{"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":"code","source":"","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# *for kaggle learners*","metadata":{}},{"cell_type":"markdown","source":"![ Write anything over here ](https://e7.pngegg.com/pngimages/41/950/png-clipart-smiley-emoji-emoticon-study-skills-smiley-miscellaneous-smiley.png) ![]()","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom PIL import Image","metadata":{"execution":{"iopub.status.busy":"2022-08-17T13:52:12.544588Z","iopub.execute_input":"2022-08-17T13:52:12.545033Z","iopub.status.idle":"2022-08-17T13:52:12.549881Z","shell.execute_reply.started":"2022-08-17T13:52:12.544996Z","shell.execute_reply":"2022-08-17T13:52:12.548968Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# open slide\n****\n![](data:image/png;base64,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) ![]()\n\n****\n*Openslide lib provides a simple interface for reading whole-slide images, which are high-resolution images used in digital pathology.*\n\n","metadata":{}},{"cell_type":"markdown","source":"# *import the openslide library*","metadata":{}},{"cell_type":"code","source":"import openslide  #import the openslide library","metadata":{"execution":{"iopub.status.busy":"2022-08-17T13:52:13.136053Z","iopub.execute_input":"2022-08-17T13:52:13.136652Z","iopub.status.idle":"2022-08-17T13:52:13.240451Z","shell.execute_reply.started":"2022-08-17T13:52:13.136618Z","shell.execute_reply":"2022-08-17T13:52:13.239463Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"'''create an openslide object'''\nslide=openslide.OpenSlide('../input/mayo-clinic-strip-ai/other/065053_0.tif')","metadata":{"execution":{"iopub.status.busy":"2022-08-17T13:52:13.281515Z","iopub.execute_input":"2022-08-17T13:52:13.282623Z","iopub.status.idle":"2022-08-17T13:52:13.320523Z","shell.execute_reply.started":"2022-08-17T13:52:13.282566Z","shell.execute_reply":"2022-08-17T13:52:13.319301Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"'''Want to know the properties'''\nprint(slide.properties)","metadata":{"execution":{"iopub.status.busy":"2022-08-17T13:52:13.739929Z","iopub.execute_input":"2022-08-17T13:52:13.740341Z","iopub.status.idle":"2022-08-17T13:52:13.74684Z","shell.execute_reply.started":"2022-08-17T13:52:13.740308Z","shell.execute_reply":"2022-08-17T13:52:13.745409Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print('slide.dimensions--',slide.dimensions)  \n        #A (width, height) tuple for level 0 of the slide.\nprint('slide.detectformat--',slide.detect_format) \n        #detects the format of the given image\nprint('slide.level_count--',slide.level_count)\n        # The number of levels in the slide.\n        #Levels are numbered from 0 (highest resolution) \n        #to level_count - 1 (lowest resolution).","metadata":{"execution":{"iopub.status.busy":"2022-08-17T13:52:15.114105Z","iopub.execute_input":"2022-08-17T13:52:15.115304Z","iopub.status.idle":"2022-08-17T13:52:15.122937Z","shell.execute_reply.started":"2022-08-17T13:52:15.115261Z","shell.execute_reply":"2022-08-17T13:52:15.1217Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Return an Image containing an RGB thumbnail of the slide\nslidenew=slide.get_thumbnail(size=(1000,1000))\nplt.imshow(slidenew)\n\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-08-17T13:52:15.939771Z","iopub.execute_input":"2022-08-17T13:52:15.941605Z","iopub.status.idle":"2022-08-17T13:54:50.821706Z","shell.execute_reply.started":"2022-08-17T13:52:15.941558Z","shell.execute_reply":"2022-08-17T13:54:50.820197Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.figure(figsize=(100, 100))\nplt.imshow(slidenew)\n","metadata":{"execution":{"iopub.status.busy":"2022-08-17T13:54:50.824256Z","iopub.execute_input":"2022-08-17T13:54:50.824752Z","iopub.status.idle":"2022-08-17T13:54:52.349234Z","shell.execute_reply.started":"2022-08-17T13:54:50.824713Z","shell.execute_reply":"2022-08-17T13:54:52.347946Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**level_dimensions**\n=\n**A list of (pixels_x, pixels_y) tuples for each Deep Zoom