{"cells":[{"metadata":{},"cell_type":"markdown","source":"![tissue_logo.001.jpeg](attachment:tissue_logo.001.jpeg)\n\nTissue Detection is a key aspect to research in the domain of computer vision applied to cancer classification. My main focus in this competition so far has been exploring previous work done in this domain and furthering its application towards this dataset. Notebooks in this collection include the following.\n* [Base Notebook **(Currently Here)**](https://www.kaggle.com/dannellyz/panda-tissue-detection-size-optimization-70) : Tissue Detection Intro and First Application\n* [Base Dataset Generation](https://www.kaggle.com/dannellyz/tissue-detect-td-conv-png-512x512): Notebook to export images to zip file\n* [Scaling Bounding Boxes](https://www.kaggle.com/dannellyz/tissue-detect-scaling-bounding-boxes-4xfaster): 4x speed increase to base notebook\n* [Tissue Dection Metadata Analysis](https://www.kaggle.com/dannellyz/tissue-detection-bounding-box-metadata-eda-viz/): Exploring features from bounding boxes discovery on the slides","attachments":{"tissue_logo.001.jpeg":{"image/jpeg":"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"}},"execution_count":null},{"metadata":{},"cell_type":"markdown","source":"# Tissue Detection and Size Optimization\n\n## Objective:\nThis notebook leverages academic research and previous repositories in pathology to idnetiify regions of interst (ROIs) in provided slides and optimally crop the full image to contain a the maximum amount of tissue informaiton while minimizing the overall size. This can be used as a preprocessing step in order to remove large swaths of the images that do not conatin meningful data for prediciton. It also greatly improves speed and memory capacity.\n\n## Results:\nI have [another notebook](https://www.kaggle.com/dannellyz/tissue-detect-png-512x512-pre-process/) that takes the lessons from this process and directly applies them to the data corpus as pre-processing. Additionally I have made the [pre-processed images](https://www.kaggle.com/dannellyz/panda-preprocessing-tissue-detection/) available as well.","execution_count":null},{"metadata":{},"cell_type":"markdown","source":"# Getting Strated with the Numpy Images\n\n## Basic Slide Downsample\nDownsampling is a process to take the Whole Slide Image (WSI) and get lower lever representations of the slide. The higher the downsampling rate the less resolution the image will have. to start this walk through we use a very high downsampling in order to prove the concept and at the end with a low downsampling to show the effectiveness.\n![Downsample Example](http://dicom.nema.org/Dicom/DICOMWSI/sup145_fromword_files/image010.gif)\n\n## As Numpy Array\nOnce the image has been downsampled there are many ways it could be represented as best described in the Notebook [Getting Started with the PANDA Dataset -> Using Matplotlib](https://www.kaggle.com/wouterbulten/getting-started-with-the-panda-dataset#Visualizing-masks-(using-matplotlib)). For this notebook we will be working with the slides as Numpy Arrays. As the below examples illustrate the choosen downsample has a big imapct on the size of the representation","execution_count":null},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"#All imports\n%matplotlib inline\nimport matplotlib.pyplot as plt\nplt.rcParams['figure.figsize'] = [15, 5]\nimport numpy\nimport pandas as pd\nimport numpy as np\nimport cv2\nfrom skimage import morphology\nimport openslide\n\n#Setup code\nslide_dir = \"../input/prostate-cancer-grade-assessment/train_images/\"\nannotation_dir = \"../input/prostate-cancer-grade-assessment/train_label_masks/\"\ntrain_data_df = pd.read_csv(\"../input/prostate-cancer-grade-assessment/train.csv\")\nsample_id_list = list(train_data_df[\"image_id\"].sample(5))\nsample_slides = [f\"{slide_dir}{slide_id}.tiff\" for slide_id in sample_id_list]\nsample_annotations = [f\"{annotation_dir}{slide_id}_mask.tiff\" for slide_id in sample_id_list]\n\ndef get_disk_size(numpy_image):\n    \"\"\" Returns size in MB of numpy array on disk.\"\"\"\n    return (numpy_image.size * numpy_image.itemsize) / 1000000\n\ndef plot_figures(figures, nrows = 1, ncols=1):\n    #https://stackoverflow.com/a/11172032\n    \"\"\"Plot a dictionary of figures.