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This Gist contains files of the PsiPhight app that I developed, which is a web application that is equipped with user-specific dashboards that contain relevant data about the fighter (user) performance. This version of the project is a proof of concept (POC or PoC) in transition to the minimum viable product MVP stage (pilot program).
Raw
00_app_views.md

Description

This file lists some main/core views I designed (low-fidelity, high fidelity, final implementation) and developed with Bootstrap It includes the latest version of the project (24/07/22), and does not have the branding image adapted to it. During the next two weeks, another designer will provide the “branding files” and a UX designer will review the project again. This file will not contain those updates, as it is not a representation of my work.

1. Splash screen

Imgur

2. Sign in/Log in

Imgur

3. Sign up/Register

Imgur

4. Main dashboard with lifetime statistics

Imgur

5. Sessions dashboard

Imgur

Add a session

Imgur

Diagram description

Imgur

Imgur

6. Upload a video

Imgur

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00_README.md

PsiPhight

The Modern Performance Analyzer for Fighters

MHL Application section for the secret GitHub Gist

This secret gist contains a version of the PsiPhight/Queensbury.io app I have been working on during the last 5 months, as a freelance junior software engineer. I, in agreement with the client and project owner, carefully selected and curated files of the Django web app of the project to apply to the Major League Hackathon Fellowship. The full project also requires a Docker container and a data pipeline process that contains the core data science component. However, I cannot share it, even though I participated in the software development process, along with a data scientist.

Organization

This is the organization and relevance of each group of files, based on the first two digits of each filename

00_
Descriptive files: general info about the project.
  • 00_README.md
  • 00_app_views.md
01_
App files: files inside the Django app that are core to the project.
  • 01_admin.py
  • 01_form_validators.py
  • 01_forms.py
  • 01_models.py
  • 01_urls.py
  • 01_util.py
  • 01_views.py
02_
Test files: unit and integration tests.
  • 02_test_apis.py
  • 02_test_calculations.py
  • 02_test_client.py
  • 02_test_expected_processed_output.json
  • 02_test_interface.py
  • 02_test_json_output.json
03_
Templates/UI: related to the user interface and HTML/CSS/Bootstrap and the template engine.
  • 03_base.html
  • 03_single_diagram.html
04_
Client functions: related to the logic and calls to the Vimeo and Google Cloud Storage APIs used.
  • 04_gcloud_functions.py
  • 04_vimeo_functions.py
05_
Supporting files: related to the sample input, sample output, and a Git Hook file for the "git push" command.
  • 05_pre_push
  • 05_sample_input.md
  • 05_sample_output.json

Hope it is easy to follow, and thanks for your time.

PsiPhight is a web application that is equipped with user-specific dashboards that contain relevant data about the fighter performance.

Boxers need more accurate ways of estimating their performance and improving with the help of a technical assessment that provides consistent output for specific training sessions across different timeframes.

This system uses machine learning algorithms to allow for the deep analysis of body mechanics, so that boxers can analyze their performance and the performance of a potential opponent with better accuracy and improve their fighting techniques.

Process involved

  • A video is given as an input
  • For each frame of the video, a computer vision model will help detect the entity closest to the concept of a human and evaluate the activity to generate x,y, and z points that correspond to COCO keypoints or person keypoints localization.
  • The dataset is generated and is used to create figures/graphs that will be seized by user dashboards.
  • The dashboards will be accessible via the web app.

Features

  • User authentication for custom analysis.
  • Create and manage user sessions.
  • Create and manage training videos.
  • Visualize and generate custom statistics per video and session/group of videos.

PsiPhight is a lightweight app based on Machine Learning techniques that permit the analysis of user performance and help generate intuitive dashboards for the user.

Tech

Psiphight uses a number of open source projects to work properly:

  • Python - a high-level, general-purpose programming language.
  • Flask - a micro web framework written in Python.
  • Docker - a set of platform as a service products that use OS-level virtualization to deliver software in packages called containers.
  • Bootstrap - a free and open-source CSS framework directed at responsive, mobile-first front-end web development.
  • Plotly - the front end for ML and data science models.
  • Vertex AI - the single environment for data scientists to complete all of their ML work.

Installation

Psiphight requires Python 3+ to run.

