Add application file

app.py

@@ -0,0 +1,80 @@
+import streamlit as st
+import torch
+import cv2
+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 = 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 = Image.open("pred.jpg")
+    col2.image(pred, use_column_width=True)
+    
+
+
+
+
+
+
+
+
+  

requirements.txt

@@ -0,0 +1,4 @@
+torch
+torchvision
+opencv-python
+albumentations