From 8560b93a3ea363e910f7dea953809f021e55ec71 Mon Sep 17 00:00:00 2001 From: Aditya Oke Date: Sun, 26 Sep 2021 01:14:19 +0530 Subject: [PATCH 1/7] start writing example --- gallery/plot_repurposing_annotations.py | 53 ++++++++++++++++++++----- 1 file changed, 42 insertions(+), 11 deletions(-) diff --git a/gallery/plot_repurposing_annotations.py b/gallery/plot_repurposing_annotations.py index 2decefcc815..67128059502 100644 --- a/gallery/plot_repurposing_annotations.py +++ b/gallery/plot_repurposing_annotations.py @@ -7,28 +7,41 @@ localization annotations for different tasks (e.g. transforming masks used by instance and panoptic segmentation methods into bounding boxes used by object detection methods). """ -import os.path +import os -import PIL.Image +from PIL import Image import matplotlib.patches -import matplotlib.pyplot -import numpy +import matplotlib.pyplot as plt +import numpy as np import torch +import torchvision.transforms.functional as F from torchvision.ops import masks_to_boxes -ASSETS_DIRECTORY = "../test/assets" +ASSETS_DIRECTORY = "../assets" matplotlib.pyplot.rcParams["savefig.bbox"] = "tight" + +def show(imgs): + if not isinstance(imgs, list): + imgs = [imgs] + fix, axs = plt.subplots(ncols=len(imgs), squeeze=False) + for i, img in enumerate(imgs): + img = img.detach() + img = F.to_pil_image(img) + axs[0, i].imshow(np.asarray(img)) + axs[0, i].set(xticklabels=[], yticklabels=[], xticks=[], yticks=[]) + + #################################### # Masks # ----- # In tasks like instance and panoptic segmentation, masks are commonly defined, and are defined by this package, # as a multi-dimensional array (e.g. a NumPy array or a PyTorch tensor) with the following shape: # -# (objects, height, width) +# (num_objects, height, width) # -# Where objects is the number of annotated objects in the image. Each (height, width) object corresponds to exactly +# Where num_objects is the number of annotated objects in the image. Each (height, width) object corresponds to exactly # one object. For example, if your input image has the dimensions 224 x 224 and has four annotated objects the shape # of your masks annotation has the following shape: # @@ -37,10 +50,14 @@ # A nice property of masks is that they can be easily repurposed to be used in methods to solve a variety of object # localization tasks. # -# Masks to bounding boxes -# ---------------------------------------- +# Converting Segmentation Masks to bounding boxes +# ----------------------------------------------- # For example, the masks to bounding_boxes operation can be used to transform masks into bounding boxes that can be -# used in methods like Faster RCNN and YOLO. +# used as input to detection models such as FasterRCNN and RetinaNet. + +# Let's visualize an image and segmentation masks. We will use the plotting utilities provided by torchvision.utils +# to visualize. The images are taken from PenFudan Dataset. +# with PIL.Image.open(os.path.join(ASSETS_DIRECTORY, "masks.tiff")) as image: masks = torch.zeros((image.n_frames, image.height, image.width), dtype=torch.int) @@ -48,12 +65,18 @@ for index in range(image.n_frames): image.seek(index) - frame = numpy.array(image) + frame = np.array(image) masks[index] = torch.tensor(frame) +# We will use the masks_to_boxes from the torchvision.ops module +# It returns the boxes in (xmin, ymin, xmax, ymax) format. + bounding_boxes = masks_to_boxes(masks) +# These can be visualized very easily with draw_bounding_boxes utility +# provided in torchvision.utils. + figure = matplotlib.pyplot.figure() a = figure.add_subplot(121) @@ -73,3 +96,11 @@ a.set(xticklabels=[], yticklabels=[], xticks=[], yticks=[]) b.set(xticklabels=[], yticklabels=[], xticks=[], yticks=[]) + + +# Converting Segmentation Dataset to Detection Dataset +# ---------------------------------------------------- +# +# With this utility it becomes very simple to convert a segmentation dataset to a detection dataset. +# We will also