We will walk you through the regular process of installing a Django app:

  1. Clone the repository
git clone https://github.com/Cryptius/psiPhightMain.git
  1. Create your own virtual environment
python -m venv venv
source venv/bin/activate
  1. Install the requirements
pip install -r requirements.txt

  1. Generate a new secret key For this, you can either generate a new one by creating a new Django project and just copying and pasting the secret key of the project (1), or by using a service like Djecrety.

  2. Make your migrations

python manage.py makemigrations
python manage.py migrate
  1. Create a new superuser
python manage.py createsuperuser
  1. Run the server
python manage.py runserver

This command will also trigger a post initialization script that will activate a pre-push git hook that you need to use to run tests before pushing commits to the remote repository.

Development

Want to contribute? Great!

Contact @ahn-nath on how to proceed.

Docker

The Docker container for this project is in this repository.

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01_admin.py
from django.contrib import admin
# Register your models here.
from psiphightApp.models import Session, Video, Stat
# when you go to the admin panel, this tables will be displayed
admin.site.register(Session)
admin.site.register(Video)
admin.site.register(Stat)
Raw
01_form_validators.py
from django.core.exceptions import ValidationError
from django.utils.translation import gettext_lazy as _
from psiphightApp.util import fetch_vimeo_video_data
def validate_not_null(value):
"""
Validator used to check that a valid select option was chosen. The 'None' option is the placeholder option,
therefore, is invalid.
:arg value: option the user selected.
"""
if value is None:
raise ValidationError(
_('%(value)s is not a category. Please select a category'),
params={'value': value},
)
def validate_is_a_vimeo_video(value):
"""
Validator used to check if we received a valid Vimeo URL as input.
:arg value: the Vimeo URL to check.
"""
# make API request to get video data
should_raise_error = True
data = fetch_vimeo_video_data(value)
# if we receive data and a valid id, do not raise an error
if data is not None and data['video_id'] > 0:
should_raise_error = False
# raise error
if should_raise_error:
raise ValidationError(
_('"%(value)s" is not a valid Vimeo video URL. Please try again'),
params={'value': value},
)
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01_forms.py
from django import forms
from django.contrib.auth.forms import UserCreationForm
from django.contrib.auth.models import User
from django.forms import ModelForm
from psiphightApp.form_validators import validate_not_null
from psiphightApp.models import Session, Video
class RegistrationForm(UserCreationForm):
# define fields and restrictions
email = forms.EmailField(
max_length=100,
required=True,
help_text='Enter Email Address',
widget=forms.TextInput(attrs={'class': 'form-control form-control-user', 'placeholder': 'Email',
'aria-describedby': 'emailHelp'}),
)
first_name = forms.CharField(
max_length=100,
required=True,
help_text='Enter First Name',
widget=forms.TextInput(attrs={'class': 'form-control form-control-user', 'placeholder': 'First Name'}),
)
last_name = forms.CharField(
max_length=100,
required=True,
help_text='Enter Last Name',
widget=forms.TextInput(attrs={'class': 'form-control form-control-user', 'placeholder': 'Last Name'}),
)
username = forms.CharField(
max_length=200,
required=True,
help_text='Enter Username',
widget=forms.TextInput(attrs={'class': 'form-control form-control-user', 'placeholder': 'Username'}),
)
password1 = forms.CharField(
help_text='Enter Password',
required=True,
widget=forms.PasswordInput(attrs={'class': 'form-control form-control-user', 'placeholder': 'Password'}),
)
password2 = forms.CharField(
required=True,
help_text='Enter Password Again',
widget=forms.PasswordInput(attrs={'class': 'form-control form-control-user', 'placeholder': 'Password Again'}),
)
class Meta:
model = User
fields = (
'username',
'first_name',
'last_name',
'email',
'password1',
'password2'
)
def save(self, commit=True):
# set user and do not save until I add the 'custom fields'
user = super(RegistrationForm, self).save(commit=False)
user.first_name = self.cleaned_data['first_name']
user.last_name = self.cleaned_data['last_name']
user.email = self.cleaned_data['email']
if commit:
user.save()
return user
class SessionForm(ModelForm):
# define fields and restrictions
session_id = forms.IntegerField(
required=False,
initial=None,
label=False,
widget=forms.NumberInput(