\n\n    Parameters\n    ----------\n    figures : <title, figure> dictionary\n    ncols : number of columns of subplots wanted in the display\n    nrows : number of rows of subplots wanted in the figure\n    \"\"\"\n\n    fig, axeslist = plt.subplots(ncols=ncols, nrows=nrows)\n    for ind,title in enumerate(figures):\n        axeslist.ravel()[ind].imshow(figures[title], aspect='auto')\n        axeslist.ravel()[ind].set_title(title)\n    plt.tight_layout()\n    return plt\n\nprint(\"^ All imports and setup code in above hidden code block. ^\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"def downsample(wsi, downsampling_factor=16):\n    #Select the min downsampling factor \n    #Between the input value and the available values from the slide\n    downsampling_factor = min(wsi.level_downsamples, \n                              key=lambda x: abs(x - downsampling_factor))\n    \n    #Set the level of the slide by the downsampling\n    level = wsi.level_downsamples.index(downsampling_factor)\n    \n    #Read and convert to numpy array\n    slide = wsi.read_region((0, 0), level, wsi.level_dimensions[level])\n    numpy_slide = np.array(slide)[:, :, :3]\n    \n    return numpy_slide\n\n#Set up example slide\nexample_id = \"037504061b9fba71ef6e24c48c6df44d\"\nexample_slide = f\"{slide_dir}{example_id}.tiff\"\n\n#Open slide as wsi\nwsi = openslide.open_slide(example_slide)\n\ndef display_downsample(factor):\n    display_slide = downsample(wsi, factor)\n    return (f\"Factor:\\n{factor}\\nSize (MB):\\n{get_disk_size(display_slide)}\",display_slide)\n\npotential_lvls = wsi.level_downsamples\ndownsample_results = [display_downsample(factor) for factor in potential_lvls]\ndownsample_dict = {k:v for k,v in downsample_results}\nplt = plot_figures(downsample_dict, 1, len(downsample_dict))\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Detecting Tissue\n\nThis notebook draws on the work of [Luís](https://github.com/luisvalesilva) and his [WSIPRE](https://github.com/luisvalesilva/wsipre) work available on Github.\n\n## Leveraging Academic Research\n\nPreviously pubilshed work in the domain of [Deep Learning for Identifying Metastatic Breast Cancer](https://arxiv.org/pdf/1606.05718.pdf) lends itself nicely to the detection on Prostate Cells in the PANDA challenge esepcially in the area of pre-processing. An excerpt from the paper explaining the methodology: \n\n> To reduce computation time and to focus our analysis on regions of the slide most likely to contain cancer metastasis, we first identify tissue within the WSI and exclude background white space. To achieve this, we adopt a threshold based segmentation method to automatically detect the background region. In particular, we first transfer the original image from the RGB color space to the HSV color space, then the optimal threshold values in each channel are computed using the [Otsu algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4310076), and the final mask images are generated by combining the masks from H and S channels.\n\nThis work informs the method `def detect_tissue`:\n\n    Find RoIs containing tissue in WSI.\n    Generate mask locating tissue in an WSI. Inspired by method used by\n    Wang et al. [1]_.\n    .. [1] Dayong Wang, Aditya Khosla, Rishab Gargeya, Humayun Irshad, Andrew\n    H. Beck, \"Deep Learning for Identifying Metastatic Breast Cancer\",\n    arXiv:1606.05718\n    \n    Parameters\n    ----------\n    wsi: OpenSlide/AnnotatedOpenSlide class instance\n        The whole-slide image (WSI) to detect tissue in.\n    downsampling_factor: int\n        The desired factor to downsample the image by, since full WSIs will\n        not fit in memory. The image's closest level downsample is found\n        and used.\n    sensitivity: int\n        The desired sensitivty of the model to detect tissue. The baseline is set\n        at 5000 and should be adjusted down to capture more potential issue and\n        adjusted up to be more agressive with trimming the slide.\n        \n    Returns\n    -------\n    -Binary mask as numpy 2D array, \n    -RGB slide image (in the used downsampling level, in case the user is visualizing output examples),\n    -Downsampling factor.\n    \nThe method `def draw_tissue_polygons`, from WSIPRE, allows for vizualizaiton of this deteciton:\n\n    Parameters\n    ----------\n    wsi: OpenSlide/AnnotatedOpenSlide class instance\n        The whole-slide image (WSI) to detect tissue in.