use box_convert method from torchvision ops which will help in converting boxes to desired format. +# One can similarly convert panoptic dataset to detection dataset. From 075029eb269c32f11a432a639ea0e9e6c3188222 Mon Sep 17 00:00:00 2001 From: Aditya Oke Date: Mon, 27 Sep 2021 00:59:42 +0530 Subject: [PATCH 2/7] Update example --- gallery/plot_repurposing_annotations.py | 69 ++++++++++++------------- 1 file changed, 33 insertions(+), 36 deletions(-) diff --git a/gallery/plot_repurposing_annotations.py b/gallery/plot_repurposing_annotations.py index 67128059502..52bc11af6ab 100644 --- a/gallery/plot_repurposing_annotations.py +++ b/gallery/plot_repurposing_annotations.py @@ -7,19 +7,18 @@ localization annotations for different tasks (e.g. transforming masks used by instance and panoptic segmentation methods into bounding boxes used by object detection methods). """ -import os -from PIL import Image -import matplotlib.patches -import matplotlib.pyplot as plt + +import os import numpy as np import torch +import matplotlib.pyplot as plt import torchvision.transforms.functional as F -from torchvision.ops import masks_to_boxes -ASSETS_DIRECTORY = "../assets" -matplotlib.pyplot.rcParams["savefig.bbox"] = "tight" +ASSETS_DIRECTORY = "assets" + +plt.rcParams["savefig.bbox"] = "tight" def show(imgs): @@ -49,55 +48,53 @@ def show(imgs): # # A nice property of masks is that they can be easily repurposed to be used in methods to solve a variety of object # localization tasks. -# + +#################################### # Converting Segmentation Masks to bounding boxes # ----------------------------------------------- # For example, the masks to bounding_boxes operation can be used to transform masks into bounding boxes that can be # used as input to detection models such as FasterRCNN and RetinaNet. + +from torchvision.io import read_image +from torchvision.ops import masks_to_boxes + +img_path = os.path.join(ASSETS_DIRECTORY, "FudanPed00054.png") +mask_path = os.path.join(ASSETS_DIRECTORY, "FudanPed00054_mask.png") +img = read_image(img_path) +mask = read_image(mask_path) +mask = F.convert_image_dtype(mask, dtype=torch.float) +obj_ids = torch.unique(mask) +obj_ids = obj_ids[1:] +masks = mask == obj_ids[:, None, None] + +#################################### # Let's visualize an image and segmentation masks. We will use the plotting utilities provided by torchvision.utils # to visualize. The images are taken from PenFudan Dataset. -# -with PIL.Image.open(os.path.join(ASSETS_DIRECTORY, "masks.tiff")) as image: - masks = torch.zeros((image.n_frames, image.height, image.width), dtype=torch.int) - for index in range(image.n_frames): - image.seek(index) +from torchvision.utils import draw_segmentation_masks, draw_bounding_boxes - frame = np.array(image) +drawn_masks = [] +for mask in masks: + drawn_masks.append(draw_segmentation_masks(img, mask, alpha=0.8, colors="blue")) - masks[index] = torch.tensor(frame) +show(drawn_masks) +#################################### # We will use the masks_to_boxes from the torchvision.ops module # It returns the boxes in (xmin, ymin, xmax, ymax) format. -bounding_boxes = masks_to_boxes(masks) +boxes = masks_to_boxes(masks) +#################################### # These can be visualized very easily with draw_bounding_boxes utility # provided in torchvision.utils. -figure = matplotlib.pyplot.figure() - -a = figure.add_subplot(121) -b = figure.add_subplot(122) - -labeled_image = torch.sum(masks, 0) - -a.imshow(labeled_image) -b.imshow(labeled_image) - -for bounding_box in bounding_boxes: - x0, y0, x1, y1 = bounding_box - - rectangle = matplotlib.patches.Rectangle((x0, y0), x1 - x0, y1 - y0, linewidth=1, edgecolor="r", facecolor="none") - - b.add_patch(rectangle) - -a.set(xticklabels=[], yticklabels=[], xticks=[], yticks=[]) -b.set(xticklabels=[], yticklabels=[], xticks=[], yticks=[]) - +drawn_boxes = draw_bounding_boxes(img, boxes, colors="red") +show(drawn_boxes) +#################################### # Converting