{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Image Segmentation AI Report 2023\n\nHuman vision can easily recognise objects, faces, and perceive light, texture, colour, position, shape in just 13 milliseconds. Computer Vision has a lot to catch up when compared to the granularity, robustness and speed with which humans can perceive vision.\n\nConsider going shopping to a supermarket by walking, which is a frequent task done by many of us. We navigate our way to the market by looking at our surroundings, nearby landmarks and more. We might need to cross the road to reach there, we take note of the different vehicles and people on the road, approximate their speed and cross at an optimal time. We then enter the market and look for aisles that have the objects we need, for example fruit. We identify the fruit we need and choose the fruits that are either big or small, distinguish them based on shape and choose the fruits that have not gone bad and look fresh. A task as basic as shopping is making use of so many aspects of our vision.\n\n![googleAIBlog.gif](https://1.bp.blogspot.com/-evIOqC7twgY/XyHbBfrwVeI/AAAAAAAAGSA/6fO0Q1txQvAkbhTtqc2o_-idvrLS9QnwgCLcBGAsYHQ/s400/image2.gif)\n\nSource: https://ai.googleblog.com/2020/07/on-device-supermarket-product.html","metadata":{}},{"cell_type":"markdown","source":"Computer Vision consists of several tasks like,\n* Image Classification\n* Object Detection\n* Image Segmentation\n* Face Recognition\n* Edge Detection\n* Scene Reconstruction\n* Image Captioning\n* Video Tracking\n\nZooming in on Image Segmentation, while Object Detection can identify and locate objects Image Segmentation provides a more granular understanding of the image. Hence, this task of Computer Vision is key in understanding the input image. \n\n![decseg.png](https://i.ibb.co/z2SFr1b/decseg.png)\n\nSource: http://cs231n.stanford.edu/","metadata":{}},{"cell_type":"markdown","source":"# Introduction\n\nIn simple words, Image Segmentation is the process of segmenting(dividing) an image into various regions. Image Segmentation can be thought of as a “**Per pixel classification**” task. This involves assigning a label to each of the pixels in the image. Hence, the output of an Image Segmentation model is a matrix of size of input images containing labels to relevant classes. Based on how we define these “classes”, we can have different types of Image Segmentation.\n\n**Semantic Segmentation**, is segmenting the image scene into regions of various classes. This type of segmentation cannot distinguish between different instances of the same class. For example, in an image showing a crowd, all the people will be marked the same colour.\n\n**Instance Segmentation** can distinguish between the instances but cannot assign or predict that a particular instance belongs to a given class. Panoptic Segmentation is a mixture of these 2 segmentations.\n\n![segmentationTypes.jpg](https://www.researchgate.net/profile/Vinorth-Varatharasan/publication/339328277/figure/fig1/AS:864554888204291@1583137356688/Semantic-segmentation-left-and-Instance-segmentation-right-8.ppm)\n\nSource: https://www.researchgate.net/publication/339328277_Improving_Learning_Effectiveness_For_Object_Detection_and_Classification_in_Cluttered_Backgrounds\n","metadata":{}},{"cell_type":"markdown","source":"# Process of Image Segmentation\n\nThe process of Image Segmentation can be explained using, state of the art model **DeepLabv3 by Google Research**. DeepLabv3 uses “***Atrous Spatial Pyramid Pooling (ASPP)***” which removes the fixed input size constraint on the CNN by considering various layers of atrous convolutions. Atrous convolutions refer to convolving while skipping some pixels in a broader sense, as the name suggests “atrous” means holes. This type of convolution gives room for a richer feature map and keeps track of the “Field Of View” in every layer. DeepLabv3 is tested on **Pascal VOC 2012** and Cityscape datasets.