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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) | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| # 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(""" Object Detection Using Faster-RCNN """) | |
| # st.subheader("Prediction of Object Detection") | |
| st.write(""" The Faster R-CNN (Region-based Convolutional Neural Network) is a powerful 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","test1.jpg","img18.jpg","img3.jpg","img15.jpg","img17.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='.') as f: | |
| f.write(file.getbuffer()) | |
| # 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='.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) | |
| 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. | |
| """) | |