\n    downsampling_factor: int\n        The desired factor to downsample the image by, since full WSIs will\n        not fit in memory. The image's closest level downsample is found\n        and used.\n    sensitivity: int\n        The desired sensitivty of the model to detect tissue. The baseline is set\n        at 5000 and should be adjusted down to capture more potential issue and\n        adjusted up to be more agressive with trimming the slide.\n    Returns\n    -------\n    Binary mask as numpy 2D array, RGB slide image (in the used\n    downsampling level, in case the user is visualizing output examples)\n    and downsampling factor.\n\n","execution_count":null},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"def otsu_filter(channel, gaussian_blur=True):\n    \"\"\"Otsu filter.\"\"\"\n    if gaussian_blur:\n        channel = cv2.GaussianBlur(channel, (5, 5), 0)\n    channel = channel.reshape((channel.shape[0], channel.shape[1]))\n\n    return cv2.threshold(\n        channel, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]\n\ndef detect_tissue(wsi, sensitivity = 3000, downsampling_factor=64):\n    \n    \"\"\"\n    Find RoIs containing tissue in WSI.\n    Generate mask locating tissue in an WSI. Inspired by method used by\n    Wang et al. [1]_.\n    .. [1] Dayong Wang, Aditya Khosla, Rishab Gargeya, Humayun Irshad, Andrew\n    H. Beck, \"Deep Learning for Identifying Metastatic Breast Cancer\",\n    arXiv:1606.05718\n    \n    Parameters\n    ----------\n    wsi: OpenSlide/AnnotatedOpenSlide class instance\n        The whole-slide image (WSI) to detect tissue in.\n    downsampling_factor: int\n        The desired factor to downsample the image by, since full WSIs will\n        not fit in memory. The image's closest level downsample is found\n        and used.\n    sensitivity: int\n        The desired sensitivty of the model to detect tissue. The baseline is set\n        at 5000 and should be adjusted down to capture more potential issue and\n        adjusted up to be more agressive with trimming the slide.\n        \n    Returns\n    -------\n    -Binary mask as numpy 2D array, \n    -RGB slide image (in the used downsampling level, in case the user is visualizing output examples),\n    -Downsampling factor.\n    \"\"\"\n    \n    # Get a downsample of the whole slide image (to fit in memory)\n    downsampling_factor = min(\n        wsi.level_downsamples, key=lambda x: abs(x - downsampling_factor))\n    level = wsi.level_downsamples.index(downsampling_factor)\n\n    slide = wsi.read_region((0, 0), level, wsi.level_dimensions[level])\n    slide = np.array(slide)[:, :, :3]\n\n    # Convert from RGB to HSV color space\n    slide_hsv = cv2.cvtColor(slide, cv2.COLOR_BGR2HSV)\n\n    # Compute optimal threshold values in each channel using Otsu algorithm\n    _, saturation, _ = np.split(slide_hsv, 3, axis=2)\n\n    mask = otsu_filter(saturation, gaussian_blur=True)\n\n    # Make mask boolean\n    mask = mask != 0\n\n    mask = morphology.remove_small_holes(mask, area_threshold=sensitivity)\n    mask = morphology.remove_small_objects(mask, min_size=sensitivity)\n\n    mask = mask.astype(np.uint8)\n    mask_contours, tier = cv2.findContours(\n        mask, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)\n\n    return mask_contours, tier, slide, downsampling_factor\n\ndef draw_tissue_polygons(mask, polygons, polygon_type,\n                              line_thickness=None):\n        \"\"\"\n        Plot as numpy array detected tissue.\n        Modeled WSIPRE github package\n        \n        Parameters\n        ----------\n        mask: numpy array \n            This is the original image represented as 0's for a starting canvas\n        polygons: numpy array \n            These are the identified tissue regions\n        polygon_type: str (\"line\" | \"area\")\n            The desired display type for the tissue regions\n        polygon_type: int\n            If the polygon_type==\"line\" then this parameter sets thickness\n\n        Returns\n        -------\n        Nunmpy array of tissue mask plotted\n        \"\"\"\n        \n        tissue_color = 1\n\n        for poly in polygons:\n            if polygon_type == 'line':\n                mask = cv2.polylines(\n                    mask, [poly], True, tissue_color, line_thickness)\n            elif polygon_type == 'area':\n                if line_thickness is not None:\n                    warnings.warn('\"line_thickness\" is only used if ' +\n                                  '\"polygon_type\" is \"line\".')