\n\nThe below code snippets show the execution of DeepLabv3 using resnet101 which is a CNN of 101 layers, on a sample image. This code is taken from the PyTorch documentation and executed in the Kaggle environment.","metadata":{}},{"cell_type":"code","source":"import torch\nmodel = torch.hub.load('pytorch/vision:v0.10.0', 'deeplabv3_resnet101', pretrained=True)\nmodel.eval()\n\nimport urllib\nurl, filename = (\"https://github.com/pytorch/hub/raw/master/images/deeplab1.png\", \"deeplab1.png\")\ntry: urllib.URLopener().retrieve(url, filename)\nexcept: urllib.request.urlretrieve(url, filename)","metadata":{"execution":{"iopub.status.busy":"2023-07-05T05:25:24.896731Z","iopub.execute_input":"2023-07-05T05:25:24.897128Z","iopub.status.idle":"2023-07-05T05:25:27.189526Z","shell.execute_reply.started":"2023-07-05T05:25:24.897097Z","shell.execute_reply":"2023-07-05T05:25:27.188425Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# sample execution (requires torchvision)\nfrom PIL import Image\nfrom torchvision import transforms\ninput_image = Image.open(filename)\ninput_image = input_image.convert(\"RGB\")\npreprocess = transforms.Compose([\n    transforms.ToTensor(),\n    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n])\n\ninput_tensor = preprocess(input_image)\ninput_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model\n\n# move the input and model to GPU for speed if available\nif torch.cuda.is_available():\n    input_batch = input_batch.to('cuda')\n    model.to('cuda')\n\nwith torch.no_grad():\n    output = model(input_batch)['out'][0]\noutput_predictions = output.argmax(0)","metadata":{"execution":{"iopub.status.busy":"2023-07-05T05:25:34.952523Z","iopub.execute_input":"2023-07-05T05:25:34.952906Z","iopub.status.idle":"2023-07-05T05:25:59.884256Z","shell.execute_reply.started":"2023-07-05T05:25:34.952875Z","shell.execute_reply":"2023-07-05T05:25:59.883106Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# create a color pallette, selecting a color for each class\npalette = torch.tensor([2 ** 25 - 1, 2 ** 15 - 1, 2 ** 21 - 1])\ncolors = torch.as_tensor([i for i in range(21)])[:, None] * palette\ncolors = (colors % 255).numpy().astype(\"uint8\")\n\n# plot the semantic segmentation predictions of 21 classes in each color\nr = Image.fromarray(output_predictions.byte().cpu().numpy()).resize(input_image.size)\nr.putpalette(colors)\n\nimport matplotlib.pyplot as plt\nplt.imshow(r)","metadata":{"execution":{"iopub.status.busy":"2023-07-05T05:26:03.194782Z","iopub.execute_input":"2023-07-05T05:26:03.195186Z","iopub.status.idle":"2023-07-05T05:26:03.662422Z","shell.execute_reply.started":"2023-07-05T05:26:03.195153Z","shell.execute_reply":"2023-07-05T05:26:03.661257Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Advancements in Image Segmentation\n\n1.**Segment Anything Model (SAM) by Meta**\n\nThis was released to serve as a foundation segmentation model along with Segment Anything 1-Billion mask dataset (SA-1B), which is a dataset of 11 Million images and 1.1 Billion segmentation masks. Like YOLO-NAS is a foundation model for Object Detection, BERT & GPT are foundation models for NLP, SAM aims to be a foundation model for the Image Segmentation task in Computer Vision.\n\n![sam.png](https://i.ibb.co/W3Rw2cx/sam.png)\n\nSource: https://aibusiness.com/computer-vision/meta-s-sam-could-revolutionize-computer-vision\n\nSAM implements a promptable image segmentation, which is interactive and trained on a broad range of data. A valid segmentation mask is generated for any segmentation prompt. For an image segmentation project, we need custom annotation of images belonging to the dataset and task-specific expertise. These limitations are overcome by SAM through simultaneously developing the model with a powerful generalisation capability and creating a powerful dataset.\n\nSAM promises innovative applications, for example, people wearing AR glasses can be prompted with reminders or description upon spotting an object. \n\n![SAMapplications](https://s11.gifyu.com/images/SQeMi.gif)\n\nSource: https://ai.facebook.com/blog/segment-anything-foundation-model-image-segmentation/\n\n2.