Segmentation Dataset to Detection Dataset # ---------------------------------------------------- # From bf504e70e7915e6b76bf7fcbf99d9a19170928cb Mon Sep 17 00:00:00 2001 From: Aditya Oke Date: Mon, 27 Sep 2021 11:39:56 +0530 Subject: [PATCH 3/7] Add PenFudan Files --- gallery/assets/FudanPed00050.png | Bin 0 -> 386132 bytes gallery/assets/FudanPed00050_mask.png | Bin 0 -> 2134 bytes 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illustrates the operations available in the torchvision.ops module for repurposing object -localization annotations for different tasks (e.g. transforming masks used by instance and panoptic segmentation +===================================== +Repurposing masks into bounding boxes +===================================== + +The following example illustrates the operations available in the +`torchvision.ops `_ module for repurposing +segmentation masks into object localization annotations for different tasks +(e.g. transforming masks used by instance and panoptic segmentation methods into bounding boxes used by object detection methods). """ @@ -14,7 +16,6 @@ import torch import matplotlib.pyplot as plt -import torchvision.models.detection import torchvision.transforms.functional as F @@ -57,22 +58,23 @@ def show(imgs): # For example, the :func:`~torchvision.ops.masks_to_boxes` operation can be used to # transform masks into bounding boxes that can be # used as input to detection models such as FasterRCNN and RetinaNet. -# We will take images and masks from the PenFudan Dataset. +# We will take images and masks from the `PenFudan Dataset `_. from torchvision.io import read_image -from torchvision.ops import masks_to_boxes img_path = os.path.join(ASSETS_DIRECTORY, "FudanPed00054.png") mask_path = os.path.join(ASSETS_DIRECTORY, "FudanPed00054_mask.png") img = read_image(img_path) mask = read_image(mask_path) -mask = F.convert_image_dtype(mask, dtype=torch.float) + +# You can use floating point masks as well +# mask = F.convert_image_dtype(mask, dtype=torch.float) ######################### # Here the masks are represented as a PNG Image, with floating point values. -# Each pixel is encoded as different colors, with 0 being background -# Notice that that dimensions of image and mask match. +# Each pixel is encoded as different colors, with 0 being background. +# Notice that the spatial dimensions of image and mask match. print(mask.size()) print(img.size()) @@ -90,16 +92,16 @@ def show(imgs): masks = mask == obj_ids[:, None, None] ######################## -# Now the masks are boolean tensor. -# The first dimension in this case 3. Signifies number of instance, that is 3 people in the image. -# The other two dimensions are height and width, which match the dimensions of the image. -# The boolean tensors represent if the pixel belong to the segmentation mask of the image. +# Now the masks are a boolean tensor. +# The first dimension in this case 3. Which signifies the number of instances, that there are 3 people in the image. +# The other two dimensions are height and width, which are equal to the dimensions of the image. +# For each instance, the boolean tensors represent if the particular pixel belong to the segmentation mask of the image. print(masks.size()) print(masks) #################################### -# Let's visualize an image and it's corresponding segmentation masks. +# Let us visualize an image and plot its corresponding segmentation masks. # We will use the :func:`~torchvision.utils.draw_segmentation_masks` to draw the segmentation masks. from torchvision.utils import draw_segmentation_masks @@ -115,6 +117,8 @@ def show(imgs): # We will use the :func:`~torchvision.ops.masks_to_boxes` from the torchvision.ops module # It returns the boxes in ``(xmin, ymin, xmax, ymax)`` format. +from torchvision.ops import masks_to_boxes + boxes = masks_to_boxes(masks) print(boxes.size()) print(boxes) @@ -122,9 +126,10 @@ def