plot_visualization_utils.py

"""
=======================
Visualization utilities
=======================

This example illustrates some of the utilities that torchvision offers for
visualizing images, bounding boxes, segmentation masks and keypoints.
"""

# sphinx_gallery_thumbnail_path = "../../gallery/assets/visualization_utils_thumbnail2.png"

import torch
import numpy as np
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]
    fig, 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=[])


####################################
# 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'))
dog_list = [dog1_int, dog2_int]

grid = make_grid(dog_list)
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


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 a demo with a Faster R-CNN model loaded from
# :func:`~torchvision.models.detection.fasterrcnn_resnet50_fpn`
# model. For more details on the output of such models, you may
# refer to :ref:`instance_seg_output`.

from torchvision.models.detection import fasterrcnn_resnet50_fpn, FasterRCNN_ResNet50_FPN_Weights


weights = FasterRCNN_ResNet50_FPN_Weights.DEFAULT
transforms = weights.transforms()

images = [transforms(d) for d in dog_list]

model = fasterrcnn_resnet50_fpn(weights=weights, progress=False)
model = model.eval()

outputs = model(images)
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.

score_threshold = .8
dogs_with_boxes = [
    draw_bounding_boxes(dog_int, boxes=output['boxes'][output['scores'] > score_threshold], width=4)
    for dog_int, output in zip(dog_list, outputs)
]
show(dogs_with_boxes)

#####################################
# Visualizing segmentation masks
# ------------------------------
# The :func:`~torchvision.utils.draw_segmentation_masks` function can be used to
# draw segmentation masks on images. Semantic segmentation and instance
# segmentation models have different outputs, so we will treat each
# independently.
#
# .. _semantic_seg_output:
#
# Semantic segmentation models
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
# We will see how to use it with torchvision's FCN Resnet-50, loaded with
# :func:`~torchvision.models.segmentation.fcn_resnet50`. Let's start by looking
# at the output of the model.

from torchvision.models.segmentation import fcn_resnet50, FCN_ResNet50_Weights

weights = FCN_ResNet50_Weights.DEFAULT
transforms = weights.transforms(resize_size=None)

model = fcn_resnet50(weights=weights, progress=False)
model = model.eval()

batch = torch.stack([transforms(d) for d in dog_list])
output = model(batch)['out']
print(output.shape, output.min().item(), output.max().item())

#####################################
# As we can see above, the output of the segmentation model is a tensor of shape
# ``(batch_size, num_classes, H, W)``. Each value is a non-normalized score, and
# we can normalize them into ``[0, 1]`` by using a softmax. After the softmax,
# we can interpret each value as a probability indicating how likely a given
# pixel is to belong to a given class.
#
# Let's plot the masks that have been detected for the dog class and for the
# boat class:

sem_class_to_idx = {cls: idx for (idx, cls) in enumerate(weights.meta["categories"])}

normalized_masks = torch.nn.functional.softmax(output, dim=1)

dog_and_boat_masks = [
    normalized_masks[img_idx, sem_class_to_idx[cls]]
    for img_idx in range(len(dog_list))
    for cls in ('dog', 'boat')
]

show(dog_and_boat_masks)

#####################################
# As expected, the model is confident about the dog class, but not so much for
# the boat class.
#
# The :func:`~torchvision.utils.draw_segmentation_masks` function can be used to
# plots those masks on top of the original image. This function expects the
# masks to be boolean masks, but our masks above contain probabilities in ``[0,
# 1]``. To get boolean masks, we can do the following:

class_dim = 1
boolean_dog_masks = (normalized_masks.argmax(class_dim) == sem_class_to_idx['dog'])
print(f"shape = {boolean_dog_masks.shape}, dtype = {boolean_dog_masks.dtype}")
show([m.float() for m in boolean_dog_masks])


#####################################
# The line above where we define ``boolean_dog_masks`` is a bit cryptic, but you
# can read it as the following query: "For which pixels is 'dog' the most likely
# class?"
#
# .. note::
#   While we're using the ``normalized_masks`` here, we would have
#   gotten the same result by using the non-normalized scores of the model
#   directly (as the softmax operation preserves the order).
#
# Now that we have boolean masks, we can use them with
# :func:`~torchvision.utils.draw_segmentation_masks` to plot them on top of the
# original images:

from torchvision.utils import draw_segmentation_masks

dogs_with_masks = [
    draw_segmentation_masks(img, masks=mask, alpha=0.7)
    for img, mask in zip(dog_list, boolean_dog_masks)
]
show(dogs_with_masks)

#####################################
# We can plot more than one mask per image! Remember that the model returned as
# many masks as there are classes. Let's ask the same query as above, but this
# time for *all* classes, not just the dog class: "For each pixel and each class
# C, is class C the most likely class?"
#
# This one is a bit more involved, so we'll first show how to do it with a
# single image, and then we'll generalize to the batch

num_classes = normalized_masks.shape[1]
dog1_masks = normalized_masks[0]
class_dim = 0
dog1_all_classes_masks = dog1_masks.argmax(class_dim) == torch.arange(num_classes)[:, None, None]

print(f"dog1_masks shape = {dog1_masks.shape}, dtype = {dog1_masks.dtype}")
print(f"dog1_all_classes_masks = {dog1_all_classes_masks.shape}, dtype = {dog1_all_classes_masks.dtype}")

dog_with_all_masks = draw_segmentation_masks(dog1_int, masks=dog1_all_classes_masks, alpha=.6)
show(dog_with_all_masks)