attrs={'class': 'form-control form-control-user mb-3 d-none'}),
)
session_name = forms.CharField(
min_length=3,
max_length=100,
required=True,
widget=forms.TextInput(
attrs={'class': 'form-control form-control-user mb-3', 'placeholder': 'Enter the identifier here'}),
)
session_category = forms.ChoiceField(
required=True,
widget=forms.Select(attrs={'class': 'form-control form-control-user mb-3'}),
choices=[
(None, 'Select a category'),
(0, 'Shadowboxing'),
(1, 'Heavy bag'),
(2, 'Sparring (coming soon)'),
],
# we use this validator to make the user to select a non-null option
validators=[validate_not_null]
)
class Meta:
model = Session
fields = (
'session_name',
'session_category'
)
def save(self, commit=True):
# set session and do not save until I add the 'custom fields'
session = super(SessionForm, self).save(commit=False)
session.session_name = self.cleaned_data['session_name']
session.session_category = self.cleaned_data['session_category']
if commit:
session.save()
return session
class VideoForm(ModelForm):
# on init/form creation, we want to use a custom parameter to create our form
def __init__(self, *args, **kwargs):
user_id = kwargs.pop('user_id')
super(VideoForm, self).__init__(*args, **kwargs)
self.fields['session_id'] = forms.ModelChoiceField(
label=False,
required=True,
empty_label="Select a session",
queryset=Session.objects.filter(user_id=user_id),
widget=forms.Select(attrs={'class': 'form-control form-control-user mb-3'}),
)
# define fields and restrictions
video_content = forms.FileField(label=False, required=True, widget=forms.FileInput(
attrs={'class': 'form-control mb-3 py-3 border-0'}))
video_name = forms.CharField(
label=False,
min_length=3,
max_length=100,
required=True,
widget=forms.TextInput(
attrs={'class': 'form-control form-control-user mb-3', 'placeholder': 'Video name'}),
)
class Meta:
model = Video
fields = (
'video_content',
'video_name',
'session_id',
)
def save(self, commit=True):
# set video and do not save until I add the 'custom fields'
video = super(VideoForm, self).save(commit=False)
video.video_content = self.cleaned_data['video_content']
video.video_name = self.cleaned_data['video_name']
if commit:
video.save()
return video
Raw
01_models.py
from datetime import datetime
from django.contrib.auth.models import User
from django.db import models
# Create your models here.
class Session(models.Model):
"""
The Session model/object is responsible for storing relevant data about a group of videos that are associated between
them.
Session
/session_name
/session_category
/created_date
/user_id
"""
session_name = models.CharField(max_length=100)
session_category = models.IntegerField()
created_date = models.DateTimeField(auto_now_add=True, blank=True)
user_id = models.ForeignKey(User, default=None, on_delete=models.CASCADE)
def __str__(self):
return '%s %s' % (self.session_name, self.created_date)
class Video(models.Model):
"""
The Video model/object is responsible for storing data associated to the primary source of statistics or the
most important input of the app.
Video
/video_name
/video_content
/video_stats
/created_date
/session_id
"""
video_name = models.CharField(max_length=100)
video_content = models.CharField(max_length=200)
created_date = models.DateTimeField(auto_now_add=True, blank=True)
session_id = models.ForeignKey(Session, default=None, on_delete=models.CASCADE)
video_duration = models.IntegerField(default=0)
user_id = models.ForeignKey(User, default=None, on_delete=models.CASCADE, null=True)
def __str__(self):
return '%s %s' % (self.video_name, self.created_date)
class Stat(models.Model):
"""
The Stat object/model will contain the stats associated to a particular Video.
Stat
/stats_1
/stats_2
/json_original
/avg_punches_min
/head_off_centerline
/video_id
/session_id
/created_date
/user_id
"""
stats_1 = models.TextField()
stats_2 = models.JSONField()
json_original = models.JSONField()
avg_punches_min = models.IntegerField(default=0)
head_off_centerline = models.FloatField(default=0.0)
video_id = models.ForeignKey(Video, default=None, on_delete=models.CASCADE)
session_id = models.ForeignKey(Session, default=None, on_delete=models.CASCADE)
created_date = models.DateTimeField(auto_now_add=True, blank=True)
user_id = models.ForeignKey(User, default=None, on_delete=models.CASCADE, null=True)
def __str__(self):
return 'Stats of %s' % self.video_id
Raw
01_urls.py
# psiPhightMain/psiphightApp/urls.py
from django.contrib.auth.views import LoginView
from django.urls import path
from . import views
urlpatterns = [
# views.method-name
path('', views.splash_screen, name="home"),
path('login/', LoginView.as_view(redirect_authenticated_user=True), name='login'),
path('signup/', views.register, name="signup"),
path('main_dashboard/', views.main_dashboard, name="main_dashboard"),
path('sessions/', views.sessions, name="sessions"),
path('sessions/<int:session_id>', views.sessions, name="sessions_id"),
path('sessions/delete/<int:session_id>', views.delete_session),
path('single_session/<int:session_id>', views.single_session, name="single_session"),
path('single_diagram/<int:video_id>', views.single_diagram, name="single_diagram"),
path('upload_video/', views.upload_video, name="upload_video"),
path('blank/', views.blank, name="blank"),
path('video_stats', views.video_stats, name="video_stats")
]
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01_util.py
from statistics import mean
import numpy as np
import plotly.graph_objects as go
import requests
# Plotly
from django.db.models import Sum
from plotly.offline import plot
from psiphightApp.models import Stat, Video, Session
def get_number_of_punches(json_object):
"""
We get a JSON object and inspect each frame dictiory inside of it to find and count punches.
The total number is returned.
:param json_object to inspect.
:return: total number of punches found after iterating through dictionary.
"""
# number of punches found
punch_count = 0
# iterate through dictionary and check if we have a punch in the frames
for key in json_object:
frame_object = json_object[key]
offense = False if frame_object['offense'] == 'False' else frame_object['offense']
defense = False if frame_object['defense'] == 'False' else frame_object['defense']
# if we have a punch in frame, count and store value
if offense or defense:
punch_count += 1
return punch_count
def fetch_vimeo_video_data(video_url):
"""
Given a video URL, we fetch a JSON object with metadata and related details to it on Vimeo.
:param video_url: video_url to fetch the data for.
:return: JSON response <data> of the video as a Python dictionary. None if the request is unsuccessful or if the
request body does not contain the 'video_id' key.
"""
# params
vimeo_base_url = "https://vimeo.com/api/oembed.json?url=";
url = vimeo_base_url + video_url
# make API call
r = requests.get(url)
# check if the response was successful
if r.status_code == 200:
# body response
data = r.json()
if data is not None and data['video_id'] > 0:
return data
return None
# -- helper methods --
def generate_test_plotly_divs():
"""
We use generic numbers to produce a Plotly diagram object that we can test with an HTML display.
:return: generic Plotly diagram as an HTML object (div).
"""
# args
x = [i for i in range(-10, 11)]
y2 = [i ** 2 for i in x]
labels = ['Oxygen', 'Hydrogen', 'Carbon_Dioxide', 'Nitrogen']
values = [4500, 2500, 1053, 500]
# figures
fig1 = go.Figure(data=go.Scatter(x=x, y=y2, mode='markers'))
fig2 = go.Figure(data=go.Scatter(x=x, y=y2))
fig3 = go.Figure(data=[go.Pie(labels=labels, values=values)])
fig4 = go.Figure(data=[go.Histogram(x=x)])
# setting layout of the figure.
layout = {
'title': 'Title of the figure',
'xaxis_title': 'X',
'yaxis_title': 'Y',
'height': 360,
'width': 700,
}
# getting HTML needed to render the plot.
plot_div_1 = plot({'data': fig1, 'layout': layout},
output_type='div')
plot_div_2 = plot({'data': fig2, 'layout': layout},
output_type='div')
plot_div_3 = plot({'data': fig3, 'layout': layout},
output_type='div')
plot_div_4 = plot({'data': fig4, 'layout': layout},
output_type='div')
return [plot_div_1, plot_div_2, plot_div_3, plot_div_4]
# moving average implementation that uses Numpy
def moving_average(a, n=60):
"""
Calculate the moving average of an arbitrary iterable, such as a list, over
n time window.
:param a = an iterable of any length, a Python list is fine
:param n = size of the time window; default is 60, which represents two seconds in
the sample video with a nominal FPS of 30.
:return a Numpy array.
"""
N = len(a)
return np.array([np.mean(a[i:i + n]) for i in np.arange(0, N - n + 1)])
# method we will use for single video/single case
def generate_plotly_diagram_by_area(area_measures, is_single_case, N=60):
"""
This method is responsible for creating a diagram for the foundational stat data received, via Plotly.
@:arg: area_measures: set of values to display.
@:arg: is_single_case: it evaluates if the value to render should be smoothed out first or filtered.
"""
# calculate moving average for area
measurement_ma = moving_average(area_measures, N)
if is_single_case:
# to smooth out
measurement_ma = [mean(measurement_ma) for _ in range(N)] + list(measurement_ma)
# plot with Plotly
fig = go.Figure()
# create and style traces
fig.add_trace(go.Scatter(y=area_measures, name='Your values',