show(imgs): #################################### # As the shape denotes, there are 3 boxes and in ``(xmin, ymin, xmax, ymax)`` format. # These can be visualized very easily with :func:`~torchvision.utils.draw_bounding_boxes` utility -# provided in torchvision.utils. +# provided in `torchvision.utils `_. from torchvision.utils import draw_bounding_boxes + drawn_boxes = draw_bounding_boxes(img, boxes, colors="red") show(drawn_boxes) @@ -133,7 +138,9 @@ def show(imgs): # Here is demo with a Faster R-CNN model loaded from # :func:`~torchvision.models.detection.fasterrcnn_resnet50_fpn` -model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True, progress=False) +from torchvision.models.detection import fasterrcnn_resnet50_fpn + +model = fasterrcnn_resnet50_fpn(pretrained=True, progress=False) print(img.size()) img = F.convert_image_dtype(img, torch.float) @@ -150,7 +157,8 @@ def show(imgs): # With this utility it becomes very simple to convert a segmentation dataset to a detection dataset. # With this we can now use a segmentation dataset to train a detection model. # One can similarly convert panoptic dataset to detection dataset. -# You can use the below code for Dataset creation in PenFudan Detection Tutorial +# You can use the below code for Dataset creation in +# `PenFudan Detection Tutorial `_. class SegmentationToDetectionDataset(torch.utils.data.Dataset): def __init__(self, root, transforms): From 483689e0808d24558cf09309f89833275d8e0dda Mon Sep 17 00:00:00 2001 From: Aditya Oke Date: Tue, 28 Sep 2021 23:29:34 +0530 Subject: [PATCH 7/7] Create links, fix float --- docs/source/ops.rst | 2 ++ docs/source/utils.rst | 2 ++ gallery/plot_repurposing_annotations.py | 16 ++++++++-------- 3 files changed, 12 insertions(+), 8 deletions(-) diff --git a/docs/source/ops.rst b/docs/source/ops.rst index 5fd4b75e59d..f4f03bdb298 100644 --- a/docs/source/ops.rst +++ b/docs/source/ops.rst @@ -1,3 +1,5 @@ +.. _ops: + torchvision.ops =============== diff --git a/docs/source/utils.rst b/docs/source/utils.rst index acaf785d817..b0a2d743d4e 100644 --- a/docs/source/utils.rst +++ b/docs/source/utils.rst @@ -1,3 +1,5 @@ +.. _utils: + torchvision.utils ================= diff --git a/gallery/plot_repurposing_annotations.py b/gallery/plot_repurposing_annotations.py index 76d119b9c7a..012bd9857ef 100644 --- a/gallery/plot_repurposing_annotations.py +++ b/gallery/plot_repurposing_annotations.py @@ -3,8 +3,8 @@ Repurposing masks into bounding boxes ===================================== -The following example illustrates the operations available in the -`torchvision.ops `_ module for repurposing +The following example illustrates the operations available +the :ref:`torchvision.ops ` module for repurposing segmentation masks into object localization annotations for different tasks (e.g. transforming masks used by instance and panoptic segmentation methods into bounding boxes used by object detection methods). @@ -68,8 +68,6 @@ def show(imgs): img = read_image(img_path) mask = read_image(mask_path) -# You can use floating point masks as well -# mask = F.convert_image_dtype(mask, dtype=torch.float) ######################### # Here the masks are represented as a PNG Image, with floating point values. @@ -89,13 +87,15 @@ def show(imgs): obj_ids = obj_ids[1:] # split the color-encoded mask into a set of boolean masks. +# Note that this snippet would work as well if the masks were float values instead of ints. masks = mask == obj_ids[:, None, None] ######################## # Now the masks are a boolean tensor. -# The first dimension in this case 3. Which signifies the number of instances, that there are 3 people in the image. +# The first dimension in this case 3 and denotes the number of instances: there are 3 people in the image. # The other two dimensions are height and width, which are equal to the dimensions of the image. -# For each instance, the boolean tensors represent if the particular pixel belong to the segmentation mask of the image. +# For