\n\n                mask = cv2.fillPoly(mask, [poly], tissue_color)\n            else:\n                raise ValueError(\n                    'Accepted \"polygon_type\" values are \"line\" or \"area\".')\n\n        return mask\n    \n#Base Example\ntissue_contours, tier, downsampled_slide, downsampling_factor = detect_tissue(wsi, 3000,64)\nbase_slide_mask = np.zeros(downsampled_slide.shape[:2])\ntissue_slide = draw_tissue_polygons(base_slide_mask, tissue_contours,'line', 2)\nbase_size = get_disk_size(downsampled_slide)\nplt.imshow(tissue_slide)\nplt.show()\nprint(\"^ Code hidden above for Tissue Detection Algorithm. ^\")","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Sensitivity Exploration\nWhen considering what sensitivty to choose we dont want a high enough number to remove noise, but also a low enough number to make sure we capture all of the tissue. Initial research leads to a conclusion of 3000, but I want to do more work on this in next steps.","execution_count":null},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"#Sensitivity Tests\ndef sensitivity_test(sensitivity):\n    \"\"\"Take in a given sensitivity and return tissue_slide\"\"\"\n    tissue_contours, tier, downsampled_slide, downsampling_factor = detect_tissue(wsi, sensitivity,64)\n    base_slide_mask = np.zeros(downsampled_slide.shape[:2])\n    tissue_slide = draw_tissue_polygons(base_slide_mask, tissue_contours,'line', 2)\n    return (f\"Sensitivity:\\n{sensitivity}\",tissue_slide)\n\nto_test = [i for i in range(0,7500,1500)]\nsensitvity_results = [sensitivity_test(sensitivity) for sensitivity in to_test]\nsensitvity_dict = {k:v for k,v in sensitvity_results}\nsensitvity_dict[\"Basic Slide\"] = downsampled_slide\nplt = plot_figures(sensitvity_dict, 1, len(sensitvity_dict))\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Cutting out all space except for the identified Tissue\nOnce we have identified a given sensitivity and the areas it identifies as tissue we can then cut out the rest of the image. As you can see even thought we have \"cut out\" all non-tissue parts of the slide the size has not changed.","execution_count":null},{"metadata":{"trusted":true,"_kg_hide-output":false,"_kg_hide-input":true},"cell_type":"code","source":"def tissue_cutout(tissue_slide, tissue_contours, slide):\n    #https://stackoverflow.com/a/28759496\n    crop_mask = np.zeros_like(tissue_slide) # Create mask where white is what we want, black otherwise\n    cv2.drawContours(crop_mask, tissue_contours, -1, 255, -1) # Draw filled contour in mask\n    tissue_only = np.zeros_like(slide) # Extract out the object and place into output image\n    tissue_only[crop_mask == 255] = slide[crop_mask == 255]\n    return tissue_only\n\ntissue_only_slide = tissue_cutout(tissue_slide, tissue_contours, downsampled_slide)\nplt.imshow(tissue_only_slide)\nplt.show()\n\ncurrent_size = get_disk_size(tissue_only_slide)\ncurrent_pct = current_size / base_size\nprint(f\"Slide Size on Disk: {current_size:.2f}MB\")\nprint(f\"% of original image: {current_pct*100:.2f}%\")","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Getting Bounding Boxes\n\n### Starting with simple boxes\nThe first method experimented with finds a minimum bounding rectangle for all of the identified countors.","execution_count":null},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"min_rect_bounding = tissue_only_slide.copy()\nfor c in tissue_contours:\n    rect = cv2.minAreaRect(c)\n    box = cv2.boxPoints(rect)\n    box = np.int0(box)\n    cv2.drawContours(min_rect_bounding,[box],0,(0,255,255),4)\nplt.imshow(min_rect_bounding)\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Getting Simple Rectangle Bounding Box\nThis example makes a simple rectangle that captures all of the identified countors\n","execution_count":null},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"simple_rect_bound = min_rect_bounding.copy()\nboxes = []\nfor c in tissue_contours:\n    (x, y, w, h) = cv2.boundingRect(c)\n    boxes.append([x,y, x+w,y+h])\n\nboxes = np.asarray(boxes)\nleft = np.min(boxes[:,0])\ntop = np.min(boxes[:,1])\nright = np.max(boxes[:,2])\nbottom = np.max(boxes[:,3])\n\ncv2.rectangle(simple_rect_bound, (left,top), (right,bottom), (255, 0, 0), 