line=dict(color='firebrick', width=4)))
fig.add_trace(go.Scatter(y=measurement_ma, name='Moving average',
line=dict(color='royalblue', width=4)))
# edit the layout
fig.update_layout(title='Average value in the video in the video',
xaxis_title='Frame',
yaxis_title='Value')
return plot({'data': fig},
output_type='div')
def generate_plotly_pie_punches(labels, values):
"""
Generate a pie chart diagram, with Plotly.
@:arg labels: labels to use for each section of the pie chart.
@:arg values: proportions for the sections.
@:return plot: Plotly diagram as an HTML content.
"""
# figure
fig = go.Figure(data=[go.Pie(labels=labels, values=values)])
# edit the layout
fig.update_layout(title='Punches distribution')
# out
return plot({'data': fig},
output_type='div')
def generate_plotly_diagrams_areas(json_objects, is_single_case):
# init
plotly_divs_array = []
base_measures = []
guard_measures = []
head_measures = []
all_punches = {}
# prepare all punches dict
for key in json_objects[0]['punches'].keys():
all_punches[key] = 0
# aggregate data in all dictionaries
for video_stat in json_objects:
base_measures += video_stat['base']
guard_measures += video_stat['guard']
head_measures += video_stat['head_off_centerline']
all_punches = {key: all_punches[key] + video_stat['punches'][key] for key in video_stat['punches']}
all_areas = [base_measures, guard_measures, head_measures]
# foundational measurements graphs
for area in all_areas:
plot_html = generate_plotly_diagram_by_area(area, is_single_case)
plotly_divs_array.append(plot_html)
# punches graph
keys, values = list(all_punches.keys()), list(all_punches.values())
plot_pie = generate_plotly_pie_punches(keys, values)
plotly_divs_array.append(plot_pie)
# out
return plotly_divs_array
def process_angle_measurements(json_dictionary):
"""
Method responsible for taking a json dictionary with raw data and transforming it to be used by Plotly diagrams.
The transformed data is eventually saved to the database as part of a Stat object. We process it to make the
displaying step easier and not have to handle heavy processing each time the user wants to view a particular set
of data that will remain the same until updated via an HTTP request.
:param json_dictionary: dictionary with data to be transformed.
:return: dictionary with collected/grouped angle items.
"""
# init: prepare variables
data_json = json_dictionary['keypoints']
angle_labels = ["right_sh_el", "left_sh_el", "torso_delta", "head_delta", "right_hip_ft", "left_hip_ft"]
updated_json_object = {}
base_measures = []
guard_measures = []
head_measures = []
punches = {}
punch_count = 0
# initialize each key-pair with empty array
for angle in angle_labels:
updated_json_object[angle] = []
# traverse each dict in root dict and extract relevant values:
for key, curr_dict in data_json.items():
# (1) get relevant angles per label
for label in angle_labels:
updated_json_object[label].append(curr_dict['angles'][label])
# (2) get foundational measurements
# 1: get the base
base_measures.append(curr_dict['base'])
# 2: get the guard
guard_measures.append(curr_dict['guard'])
# 3: get the head off centerline
head_measures.append(curr_dict['head_off_centerline'])
# (3) Get punches measurements: count and classification
offense = False if curr_dict['offense'] == 'False' else curr_dict['offense']
defense = False if curr_dict['defense'] == 'False' else curr_dict['defense']
# if we have a punch in frame, count and store value
if offense or defense:
punch_count += 1
punch_value = offense if offense else defense
punches[punch_value] = punches.get(punch_value) + 1 \
if punches.get(punch_value) else 1
# update values in original json object (Python dictionary now)
updated_json_object['base'] = base_measures
updated_json_object['guard'] = guard_measures
updated_json_object['head_off_centerline'] = head_measures
updated_json_object['total_punches'] = punch_count
updated_json_object['punches'] = punches
# out
return updated_json_object
def generate_general_stats_dict(user, session=None, video=None, is_video=False):
"""
Given a set of filter to apply, this method will generate a JSON object with descriptive stats to display for
each type of view.
:param user: arg to filter by user.
:param session: arg to filter by session.
:param video: arg to filter by video.
:param is_video: helps understand is certain filters should be omitted.
:return: JSON object with filtered stats.
"""
# dictionary object to contain stats
first_row_stats = {}
# Stat 1: number of sessions/videos
if is_video:
num_elements = 0
# if we did not receive a video id, get number of sessions (if not video given)
else:
num_elements = Session.objects.filter(user_id=user).count()
# if session to filter by given, get number of videos instead
if session:
num_elements = Video.objects.filter(session_id=session).count()
# Stat 2: get total number of video minutes processed
video_proc_query = Video.objects.filter(user_id=user)
# if session to filter by, get all the videos of a session
if session:
video_proc_query = video_proc_query.filter(session_id=session)
video_min_proc_total = video_proc_query.aggregate(Sum('video_duration'))['video_duration__sum']
# if video to filter by, only get the single video duration
if video:
video_min_proc_total = video.video_duration
# save value if we 'video_min_proc_total' is not None, otherwise, set to 0 to have valid data to present
video_min_proc = video_min_proc_total if video_min_proc_total is not None else 0
# Stat 3: avg number of punches per min
stats_objects = Stat.objects.filter(user_id=user)
# if session to filter by, get all stats of a session
if session:
stats_objects = stats_objects.filter(session_id=session)
# if video to filter by, only get stats for a video
if video:
stats_objects = stats_objects.filter(video_id=video)
# sum all averages (of punches per min) and divide by total number of items
stats_count = stats_objects.count()
video_punches_avg_sum = stats_objects.aggregate(Sum('avg_punches_min'))['avg_punches_min__sum']
avg_punches_min = video_punches_avg_sum // stats_count \
if (video_punches_avg_sum is not None and stats_count is not None) else 0
# Stat 4: avg head off centerline
query_head_off = Stat.objects.filter(user_id=user)
# if session to filter by, get all stats of a session
if session:
query_head_off = query_head_off.filter(session_id=session)
# calculate the variance among all items in list
avg_head_off_centerline_list = query_head_off.values_list('head_off_centerline', flat=True)
avg_head_off_centerline = np.var(avg_head_off_centerline_list)
# if video to filter bt, only get its head off centerline
if video:
avg_head_off_centerline = Stat.objects.get(video_id=video).head_off_centerline
# assign
first_row_stats['num_elements'] = num_elements
first_row_stats['video_min_proc'] = video_min_proc
first_row_stats['avg_punches_min'] = avg_punches_min
first_row_stats['avg_head_off_centerline'] = "{:.2f}".format(avg_head_off_centerline)
# out
return first_row_stats
Raw
01_views.py
import json
import traceback
from datetime import datetime
import numpy as np
from django.contrib import messages
from django.contrib.auth import login
from django.contrib.auth.decorators import login_required
from django.contrib.auth.views import PasswordResetView
from django.contrib.messages.views import SuccessMessageMixin
from django.core.exceptions import ValidationError
from django.core.files.storage import FileSystemStorage
from django.db.models import Sum
from django.http import HttpResponse, HttpResponseBadRequest, HttpResponseNotFound
from django.shortcuts import render, redirect
from django.urls import reverse, reverse_lazy
from django.views.decorators.csrf import csrf_exempt, requires_csrf_token
from resources.vimeo_functions import upload_vimeo_video, get_video_duration, remove_file
from .form_validators import validate_is_a_vimeo_video
from .forms import RegistrationForm, SessionForm, VideoForm
# Create your views here.
from .models import Session, Video, Stat
from .util import process_angle_measurements, generate_plotly_diagrams_areas
# ###### ---- Helper/controller methods ---- ######
def generate_query_diagrams_view_case(user_id, session_id, video_id, is_single_case):
"""
Method to generate Plotly divs based on view requirements or the view making the call.
:param user_id: user object needed to filter data.
:param session_id: session object needed to filter data.
:param video_id: video object needed to filter data.
:param is_single_case: variable that helps us decide if to filter or alter the results of a calculation.
:return: plotly divs: the divs to use for the HTML templates.
"""
# plotly to generate
plotly_divs = []
# query to construct
query = None
# filter records by user
if user_id:
query = Stat.objects.all().filter(user_id=user_id)
# filter records by session
if session_id:
query = query.filter(session_id=session_id)
# filter records by video
if video_id:
query = query.filter(video_id=video_id)
# filter statistics with after creating query
all_stats = query.values_list('stats_2', flat=True)
# generate plotly divs with expected input (stats)
if all_stats:
# output
plotly_divs = generate_plotly_diagrams_areas(all_stats, is_single_case)
return plotly_divs