each instance, the boolean tensors represent if the particular pixel +# belongs to the segmentation mask of the image. print(masks.size()) print(masks) @@ -126,7 +126,7 @@ def show(imgs): #################################### # As the shape denotes, there are 3 boxes and in ``(xmin, ymin, xmax, ymax)`` format. # These can be visualized very easily with :func:`~torchvision.utils.draw_bounding_boxes` utility -# provided in `torchvision.utils `_. +# provided in :ref:`torchvision.utils `. from torchvision.utils import draw_bounding_boxes @@ -157,7 +157,7 @@ def show(imgs): # With this utility it becomes very simple to convert a segmentation dataset to a detection dataset. # With this we can now use a segmentation dataset to train a detection model. # One can similarly convert panoptic dataset to detection dataset. -# You can use the below code for Dataset creation in +# Here is an example where we re-purpose the dataset from the # `PenFudan Detection Tutorial `_. class SegmentationToDetectionDataset(torch.utils.data.Dataset):

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z_%}W^GiH=+vqhn0njTF~?5Azd(S>lOtw(Z?ztRG6J0scIY;0HKLZl_w;1G!0t~o-JK`gRVgt%{CeUfbc!n%R;_64^K*^xW<{UF z16q+8A=Mh*c;KMB=-S7~46S*idnN`6scVKYj!erZwT||S&g#XK;^~+(GV7RZkYUzIW>(2VSC*aW%r_Q*%KJ-GXjISok-g zDn&4hIc#vaK<>+OE!o8?_3P-S{oxGwk32nkj>?9^O&3Oi;Xs}wgNp2RMIvx2dU-@b z+~q#OO;;;TNKt|P^hU-7dw_|T(Yn5E3pElIcoV};v(5|!}IPY6mG{Sk2q~Gusj;oF6sE=b7j^$Ox(!(;RB_` zckM^PWEjjZz6-Crcg+)A{B>X425dAS^il*a{47ZkB6<=7L)OeRHPh_~B*h<(W<5lS z**V6#iMz>zs6wY9P@8ZvE(PPf&MAo&WKXVZVI4|^o@v2Haej#ITd2n+ka$XF?+?Hn zwf{n4q8MXfgZ_J55Fn%ptB;R@to62*q~()D>Nigqjb{Tkh Date: Mon, 27 Sep 2021 21:14:18 +0530 Subject: [PATCH 4/7] Update example --- gallery/plot_repurposing_annotations.py | 113 +++++++++++++++++++++--- 1 file changed, 103 insertions(+), 10 deletions(-) diff --git a/gallery/plot_repurposing_annotations.py b/gallery/plot_repurposing_annotations.py index 52bc11af6ab..903e512328f 100644 --- a/gallery/plot_repurposing_annotations.py +++ b/gallery/plot_repurposing_annotations.py @@ -13,6 +13,8 @@ import numpy as np import torch import matplotlib.pyplot as plt + +import torchvision.models.detection import torchvision.transforms.functional as F @@ -50,10 +52,12 @@ def show(imgs): # localization tasks. #################################### -# Converting Segmentation Masks to bounding boxes +# Converting Masks to Bounding Boxes # ----------------------------------------------- -# For example, the masks to bounding_boxes operation can be used to transform masks into bounding boxes that can be +# For example, the :func:`~torchvision.ops.masks_to_boxes` operation can be used to +# transform masks into bounding boxes that can be # used as input to detection models such as FasterRCNN and RetinaNet. +# We will take images and masks from the PenFudan Dataset. from torchvision.io import read_image @@ -64,16 +68,41 @@ def show(imgs): img = read_image(img_path) mask = read_image(mask_path) mask = F.convert_image_dtype(mask, dtype=torch.float) + +######################### +# Here the masks are represented as a PNG Image, with floating point values. +# Each pixel is encoded as different colors, with 0 being background +# Notice that that dimensions of image and mask match. + +print(mask.size()) +print(img.size()) +print(mask) + +############################ + +# We get the unique colors, as these would be the object ids. obj_ids = torch.unique(mask) + +# first id is the background, so remove it. obj_ids = obj_ids[1:] + +# split the color-encoded mask into a set of boolean masks. masks = mask == obj_ids[:, None, None] -#################################### -# Let's visualize an image and segmentation masks. We will use the plotting utilities provided by torchvision.utils -# to visualize. The images are taken from PenFudan Dataset. +######################## +# Now the masks are boolean tensor. +# The first dimension in this case 3. Signifies number of instance, that is 3 people in the image. +# The other two dimensions are height and width, which match the dimensions of the image. +# The boolean tensors represent if the pixel belong to the segmentation mask of the image. + +print(masks.size()) +print(masks) +#################################### +# Let's visualize an image and it's corresponding segmentation masks. +# We will use the :func:`~torchvision.utils.draw_segmentation_masks` to draw the segmentation masks. -from torchvision.utils import draw_segmentation_masks, draw_bounding_boxes +from torchvision.utils import draw_segmentation_masks drawn_masks = [] for mask in masks: @@ -82,22 +111,86 @@ def show(imgs): show(drawn_masks) #################################### -# We will use the masks_to_boxes from the torchvision.ops module -# It returns the boxes in (xmin, ymin, xmax, ymax) format. +# To convert the boolean masks into bounding boxes. +# We will use the :func:`~torchvision.ops.masks_to_boxes` from the torchvision.ops module +# It returns the boxes in ``(xmin, ymin, xmax, ymax)`` format. boxes = masks_to_boxes(masks) +print(boxes.size()) +print(boxes) #################################### -# These can be visualized very easily with draw_bounding_boxes utility +# As the shape denotes, there are 3 boxes and in ``(xmin, ymin, xmax, ymax)`` format. +# These can be visualized very easily with :func:`~torchvision.utils.draw_bounding_boxes` utility # provided in torchvision.utils. +from torchvision.utils import draw_bounding_boxes drawn_boxes = draw_bounding_boxes(img, boxes, colors="red") show(drawn_boxes) +################################### +# These boxes can now directly be used by detection models in torchvision. +# Here is demo with a Faster R-CNN model loaded from +# :func:`~torchvision.models.detection.fasterrcnn_resnet50_fpn` + +model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True, progress=False) +print(img.size()) + +img = F.convert_image_dtype(img, torch.float) +target = {} +target["boxes"] = boxes +target["labels"] = labels = torch.ones((masks.size(0),), dtype=torch.int64) +detection_outputs = model(img.unsqueeze(0), [target]) + + #################################### # Converting Segmentation Dataset to Detection Dataset # ---------------------------------------------------- # # With this utility it becomes very simple to convert a segmentation dataset to a detection dataset. -# We will also use box_convert method from torchvision ops which will help in converting boxes to desired format. +# With this we can now use a segmentation dataset to train a detection model. # One can similarly convert panoptic dataset to detection dataset. +# You can use the below code for Dataset creation in PenFudan Detection Tutorial + +class SegmentationToDetectionDataset(torch.utils.data.Dataset): + def __init__(self, root, transforms): + self.root = root + self.transforms = transforms + # load all image files, sorting them to + # ensure that they are aligned + self.imgs = list(sorted(os.listdir(os.path.join(root, "PNGImages")))) + self.masks = list(sorted(os.listdir(os.path.join(root, "PedMasks")))) + + def __getitem__(self, idx): + # load images and masks + img_path = os.path.join(self.root, "PNGImages", self.imgs[idx]) + mask_path = os.path.join(self.root, "PedMasks", self.masks[idx]) + + img = read_image(img_path) + mask = read_image(mask_path) + + img = F.convert_image_dtype(img, dtype=torch.float) + mask = F.convert_image_dtype(mask, dtype=torch.float) + + # We get the unique colors, as these would be the object ids. + obj_ids = torch.unique(mask) + + # first id is the background, so remove it. + obj_ids = obj_ids[1:] + + # split the color-encoded mask into a set of boolean masks. + masks = mask == obj_ids[:, None, None] + + boxes = masks_to_boxes(masks) + + # there is only one class + labels = torch.ones((masks.shape[0],), dtype=torch.int64) + + target = {} + target["boxes"] = boxes + target["labels"] = labels + + if 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