4)\n\nplt.imshow(simple_rect_bound)\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Min-Rectagle Bounding Boxes\nWith the goal of minimizing the total area of the images the next method implemented concatenates all tissue contours found and builds a cumulative minimum boudning rectanlge.","execution_count":null},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"smart_bounding_boxes = min_rect_bounding.copy()\nall_bounding_rect = cv2.minAreaRect(np.concatenate(tissue_contours))\nall_bounding_box = cv2.boxPoints(all_bounding_rect)\nall_bounding_box = np.int0(all_bounding_box)\ncv2.drawContours(smart_bounding_boxes,[all_bounding_box],0,(255,0,0),4)\nplt.imshow(smart_bounding_boxes)\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Cropping the Image\n\n## Basic cropping \nNow that ROIs have been indentified we can now crop the image based on the bounding boxes. We start with a simple rectangluar crop","execution_count":null},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"simple_crop = smart_bounding_boxes.copy()\ncrop_mask = np.zeros_like(simple_crop)\n(y, x) = np.where(tissue_slide == 1)\n(topy, topx) = (np.min(y), np.min(x))\n(bottomy, bottomx) = (np.max(y), np.max(x))\nsimple_crop = simple_crop[topy:bottomy+1, topx:bottomx+1]\nplt.imshow(simple_crop)\nplt.show()\n\ncurrent_size = get_disk_size(simple_crop)\ncurrent_pct = current_size / base_size\nprint(f\"Slide Size on Disk: {current_size:.2f}MB\")\nprint(f\"% of original image: {current_pct*100:.2f}%\")","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Crop with Smart Bounding and Rotation to Minimum Size\nTo better reduce size we take the Smart Bounding Box we calulated and rotate the image to minimize the size while keeping everything within the bounding box. Since Prostate Biopsies are not orientated in a specific way this does not effect prediciton.","execution_count":null},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"def getSubImage(rect, src_img):\n    width = int(rect[1][0])\n    height = int(rect[1][1])\n    box = cv2.boxPoints(rect)\n\n    src_pts = box.astype(\"float32\")\n    dst_pts = np.array([[0, height-1],\n                        [0, 0],\n                        [width-1, 0],\n                        [width-1, height-1]], dtype=\"float32\")\n    M = cv2.getPerspectiveTransform(src_pts, dst_pts)\n    warped = cv2.warpPerspective(src_img, M, (width, height))\n    return warped\n\nsmart_bounding_crop = smart_bounding_boxes.copy()\nsmart_bounding_crop = getSubImage(all_bounding_rect,smart_bounding_crop)\nplt.imshow(smart_bounding_crop)\nplt.show()\n\ncurrent_size = get_disk_size(smart_bounding_crop)\ncurrent_pct = current_size / base_size\nprint(f\"Slide Size on Disk: {current_size:.2f}MB\")\nprint(f\"% of original image: {current_pct*100:.2f}%\")","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Remove space between countors in final bounding box\nNow that we have everything but the minimum bounding boxes we can cut any blank space between the inner most areas of the contours. For visuals the bounding boxes were left, but in the full pipeline below they are removed.","execution_count":null},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"row_not_blank =  [row.all() for row in ~np.all(smart_bounding_crop == [255,   0,   0],axis=1)]\ncol_not_blank =  [col.all() for col in ~np.all(smart_bounding_crop == [255,   0,   0],axis=0)]\nbounded_cut = smart_bounding_crop[row_not_blank,:]\nbounded_cut = bounded_cut[:,col_not_blank]\nplt.imshow(bounded_cut)\nplt.show()\n\ncurrent_size = get_disk_size(bounded_cut)\ncurrent_pct = current_size / base_size\nprint(f\"Slide Size on Disk: {current_size:.2f}MB\")\nprint(f\"% of original image: {current_pct*100:.2f}%\")","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Whole Pipeline: Start Here for Code Only\nNow that we have explained each of the steps along the way we can put the whole pipeline together.","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"def detect_and_crop(image_location:str, sensitivity:int=3000, \n                    downsample_rate:int=16, show_plots:str=\"simple\"):\n    \n    #Set-up dictionary for plotting\n    verbose_plots = {}\n    \n    #Open Slide\n    wsi = openslide.open_slide(image_location)\n    \n    #Get returns from detect_tissue()\n    (tissue_contours, tier, \n     downsampled_slide, \n     downsampling_factor) = detect_tissue(wsi,\n                                          