level. level_dimensions[k] are the dimensions of level k** ","metadata":{}},{"cell_type":"markdown","source":"![ level dimensions ](data:image/jpeg;base64,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) ![]()\n\n**imagine first layer as the image with full resolution**\n**And the last layer as the image with least resolution**","metadata":{}},{"cell_type":"code","source":"dims=slide.level_dimensions\nprint('no of levels =',len(dims))\n#by how much the levels are downsampled\n\ndims, slide.level_downsamples # this image contain only one level","metadata":{"execution":{"iopub.status.busy":"2022-08-17T13:54:52.350737Z","iopub.execute_input":"2022-08-17T13:54:52.351547Z","iopub.status.idle":"2022-08-17T13:54:52.361485Z","shell.execute_reply.started":"2022-08-17T13:54:52.351501Z","shell.execute_reply":"2022-08-17T13:54:52.360071Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# DeepZoomGenerator\n****\nOpenSlide Python provides functionality for generating individual Deep Zoom tiles from slide objects. This is useful for displaying whole-slide images in a web browser without converting the entire slide to Deep Zoom or a similar format.","metadata":{}},{"cell_type":"code","source":"from openslide.deepzoom import DeepZoomGenerator\ntiles=DeepZoomGenerator(slide, tile_size=256, overlap=0, limit_bounds=False)","metadata":{"execution":{"iopub.status.busy":"2022-08-17T13:54:52.364665Z","iopub.execute_input":"2022-08-17T13:54:52.365166Z","iopub.status.idle":"2022-08-17T13:54:52.373245Z","shell.execute_reply.started":"2022-08-17T13:54:52.36513Z","shell.execute_reply":"2022-08-17T13:54:52.372149Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"*class openslide.deepzoom.DeepZoomGenerator(osr, tile_size=254, overlap=1, limit_bounds=False)\n\n-->osr – the slide object\n\n-->tile_size (int) – the width and height of a single tile. For best viewer performance, tile_size + 2 * overlap should be a power of two.\n\n-->overlap (int) – the number of extra pixels to add to each interior edge of a tile\n\n-->limit_bounds (bool) – True to render only the non-empty slide region*","metadata":{}},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"tiles.tile_count","metadata":{"execution":{"iopub.status.busy":"2022-08-17T13:54:52.374576Z","iopub.execute_input":"2022-08-17T13:54:52.375436Z","iopub.status.idle":"2022-08-17T13:54:52.385907Z","shell.execute_reply.started":"2022-08-17T13:54:52.375386Z","shell.execute_reply":"2022-08-17T13:54:52.384919Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"tiles.level_count","metadata":{"execution":{"iopub.status.busy":"2022-08-17T13:54:52.38707Z","iopub.execute_input":"2022-08-17T13:54:52.387879Z","iopub.status.idle":"2022-08-17T13:54:52.397985Z","shell.execute_reply.started":"2022-08-17T13:54:52.387796Z","shell.execute_reply":"2022-08-17T13:54:52.396925Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dims=tiles.level_dimensions\nprint('no of levels =',len(dims))","metadata":{"execution":{"iopub.status.busy":"2022-08-17T13:54:52.399207Z","iopub.execute_input":"2022-08-17T13:54:52.399993Z","iopub.status.idle":"2022-08-17T13:54:52.408824Z","shell.execute_reply.started":"2022-08-17T13:54:52.399958Z","shell.execute_reply":"2022-08-17T13:54:52.40762Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"col, rows= tiles.level_tiles[15]\n'''returns the no. of rows and columns of tiles in the 15th layer'''","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"col, rows= tiles.level_tiles[15]\nfor c in range(col):\n    for r in range(rows):\n        tile1=tiles.get_tile(15,(c, r))\n        plt.figure(figsize=(5,5))\n        plt.imshow(tile1)\n        break\n        \n    break\n        ","metadata":{"execution":{"iopub.status.busy":"2022-08-17T14:01:11.628177Z","iopub.execute_input":"2022-08-17T14:01:11.628952Z","iopub.status.idle":"2022-08-17T14:01:11.914395Z","shell.execute_reply.started":"2022-08-17T14:01:11.628906Z","shell.execute_reply":"2022-08-17T14:01:11.913201Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **upvote if it is useful**","metadata":{}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}