#####################################
# We can see in the image above that only 2 masks were drawn: the mask for the
# background and the mask for the dog. This is because the model thinks that
# only these 2 classes are the most likely ones across all the pixels. If the
# model had detected another class as the most likely among other pixels, we
# would have seen its mask above.
#
# Removing the background mask is as simple as passing
# ``masks=dog1_all_classes_masks[1:]``, because the background class is the
# class with index 0.
#
# Let's now do the same but for an entire batch of images. The code is similar
# but involves a bit more juggling with the dimensions.

class_dim = 1
all_classes_masks = normalized_masks.argmax(class_dim) == torch.arange(num_classes)[:, None, None, None]
print(f"shape = {all_classes_masks.shape}, dtype = {all_classes_masks.dtype}")
# The first dimension is the classes now, so we need to swap it
all_classes_masks = all_classes_masks.swapaxes(0, 1)

dogs_with_masks = [
    draw_segmentation_masks(img, masks=mask, alpha=.6)
    for img, mask in zip(dog_list, all_classes_masks)
]
show(dogs_with_masks)


#####################################
# .. _instance_seg_output:
#
# Instance segmentation models
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
# Instance segmentation models have a significantly different output from the
# semantic segmentation models. We will see here how to plot the masks for such
# models. Let's start by analyzing the output of a Mask-RCNN model. Note that
# these models don't require the images to be normalized, so we don't need to
# use the normalized batch.
#
# .. note::
#
#     We will here describe the output of a Mask-RCNN model. The models in
#     :ref:`object_det_inst_seg_pers_keypoint_det` all have a similar output
#     format, but some of them may have extra info like keypoints for
#     :func:`~torchvision.models.detection.keypointrcnn_resnet50_fpn`, and some
#     of them may not have masks, like
#     :func:`~torchvision.models.detection.fasterrcnn_resnet50_fpn`.

from torchvision.models.detection import maskrcnn_resnet50_fpn, MaskRCNN_ResNet50_FPN_Weights

weights = MaskRCNN_ResNet50_FPN_Weights.DEFAULT
transforms = weights.transforms()

images = [transforms(d) for d in dog_list]

model = maskrcnn_resnet50_fpn(weights=weights, progress=False)
model = model.eval()

output = model(images)
print(output)

#####################################
# Let's break this down. For each image in the batch, the model outputs some
# detections (or instances). The number of detections varies for each input
# image. Each instance is described by its bounding box, its label, its score
# and its mask.
#
# The way the output is organized is as follows: the output is a list of length
# ``batch_size``. Each entry in the list corresponds to an input image, and it
# is a dict with keys 'boxes', 'labels', 'scores', and 'masks'. Each value
# associated to those keys has ``num_instances`` elements in it.  In our case
# above there are 3 instances detected in the first image, and 2 instances in
# the second one.
#
# The boxes can be plotted with :func:`~torchvision.utils.draw_bounding_boxes`
# as above, but here we're more interested in the masks. These masks are quite
# different from the masks that we saw above for the semantic segmentation
# models.

dog1_output = output[0]
dog1_masks = dog1_output['masks']
print(f"shape = {dog1_masks.shape}, dtype = {dog1_masks.dtype}, "
      f"min = {dog1_masks.min()}, max = {dog1_masks.max()}")

#####################################
# Here the masks correspond to probabilities indicating, for each pixel, how
# likely it is to belong to the predicted label of that instance. Those
# predicted labels correspond to the 'labels' element in the same output dict.
# Let's see which labels were predicted for the instances of the first image.

print("For the first dog, the following instances were detected:")
print([weights.meta["categories"][label] for label in dog1_output['labels']])

#####################################
# Interestingly, the model detects two persons in the image. Let's go ahead and
# plot those masks. Since :func:`~torchvision.utils.draw_segmentation_masks`
# expects boolean masks, we need to convert those probabilities into boolean
# values. Remember that the semantic of those masks is "How likely is this pixel
# to belong to the predicted class?". As a result, a natural way of converting
# those masks into boolean values is to threshold them with the 0.5 probability
# (one could also choose a different threshold).

proba_threshold = 0.5
dog1_bool_masks = dog1_output['masks'] > proba_threshold
print(f"shape = {dog1_bool_masks.shape}, dtype = {dog1_bool_masks.dtype}")

# There's an extra dimension (1) to the masks. We need to remove it
dog1_bool_masks = dog1_bool_masks.squeeze(1)

show(draw_segmentation_masks(dog1_int, dog1_bool_masks, alpha=0.9))

#####################################
# The model seems to have properly detected the dog, but it also confused trees
# with people. Looking more closely at the scores will help us plot more
# relevant masks:

print(dog1_output['scores'])

