentations