# Getting started¶

Once you have Terran installed (see Installation), you should be ready to use it. This section offers an overview of what’s possible and what tools are available within the library.

There are three aspects of Terran you should be familiar with in order to use it:

• The I/O helper functions that allow for reading and writing media.
• The actual models present in Terran, allowing you to perform face detection, face recognition and pose detection.
• The visualization utilities that can be used to, well, visualize the results.

Let’s now go over each of these.

## Reading and writing images and videos¶

Terran provides utilities for reading and writing different media through a simple interface, so you don’t have to install extra libraries or write dozens of lines of code just to read frames efficiently out of a video.

First of all is, of course, reading images (see terran.io.image). The function open_image is a simple wrapper around PIL.Image.open that returns a numpy.ndarray of size (height, width, channels) representing the image. All image data within Terran is specified in the $$HxWxC$$ format.

The function also allows reading remote images, so you could do as follows:

# All I/O functions are exposed at the terran.io level too.
from terran.io import open_image

image = open_image(
'https://raw.githubusercontent.com/nagitsu/terran'
)

print(image.shape)
# >> (1280, 1920, 3).


For reading videos, you can use open_video. This function opens a resource containing a video and returns a Video class representing it. In order to obtain the actual frames, the class may be iterated on, as it behaves like a generator. Frames will be yielded one by one, or by batches, depending on the batch_size parameter passed to the function.

open_video can handle several sources of video:

• Local video files, such as short_movie.mkv or dog.mp4.
• URLs to a video stream, such as http://127.0.0.1:8000/my-stream/, if you were streaming something locally. This is any stream that can be read by FFmpeg.
• Path to the webcam devices, such as /dev/video0. This will open the resource and start streaming from it, if it’s available.
• Videos hosted on video platforms supported by Youtube-DL. This means a URL pointing to a e.g. Youtube or Vimeo video.

As an example, this would open and start yielding frames from a Youtube video:

from terran.io import open_video

video = open_video(
start_time='00:00:30',
batch_size=32,
)

for frames in video:
print(frames.shape)
# >> (32, 1280, 1920, 3)


As you can see above, you can select the starting time, the time to read for, the batch size and more. See the function reference for all the available options.

You can also write videos through the write_video function. Calling this function and pointing it to a path will return a VideoWriter object and create a video file, exposing a write_frame method that receives an image and adds it to the video. For instance:

from terran.io import write_video

writer = write_video('dog.mp4')

# images is an array of images of the same size.
for image in images:
writer.write_frame(image)

# We *must* close the writer, or the video file will be corrupt.
writer.close()


Both the Video and VideoWriter classes will perform the reading and writing through FFmpeg in a background thread, in order to avoid blocking the program while video is read and memory is copied over. This improves resource utilization by quite a lot.

These are not all the I/O functions available, and not all they can do; you can check I/O functions for more information.

## Interacting with people¶

But, of course, we’re not here for the I/O functions. Let’s see how Terran can help us locate and interact with people in images and videos.

### Detecting faces¶

Given an image or a batch of images (say, the batched frames returned by iterating over a Video instance), you can call face_detection to obtain the faces present on them.

For each image, Terran will return a list of faces found. Each face is represented by a dictionary containing three keys:

• bbox which is a numpy.ndarray of size (4,), containing the coordinates of the bounding box that surrounds the face. These coordinates are in a $$(x_{min}, y_{min}, x_{max}, y_{max})$$ format.
• landmarks which is a numpy.ndarray of size (5, 2), containing the $$(x, y)$$ coordinates of five facial landmarks of the face. This can be (and is) used by downstream algorithms to align the face correctly before processing.
• score which is a numpy.ndarray of size (1,), with the confidence score of the detected face, a value between 0 and 1.

Terran does its best to match the return type to whichever input was sent into. This means that if you, for instance, send in a single image, you’ll receive a single list containing each face data. If you, however, send in a batch of images, the function will return a list containing a list of faces for each image.

Imagine we have the image we loaded on the previous section using open_image. We can detect all of the faces present by passing it to face_detection:

print(image.shape)
# >> (1280, 1920, 3).

# All face-related functions are re-exported at the terran.face
# level.
from terran.face import face_detection

faces = face_detection(image)
for face in faces:
print(face)
print('bbox = ({}); landmarks = ({}); conf = {:.2f}'.format(
', '.join(map(str, face['bbox'])),
' '.join(map(str, face['landmarks'])),
face['score']
))

# >> bbox = (1326, 1048, 1475, 1229); landmarks = ([1360 1115] [1427 1116] [1390 1156] [1367 1183] [1421 1183]); conf = 1.00
# >> bbox = (590, 539, 690, 667); landmarks = ([604 583] [647 586] [615 612] [608 633] [642 635]); conf = 0.99
# >> bbox = (1711, 408, 1812, 530); landmarks = ([1731  451] [1775  451] [1747  477] [1735
499] [1769  499]); conf = 0.99


If you were to send a batch of frames, for instance, the return type would be different:

print(frames.shape)
# >> (32, 1280, 1920, 3)

faces_per_frame = face_detection(frames)
print(len(faces_per_frame))
# >> 32
print(type(faces_per_frame[0]))
# >> list
print(len(faces_per_frame[0]))
# >> 1


### Recognizing faces¶

The task of face recognition aims to give a unique representation to a face. In a perfect scenario, this representation would be robust to changes in appearance, such as the person growing a beard or changing their hairstyle. Of course, that’s very difficult to achieve. What we try to do, instead, is extract features out of the face, represented by a N-dimensional vector (a numpy.ndarray of shape (N,)) that is as stable as possible across appearence changes.