**BSANet: Multi-class Parsing with Joint Boundary-Semantic Awareness**\nBSANet model is for multi-object multi-part segmentation which handles the part-level and class-level ambiguities by introducing two modules. Part level ambiguities are handled by a boundary awareness module for part localization (identifying the position and boundary of a part). A semantic awareness module is proposed to deal with the class-level ambiguities which selects the distinguishable features of a given class. BSANet can run on any encoder decoder architecture.\n\n3.**FLOAT framework by IIIT Hyderabad & Google Research**\n\nFLOAT stands for Factorized Learning of Object Attributes for Improved Multi-object Multi-part Scene Parsing. It provides a framework for image segmentation tasks by taking advantage of the ontological relationships in the images like spatial relationship (left, right, front back), relationships based on movement (animate, inanimate). According to the FLOAT framework, the images are segmented in a factorised manner using the relationships and at the object level, and then merged to give the final segment map that can parse multi-object multi-part scenes.The FLOAT framework makes use of DeepLabv3 architecture in its segmentation pipeline. Along with the framework, the Pascal Part 201 dataset is released which is the most challenging version of the Pascal dataset.\n\n![float.png](https://i.ibb.co/F6C5DpZ/float.png)\n\nSource: https://floatseg.github.io/\n\n![image.png](attachment:e2acb489-d5a5-4637-918a-8fbfca31c072.png)\n\nSource: https://floatseg.github.io/\n\nThe mIoU values of GMNet, BSANet and FLOAT are compared in the above table.","metadata":{},"attachments":{"e2acb489-d5a5-4637-918a-8fbfca31c072.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"# Conclusion\nImage Segmentation has various applications in the medical field, autonomous vehicles, image editing, traffic control systems and more. The SAM model and FLOAT framework is discussed in relation to recent advancements in the data available, techniques and methods for performing Image Segmentation. Additionally, the task of Image Segmentation as a task of Computer Vision and related concepts are introduced.","metadata":{}},{"cell_type":"markdown","source":"# References\n\n* https://ai.googleblog.com/2020/07/on-device-supermarket-product.html\n* http://cs231n.stanford.edu/slides/2022/lecture_9_jiajun.pdf\n* https://www.v7labs.com/blog/what-is-computer-vision#h3\n* https://news.mit.edu/2014/in-the-blink-of-an-eye-0116#:~:text=However%2C%20a%20team%20of%20neuroscientists,of%20such%20rapid%20processing%20speed\n* https://www.researchgate.net/publication/339328277_Improving_Learning_Effectiveness_For_Object_Detection_and_Classification_in_Cluttered_Backgrounds\n* https://towardsdatascience.com/review-deeplabv3-atrous-convolution-semantic-segmentation-6d818bfd1d74\n* https://pytorch.org/hub/pytorch_vision_deeplabv3_resnet101/\n* https://ai.facebook.com/blog/segment-anything-foundation-model-image-segmentation/\n* https://arxiv.org/abs/2304.02643\n* https://openaccess.thecvf.com/content/CVPR2022/papers/Singh_FLOAT_Factorized_Learning_of_Object_Attributes_for_Improved_Multi-Object_Multi-Part_CVPR_2022_paper.pdf\n* https://floatseg.github.io/\n","metadata":{}},{"cell_type":"code","source":"import pandas as pd\n\ns = pd.read_csv('/kaggle/input/2023-kaggle-ai-report/sample_submission.csv')\ns.loc[len(s.index)] = ['essay_category', 'Other']\ns.loc[len(s.index)] = ['essay_url', 'https://www.kaggle.com/code/soumyaoruganti/image-segmentation-ai-report-2023']\ns.loc[len(s.index)] = ['feedback1_url', 'https://www.kaggle.com/code/danielhan00/how-ai-sees-what-that-means-for-you-and-me/comments#2347063']\ns.loc[len(s.index)] = ['feedback2_url', 'https://www.kaggle.com/code/megan3/ai-ethics-and-covid-19/comments#2346991']\ns.loc[len(s.index)] = ['feedback3_url', 'https://www.kaggle.com/code/roshinifernando/real-time-object-detection/comments#2346922']\ns = s.loc[5:]\n\n\ns.to_csv('/kaggle/working/submission.csv', index=False)","metadata":{"execution":{"iopub.status.busy":"2023-07-16T18:28:23.443877Z","iopub.execute_input":"2023-07-16T18:28:23.444329Z","iopub.status.idle":"2023-07-16T18:28:23.468621Z","shell.execute_reply.started":"2023-07-16T18:28:23.444296Z","shell.execute_reply":"2023-07-16T18:28:23.467175Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}