Convert "Visualization utilities" notebook into a sphinx-gallery example

docs/Makefile

@@ -26,7 +26,7 @@ html-noplot:  # Avoids running the gallery examples, which may take time
 
 clean:
 	rm -rf $(BUILDDIR)/*
-	rm -rf auto_examples/
+	rm -rf $(SOURCEDIR)/auto_examples/
 
 .PHONY: help Makefile docset
 

gallery/plot_scripted_tensor_transforms.py

@@ -34,7 +34,7 @@
 
 
 plt.rcParams["savefig.bbox"] = 'tight'
-torch.manual_seed(0)
+torch.manual_seed(1)
 
 
 def show(imgs):

gallery/plot_visualization_utils.py

@@ -0,0 +1,131 @@
+"""
+=======================
+Visualization utilities
+=======================
+
+This example illustrates some of the utilities that torchvision offers for
+visualizing images, bounding boxes, and segmentation masks.
+"""
+
+
+import torch
+import numpy as np
+import scipy.misc
+import matplotlib.pyplot as plt
+
+import torchvision.transforms.functional as F
+
+
+plt.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 = F.to_pil_image(img.to('cpu'))
+        axs[0, i].imshow(np.asarray(img))
+        axs[0, i].set(xticklabels=[], yticklabels=[], xticks=[], yticks=[])
+
+
+####################################
+# Visualizing a grid of images
+# ----------------------------
+# The :func:`~torchvision.utils.make_grid` function can be used to create a
+# tensor that represents multiple images in a grid.  This util requires a single
+# image of dtype ``uint8`` as input.
+
+from torchvision.utils import make_grid
+from torchvision.io import read_image
+from pathlib import Path
+
+dog1_int = read_image(str(Path('assets') / 'dog1.jpg'))
+dog2_int = read_image(str(Path('assets') / 'dog2.jpg'))
+
+grid = make_grid([dog1_int, dog2_int, dog1_int, dog2_int])
+show(grid)
+
+####################################
+# Visualizing bounding boxes
+# --------------------------
+# We can use :func:`~torchvision.utils.draw_bounding_boxes` to draw boxes on an
+# image. We can set the colors, labels, width as well as font and font size !
+# The boxes are in ``(xmin, ymin, xmax, ymax)`` format
+# from torchvision.utils import draw_bounding_boxes
+
+from torchvision.utils import draw_bounding_boxes
+
+
+boxes = torch.tensor([[50, 50, 100, 200], [210, 150, 350, 430]], dtype=torch.float)
+colors = ["blue", "yellow"]
+result = draw_bounding_boxes(dog1_int, boxes, colors=colors, width=5)
+show(result)
+
+
+#####################################
+# Naturally, we can also plot bounding boxes produced by torchvision detection
+# models.  Here is demo with a Faster R-CNN model loaded from
+# :func:`~torchvision.models.detection.fasterrcnn_resnet50_fpn`
+# model. You can also try using a RetinaNet with
+# :func:`~torchvision.models.detection.retinanet_resnet50_fpn`.
+
+from torchvision.models.detection import fasterrcnn_resnet50_fpn
+from torchvision.transforms.functional import convert_image_dtype
+
+
+dog1_float = convert_image_dtype(dog1_int, dtype=torch.float)
+dog2_float = convert_image_dtype(dog2_int, dtype=torch.float)
+batch = torch.stack([dog1_float, dog2_float])
+
+model = fasterrcnn_resnet50_fpn(pretrained=True, progress=False)
+model = model.eval()
+
+outputs = model(batch)
+print(outputs)
+
+#####################################
+# Let's plot the boxes detected by our model. We will only plot the boxes with a
+# score greater than a given threshold.
+
+threshold = .8
+dogs_with_boxes = [
+    draw_bounding_boxes(dog_int, boxes=output['boxes'][output['scores'] > threshold], width=4)
+    for dog_int, output in zip((dog1_int, dog2_int), outputs)
+]
+show(dogs_with_boxes)
+
+#####################################
+# Visualizing segmentation masks
+# ------------------------------
+# The :func:`~torchvision.utils.draw_segmentation_masks` function can be used to
+# draw segmentation amasks on images. We can set the colors as well as
+# transparency of masks.
+#
+# Here is demo with torchvision's FCN Resnet-50, loaded with
+# :func:`~torchvision.models.segmentation.fcn_resnet50`.
+# You can also try using
+# DeepLabv3 (:func:`~torchvision.models.segmentation.deeplabv3_resnet50`)
+# or lraspp mobilenet models
+# (:func:`~torchvision.models.segmentation.lraspp_mobilenet_v3_large`).
+#
+# Like :func:`~torchvision.utils.draw_bounding_boxes`,
+# :func:`~torchvision.utils.draw_segmentation_masks` requires a single RGB image
+# of dtype `uint8`.
+
+from torchvision.models.segmentation import fcn_resnet50
+from torchvision.utils import draw_segmentation_masks
+
+
+model = fcn_resnet50(pretrained=True, progress=False)
+model = model.eval()
+
+# The model expects the batch to be normalized
+batch = F.normalize(batch, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))
+outputs = model(batch)
+
+dogs_with_masks = [
+    draw_segmentation_masks(dog_int, masks=masks, alpha=0.6)
+    for dog_int, masks in zip((dog1_int, dog2_int), outputs['out'])
+]
+show(dogs_with_masks)

torchvision/utils.py

@@ -40,10 +40,6 @@ def make_grid(
 
     Returns:
         grid (Tensor): the tensor containing grid of images.
-
-    Example:
-        See this notebook
-        `here <https://github.com/pytorch/vision/blob/master/examples/python/visualization_utils.ipynb>`_
     """
     if not (torch.is_tensor(tensor) or
             (isinstance(tensor, list) and all(torch.is_tensor(t) for t in tensor))):
@@ -174,10 +170,6 @@ def draw_bounding_boxes(
 
     Returns:
         img (Tensor[C, H, W]): Image Tensor of dtype uint8 with bounding boxes plotted.
-
-    Example:
-        See this notebook
-        `linked <https://github.com/pytorch/vision/blob/master/examples/python/visualization_utils.ipynb>`_
     """
 
     if not isinstance(image, torch.Tensor):
@@ -239,16 +231,12 @@ def draw_segmentation_masks(
     Args:
         image (Tensor): Tensor of shape (3 x H x W) and dtype uint8.
         masks (Tensor): Tensor of shape (num_masks, H, W). Each containing probability of predicted class.
-        alpha (float): Float number between 0 and 1 denoting factor of transpaerency of masks.
+        alpha (float): Float number between 0 and 1 denoting factor of transparency of masks.
         colors (List[Union[str, Tuple[int, int, int]]]): List containing the colors of masks. The colors can
             be represented as `str` or `Tuple[int, int, int]`.
 
     Returns:
         img (Tensor[C, H, W]): Image Tensor of dtype uint8 with segmentation masks plotted.
-
-    Example:
-        See this notebook
-        `attached <https://github.com/pytorch/vision/blob/master/examples/python/visualization_utils.ipynb>`_
     """
 
     if not isinstance(image, torch.Tensor):