Create app.py

app.py

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+import streamlit as st
+import torch
+import torchvision
+import torchvision.transforms as transforms
+from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
+from torchvision.transforms import ToTensor
+from PIL import Image, ImageDraw
+import cv2
+import numpy as np
+import pandas as pd
+import os
+
+import tempfile
+from tempfile import NamedTemporaryFile
+
+# Create an FRCNN model instance with the same structure as the saved model
+model = torchvision.models.detection.fasterrcnn_resnet50_fpn(num_classes=91)
+
+# Load the saved parameters into the model
+model.load_state_dict(torch.load("frcnn_model.pth"))
+
+# Define the classes for object detection
+classes = [
+            '__background__', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
+            'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'N/A', 'stop sign',
+            'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
+            'elephant', 'bear', 'zebra', 'giraffe', 'N/A', 'backpack', 'umbrella', 'N/A',
+            'N/A', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard',
+            'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard',
+            'surfboard', 'tennis racket', 'bottle', 'N/A', 'wine glass', 'cup', 'fork',
+            'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange',
+            'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
+            'potted plant', 'bed', 'N/A', 'dining table', 'N/A', 'N/A', 'toilet', 'N/A',
+            'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave',
+            'oven', 'toaster', 'sink', 'refrigerator', 'N/A', 'book', 'clock', 'vase',
+            'scissors', 'teddy bear', 'hair drier', 'toothbrush'
+          ]
+
+# Set the threshold for object detection. It is IoU (Intersection over Union)
+threshold = 0.5
+
+st.title(""" Image Object Detections """)
+
+# st.subheader("Prediction of Object Detection")
+
+st.write(""" The Faster R-CNN (Region-based Convolutional Neural Network) is a cutting-edge object detection model that combines deep 
+             learning with region proposal networks to achieve highly accurate object detection in images. 
+             It is trained on a large dataset of images and can detect a wide range of objects with high precision and recall. 
+             The model is based on the ResNet-50 architecture, which allows it to capture complex visual features from the input image. 
+             It uses a two-stage approach, first proposing regions of interest (RoIs) in the image and then classifying and refining the 
+             object boundaries within these RoIs. This approach makes it extremely efficient and accurate in detecting multiple objects 
+             in a single image.
+         """)
+
+
+# images = ["test2.jpg","img7.jpg","img20.jpg","img23.jpg"]
+# with st.sidebar:
+#     st.write("Choose an image")
+#     selected_image = st.selectbox("Select an image", images)
+
+
+images = ["test2.jpg","img7.jpg","img20.jpg","img23.jpg"]
+with st.sidebar:
+    st.write("Choose an image")
+    st.image(images)
+    
+
+# define the function to perform object detection on an image
+def detect_objects(image_path):
+    # load the image
+    image = Image.open(image_path).convert('RGB')
+    
+    # convert the image to a tensor
+    image_tensor = ToTensor()(image).to(device)
+    
+    # run the image through the model to get the predictions
+    model.eval()
+    with torch.no_grad():
+        predictions = model([image_tensor])
+
+    # filter out the predictions below the threshold
+    scores = predictions[0]['scores'].cpu().numpy()
+    boxes = predictions[0]['boxes'].cpu().numpy()
+    labels = predictions[0]['labels'].cpu().numpy()
+    mask = scores > threshold
+    scores = scores[mask]
+    boxes = boxes[mask]
+    labels = labels[mask]
+
+    # create a new image with the predicted objects outlined in rectangles
+    draw = ImageDraw.Draw(image)
+    for box, label in zip(boxes, labels):
+        
+        # draw the rectangle around the object
+        draw.rectangle([(box[0], box[1]), (box[2], box[3])], outline='red')
+        
+        # write the object class above the rectangle
+        class_name = classes[label]
+        draw.text((box[0], box[1]), class_name, fill='yellow')
+    
+    # show the image
+    st.write("Obects detected in the image are: ")
+    # st.image(image, use_column_width=True)
+    st.image.show()
+
+
+file = st.file_uploader('Upload an Image', type=(["jpeg", "jpg", "png"]))
+
+if file is None:
+    st.write("Please upload an image file")
+else:
+    image = Image.open(file)
+    st.write("Input Image")
+    st.image(image, use_column_width=True)
+    with NamedTemporaryFile(dir='.', suffix='.jpeg') as f:  # this line gives error and only accepts .jpeg and so used above snippet
+        f.write(file.getbuffer())                           # which will accepts all formats of images.
+        # your_function_which_takes_a_path(f.name)
+        detect_objects(f.name)
+
+# if file is None:
+#     st.write("Please upload an image file")
+# else:
+#     image = Image.open(file)
+#     st.write("Input Image")
+#     st.image(image, use_column_width=True)
+#     with NamedTemporaryFile(dir='.', suffix='.' + file.name.split('.')[-1]) as f:
+#         f.write(file.getbuffer())
+#         # your_function_which_takes_a_path(f.name)
+#         detect_objects(f.name)
+
+
+st.write(""" This Streamlit app provides a user-friendly interface for uploading an image and visualizing the output of the Faster R-CNN 
+             model. It displays the uploaded image along with the predicted objects highlighted with bounding box overlays. The app allows
+             users to explore the detected objects in the image, providing valuable insights and understanding of the model's predictions. 
+             It can be used for a wide range of applications, such as object recognition, image analysis, and visual storytelling. 
+             Whether it's identifying objects in real-world images or understanding the capabilities of state-of-the-art object detection
+             models, this Streamlit app powered by Faster R-CNN is a powerful tool for computer vision tasks.
+        """)