#####################################
# Clearly the model is more confident about the dog detection than it is about
# the people detections. That's good news. When plotting the masks, we can ask
# for only those that have a good score. Let's use a score threshold of .75
# here, and also plot the masks of the second dog.

score_threshold = .75

boolean_masks = [
    out['masks'][out['scores'] > score_threshold] > proba_threshold
    for out in output
]

dogs_with_masks = [
    draw_segmentation_masks(img, mask.squeeze(1))
    for img, mask in zip(dog_list, boolean_masks)
]
show(dogs_with_masks)

#####################################
# The two 'people' masks in the first image where not selected because they have
# a lower score than the score threshold. Similarly, in the second image, the
# instance with class 15 (which corresponds to 'bench') was not selected.

#####################################
# .. _keypoint_output:
#
# Visualizing keypoints
# ------------------------------
# The :func:`~torchvision.utils.draw_keypoints` function can be used to
# draw keypoints on images. We will see how to use it with
# torchvision's KeypointRCNN loaded with :func:`~torchvision.models.detection.keypointrcnn_resnet50_fpn`.
# We will first have a look at output of the model.
#

from torchvision.models.detection import keypointrcnn_resnet50_fpn, KeypointRCNN_ResNet50_FPN_Weights
from torchvision.io import read_image

person_int = read_image(str(Path("assets") / "person1.jpg"))

weights = KeypointRCNN_ResNet50_FPN_Weights.DEFAULT
transforms = weights.transforms()

person_float = transforms(person_int)

model = keypointrcnn_resnet50_fpn(weights=weights, progress=False)
model = model.eval()

outputs = model([person_float])
print(outputs)

#####################################
# As we see the output contains a list of dictionaries.
# The output list is of length batch_size.
# We currently have just a single image so length of list is 1.
# Each entry in the list corresponds to an input image,
# and it is a dict with keys `boxes`, `labels`, `scores`, `keypoints` and `keypoint_scores`.
# Each value associated to those keys has `num_instances` elements in it.
# In our case above there are 2 instances detected in the image.

kpts = outputs[0]['keypoints']
scores = outputs[0]['scores']

print(kpts)
print(scores)

#####################################
# The KeypointRCNN model detects there are two instances in the image.
# If you plot the boxes by using :func:`~draw_bounding_boxes`
# you would recognize they are the person and the surfboard.
# If we look at the scores, we will realize that the model is much more confident about the person than surfboard.
# We could now set a threshold confidence and plot instances which we are confident enough.
# Let us set a threshold of 0.75 and filter out the keypoints corresponding to the person.

detect_threshold = 0.75
idx = torch.where(scores > detect_threshold)
keypoints = kpts[idx]

print(keypoints)

#####################################
# Great, now we have the keypoints corresponding to the person.
# Each keypoint is represented by x, y coordinates and the visibility.
# We can now use the :func:`~torchvision.utils.draw_keypoints` function to draw keypoints.
# Note that the utility expects uint8 images.

from torchvision.utils import draw_keypoints

res = draw_keypoints(person_int, keypoints, colors="blue", radius=3)
show(res)

#####################################
# As we see the keypoints appear as colored circles over the image.
# The coco keypoints for a person are ordered and represent the following list.\

coco_keypoints = [
    "nose", "left_eye", "right_eye", "left_ear", "right_ear",
    "left_shoulder", "right_shoulder", "left_elbow", "right_elbow",
    "left_wrist", "right_wrist", "left_hip", "right_hip",
    "left_knee", "right_knee", "left_ankle", "right_ankle",
]

#####################################
# What if we are interested in joining the keypoints?
# This is especially useful in creating pose detection or action recognition.
# We can join the keypoints easily using the `connectivity` parameter.
# A close observation would reveal that we would need to join the points in below
# order to construct human skeleton.
#
# nose -> left_eye -> left_ear.                              (0, 1), (1, 3)
#
# nose -> right_eye -> right_ear.                            (0, 2), (2, 4)
#
# nose -> left_shoulder -> left_elbow -> left_wrist.         (0, 5), (5, 7), (7, 9)
#
# nose -> right_shoulder -> right_elbow -> right_wrist.      (0, 6), (6, 8), (8, 10)
#
# left_shoulder -> left_hip -> left_knee -> left_ankle.      (5, 11), (11, 13), (13, 15)
#
# right_shoulder -> right_hip -> right_knee -> right_ankle.  (6, 12), (12, 14), (14, 16)
#
# We will create a list containing these keypoint ids to be connected.

connect_skeleton = [
    (0, 1), (0, 2), (1, 3), (2, 4), (0, 5), (0, 6), (5, 7), (6, 8),
    (7, 9), (8, 10), (5, 11), (6, 12), (11, 13), (12, 14), (13, 15), (14, 16)
]

#####################################
# We pass the above list to the connectivity parameter to connect the keypoints.
#

res = draw_keypoints(person_int, keypoints, connectivity=connect_skeleton, colors="blue", radius=4, width=3)
show(res)