Through the function extract_features, you can extract these features. If you run it through a face, such as the ones detected above, you’ll get a dense representation of it. This representation is constructed so that, if you take two faces of the same person, the cosine distance between their features should be very small.

This is better illustrated with an example. Let’s take the following three images:

We can obtain the representations of each as follows:

from terran.face import extract_features

# We'll go over on how exactly the function is called in a bit.
features_rw1 = extract_features(
rw1, faces_per_image=face_detection(rw1)
)[0]
features_rw2 = extract_features(
rw2, faces_per_image=face_detection(rw2)
)[0]
features_th = extract_features(
th, faces_per_image=face_detection(th)
)[0]

# In this case, the vector dimension, N, is 512:
print(features_rw1.shape)
# >> (512,)

# We can compare the vectors using the cosine distance.
from scipy.spatial.distance import cosine

# If the distance between two faces is below 0.7, it's probably the
# same person. If it's below 0.4, you can be almost certain it is.
print(cosine(features_rw1, features_rw2))
# >> 0.5384056568145752
print(cosine(features_rw1, features_th))
# >> 1.0747144743800163
print(cosine(features_rw2, features_th))
# >> 1.06807991117239


As you can see, extracting features on a face will give us a vector of shape (512,) that, along with the cosine function, will help us identify a person across images.

The function extract_features can be called in two ways:

• Like we did above, by sending in the image and passing the faces detected by face_detection in the faces_per_image optional parameter. This will make the function return a list with one entry per image and, within each, a list of one entry per face containing the features. (Note that this is why we used the [0], to obtain the features for the first -and only- face.)
• By sending in a list of already-cropped faces. You just send in a list of faces and you receive a list of features.

See usage/algorithms for more information into why these alternatives exist and what’s the recommended way of calling it (hint: it’s the first one!).

### Estimating poses of people¶

You can use pose_estimation to obtain the poses of the people present in an image. The process is similar to how we did it with face detection: you pass in an image, and you obtain the coordinates of each keypoint of the pose.

In this case, instead of the bbox and landmarks keys, you’ll get a keypoints key containing a numpy.ndarray of shape (18, 3), consisting of the 18 keypoints detected for poses:

image = open_image(
'https://raw.githubusercontent.com/nagitsu/terran'
)

from terran.pose import pose_estimation

poses = pose_estimation(image)
print(len(poses))
# >> 6

print(poses[0]['keypoints'])
# >> array([[  0,   0,   0],
# >>        [714, 351,   1],
# >>        ...
# >>        [  0,   0,   0],
# >>        [725, 286,   1],
# >>        [678, 292,   1]], dtype=int32)


The keypoints array has three columns: the first two are the $$(x, y)$$ coordinates, while the third is either 0 or 1, indicating whether the keypoint is visible or not. You can see which of the 18 keypoints is which by taking a look at the Keypoint enum.

The first time you use any of the functions above, you will get prompted to download the appropriate checkpoint. Terran offers several pre-trained models, each for different scenarios that may have different resource constraints. You can list them all by using the CLI tool exposed by Terran upon install:

\$ terran checkpoint list
========================================================================================
|                           Name |        Alias |    Eval. |    Perf. |         Status |
========================================================================================
| Face detection (terran.face.Detection)                                             |
----------------------------------------------------------------------------------------
----------------------------------------------------------------------------------------
| Face recognition (terran.face.Recognition)                                         |
----------------------------------------------------------------------------------------
----------------------------------------------------------------------------------------
| Pose estimation (terran.pose.Estimation)                                           |
----------------------------------------------------------------------------------------
========================================================================================


If you don’t want to be prompted during use, you can download them in advance by using the terran checkpoint download command. For instance, to download the pose estimation checkpoint, you would run terran checkpoint download 11a769ad, where 11a769ad is the ID shown in parentheses.

Note that for every algorithm, Terran provides a function-based version that allows minimal customization (such as face_detection) and a class-based version that can be configured as needed, allowing you to change detection thresholds, internal image resizing, batching, and such. The functions above are actually instantiations of these classes with default settings. We only touched upon the shortcut functions-based versions here, so be sure to check usage/algorithms for more information.

## Visualizing the results¶

It’s difficult to make sense of the results just by looking at the coordinates, so Terran also offers some visualization utilities to draw markers over images for faces and poses, through vis_faces and vis_poses, respectively.

The usage is straightforward, just pass in the image and the detections straight out of Terran, and you’ll get an image (as a numpy.ndarray) with the markers. You can also use the provided display_image to quickly peek the image:

from terran.vis import display_image, vis_faces

display_image(vis_faces(image, faces))


The results of vis_faces and vis_poses can even be fed directly to VideoWriter.write_frame to visualize videos as well.

## Next steps¶

That’s all there is to start! The documentation is still pretty basic, so if you have any questions, feel free to open an issue on Github.