def error_404_view(request, exception):
"""
Method to handle 404 error
"""
return render(request, 'psiPhightApp/404.html')
def splash_screen(request, **kwargs):
"""
Loads the template and provides the contexts when they are passed as arguments
"""
print(request.content_params)
return render(request, "psiPhightApp/home.html")
def register(request):
"""
Register user and generate register view.
"""
# if the user is already authenticated, redirect
if request.user.is_authenticated:
return redirect(reverse('home'))
# create a new form with clean data, to use for GET requests
form = RegistrationForm()
# when we receive a POST request, we want to save details, including errors
if request.method == 'POST':
form = RegistrationForm(request.POST)
print('received post - form +', request.POST)
if form.is_valid():
# if valid, save a log user in to simplify process
print('feeling good, now check it on admin')
user = form.save()
login(request, user)
messages.success(request, "Registration successful!")
return redirect(reverse('home'))
else:
print(form.errors)
args = {'form': form}
return render(request, 'registration/register.html', args)
class ResetPasswordView(SuccessMessageMixin, PasswordResetView):
"""
Class-based view use for the reset password activity.
"""
template_name = 'registration/password_reset.html'
email_template_name = 'registration/password_reset_email.html'
subject_template_name = 'registration/password_reset_subject.txt'
success_message = "We've emailed you instructions for setting your password, " \
"if an account exists with the email you entered. You should receive them shortly." \
" If you don't receive an email, " \
"please make sure you've entered the address you registered with, and check your spam folder."
success_url = reverse_lazy('login')
@login_required
def main_dashboard(request):
"""
View demonstrating how to display a graph object
on a web page with Plotly.
"""
# Init
args = {
'message': 'No data available yet. Upload your training videos and generate data for them.',
'plotly_divs': None,
'sessions': None
}
# user making request
user = request.user
# Generate stats/data to be presented
# First row stats/general stats
first_row_stats = generate_general_stats_dict(user)
# table: get sessions items to render to frontend
sessions_data = Session.objects.filter(user_id=user)
args['sessions'] = sessions_data
args['first_row_stats'] = first_row_stats
# get all videos stats by user
plotly_divs = generate_query_diagrams_view_case(user, None, None, False)
args['plotly_divs'] = plotly_divs
args['slider_titles'] = ["Base", "Guard", "Head off centerline", "Punches"]
return render(request, "psiPhightApp/index.html", args)
@login_required
def sessions(request):
"""
Generate sessions view and also handles POST requests to:
- Update session by id.
- Create a new session.
@:param session_id: session id for the data to be retrieved for the main session. Defaults to the first
object in database in None received.
"""
# user making the request
user = request.user
plotly_array = []
slider_titles = []
# if this is a POST request we need to process the form data
if request.method == 'POST':
# create a form instance and populate it with data from the request:
form = SessionForm(request.POST)
# check whether it's valid:
if form.is_valid():
form_session_id = form.cleaned_data['session_id']
print(form_session_id)
# if we did not receive an id, create new object
if form_session_id is None:
print('creating new session')
form.instance.created_date = datetime.now()
form.instance.user_id = user
form.save()
messages.success(request, "The session was saved!")
# if we received an id, update object
else:
print('updating session with id', form_session_id)
form_session_name = form.cleaned_data['session_name']
form_session_category = form.cleaned_data['session_category']
Session.objects.filter(pk=form_session_id).update(session_name=form_session_name,
session_category=form_session_category)
# redirect to a new URL:
return redirect(reverse('sessions'))
# if a GET (or any other method) we'll create a blank form
else:
form = SessionForm()
# get sessions items to render to frontend
sessions_data = Session.objects.all().filter(user_id=user)
# get video items to render to frontend
try:
# main slider args
selected_session = Session.objects.latest('created_date')
plotly_array = generate_query_diagrams_view_case(user, selected_session, None, False)
slider_titles = ["Base", "Guard", "Head off centerline", "Punches"]
except (Session.DoesNotExist, AttributeError) as e:
print('Exception received: {} '.format(e))
print('Error traceback: {}'.format(traceback.format_exc()))