sensitivity,downsample_rate)\n    #Add Base Slide to verbose print\n    verbose_plots[f\"Base Slide\\n{get_disk_size(downsampled_slide):.2f}MB\"] = downsampled_slide\n    \n    #Get Tissue Only Slide\n    base_slide_mask = np.zeros(downsampled_slide.shape[:2])\n    tissue_slide = draw_tissue_polygons(base_slide_mask, tissue_contours,'line', 5)\n    base_size = get_disk_size(downsampled_slide)\n    tissue_only_slide = tissue_cutout(tissue_slide, tissue_contours, downsampled_slide)\n    #Add Tissue Only to verbose print\n    verbose_plots[f\"Tissue Detect\\nNo Change\"] = tissue_slide\n    \n    #Get minimal bounding rectangle for all tissue contours\n    if len(tissue_contours) == 0:\n        img_id = image_location.split(\"/\")[-1]\n        print(f\"No Tissue Contours - ID: {img_id}\")\n        return None, 1.0\n    \n    all_bounding_rect = cv2.minAreaRect(np.concatenate(tissue_contours))\n    #Crop with getSubImage()\n    smart_bounding_crop = getSubImage(all_bounding_rect,tissue_only_slide)\n    #Add Bounding Boxes to verbose print\n    verbose_plots[f\"Bounding Boxes\\n{get_disk_size(smart_bounding_crop):.2f}MB\"] = smart_bounding_crop\n\n    #Crop empty space\n    #Remove by row\n    row_not_blank =  [row.all() for row in ~np.all(smart_bounding_crop == [255,0,0],\n                                                   axis=1)]\n    space_cut = smart_bounding_crop[row_not_blank,:]\n    #Remove by column\n    col_not_blank =  [col.all() for col in ~np.all(smart_bounding_crop == [255,0,0],\n                                                   axis=0)]\n    space_cut = space_cut[:,col_not_blank]\n    #Add Space Cut Boxes to verbose print\n    verbose_plots[f\"Space Cut\\n{get_disk_size(space_cut):.2f}MB\"] = space_cut\n    \n    #Get size change\n    start_size = get_disk_size(downsampled_slide)\n    final_size = get_disk_size(space_cut)\n    pct_change = final_size / start_size\n    \n    if show_plots == \"simple\":\n        print(f\"Percent Reduced from Base Slide to Final: {(1- pct_change)*100:.2f}\")\n        plt.imshow(space_cut)\n        plt.show() \n    elif show_plots == \"verbose\":\n        print(f\"Percent Reduced from Base Slide to Final: {(1- pct_change)*100:.2f}\")\n        plt = plot_figures(verbose_plots, 1, len(verbose_plots))\n        plt.show()\n    elif show_plots == \"none\":\n        pass\n    else:\n        pass\n    return space_cut, (1-pct_change)\n    \nnumpy_result, pct_change = detect_and_crop(image_location=example_slide, show_plots=\"verbose\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for sample in sample_slides:\n    detect_and_crop(image_location=sample, show_plots=\"verbose\")","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Baseline for Corpus: 67.8%\nHere we try to get a average for the reduciton capacity of this code across the data corpus. We do this by sampling from the whole and taking an average of the recoded size reduciton. I have run the sampler over the whole corpus and come out with 68.95%. For speed of commiting the notebook,  I have it set to only 5% in this notebook. ","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"from statistics import mean \nfrom multiprocessing import Pool\nfrom tqdm.notebook import tqdm\nimport gc\nbaseline_pct = .05\nbaseline_count = int(baseline_pct*len(train_data_df))\nbaseline_slide_ids = list(train_data_df[\"image_id\"].sample(baseline_count))\nbaseline_slide_locs = [f\"{slide_dir}{slide_id}.tiff\" for slide_id in baseline_slide_ids]\n#Nested in funciton to not take up more memory and allow mulitprocessing\ndef baseline_check(image_id):\n    numpy_result, pct_change = detect_and_crop(image_location=image_id, show_plots=\"none\")\n    del numpy_result\n    gc.collect()\n    return pct_change\n\nwith Pool(processes=4) as pool:\n    avg_pct_reduced = list(\n        tqdm(pool.imap(baseline_check, baseline_slide_locs), total = len(baseline_slide_locs))\n    )\n\nprint(f\"The averge size reduced reduced from a {baseline_pct:.0%} sample of slides is {mean(avg_pct_reduced):.2%}\")","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Next Steps\n1. Exploring how this methodology effects the mask slides\n2. Timing the processing and making it more efficient\n3. Checking Effectiveness on lower downsamples","execution_count":null}],"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":4,"nbformat_minor":4}