selected_session = None
args = {
"sessions": sessions_data,
"plotly_divs": plotly_array,
"slider_titles": slider_titles,
"sessions_form": form,
"selected_session": selected_session
}
return render(request, "psiPhightApp/sessions.html", args)
@login_required
def single_session(request, session_id=-1):
"""
Generate single sessions.
"""
args = {}
template = "psiPhightApp/blank.html"
# user making the request
user = request.user
try:
# retrieve Session instance, if it exists
selected_session = Session.objects.get(pk=session_id)
plotly_array = generate_query_diagrams_view_case(user, selected_session, None, False)
slider_titles = ["Base", "Guard", "Head off centerline", "Punches"]
# videos table
videos_by_session = Video.objects.all().filter(session_id=selected_session)
# First row stats/general stats
first_row_stats = generate_general_stats_dict(user, selected_session, None)
# if we have data to show
if plotly_array:
# set arguments for stats
args = {
'plotly_divs': plotly_array,
'slider_titles': slider_titles,
'videos': videos_by_session,
'first_row_stats': first_row_stats
}
template = "psiPhightApp/single_session.html"
else:
# value error: the data is not valid
raise ValueError("The stats data is empty or not valid")
# video not found exception
except Video.DoesNotExist as ve:
print('Video does not exist. The exception was: {}', ve)
args = {'message': 'The video you specified does not exist'}
# stats not available for selected video
except (Stat.DoesNotExist, ValueError) as se:
print('Stats does not exist or the data is invalid. The exception was: {}', se)
args = {'message': 'The statistics for the video you specified are not available'}
except Exception as e:
print(e)
print(traceback.format_exc())
finally:
return render(request, template, args)
def delete_session(request, session_id):
"""
Deletes a Session object from the database by id.
@:param session_id: session id to delete.
"""
session_id = int(session_id)
try:
session_sel = Session.objects.get(id=session_id)
except Session.DoesNotExist:
return redirect('sessions')
session_sel.delete()
return redirect('sessions')
@login_required
def single_diagram(request, video_id=-1):
"""
Generate single diagram.
"""
args = {}
template = "psiPhightApp/blank.html"
user = request.user
try:
# retrieve Video instance, if exists
# find Stats object by video id, if it exists
selected_video = Video.objects.get(pk=video_id)
stat_object = Stat.objects.get(video_id=selected_video)
# define arguments
# main slider
plotly_array = generate_query_diagrams_view_case(user, selected_video.session_id, selected_video, True)
slider_titles = ["Base", "Guard", "Head off centerline", "Punches"]
final_output_video = stat_object.stats_1
# first row stats
first_row_stats = generate_general_stats_dict(user, None, selected_video, True)
# if we have data to show
if plotly_array and final_output_video:
# set arguments for stats
args = {
'plotly_divs': plotly_array,
'3D_reproduction_video': final_output_video,
'slider_titles': slider_titles,
'first_row_stats': first_row_stats
}
template = "psiPhightApp/single_diagram.html"
else:
# value error: the data is not valid
raise ValueError("The stats data is empty or not valid")
# video not found exception
except Video.DoesNotExist as ve:
print(traceback.format_exc())
print('Video does not exist. The exception was: {}', ve)
args = {'message': 'The video you specified does not exist'}
# stats not available for selected video
except (Stat.DoesNotExist, ValueError) as se:
print(traceback.format_exc())
print('Stats does not exist or the data is invalid. The exception was: {}', se)
args = {'message': 'The statistics for the video you specified are not available'}
except Exception as e:
print(e)
print(traceback.format_exc())
finally:
return render(request, template, args)
@login_required
def upload_video(request):
"""
Generate the 'upload a video' view.
"""
# retrieve all sessions by user, convert their name and id to tuples we can use for the select options
user = request.user
# create a form instance and populate it with data from the request:
form_video = VideoForm(user_id=user)
form_session = SessionForm()
# if this is a POST request we need to process the form data
if request.method == 'POST':
print('we are receiving a POST request')
# check if we received a request for creating a video
if "upload_a_video_submit" in request.POST:
# create a form instance and populate it with data from the request:
form = VideoForm(request.POST, request.FILES, user_id=user)
# check whether it's valid:
if form.is_valid():
print('upload a video - is valid')
# upload video to Vimeo and save the URL
upload_file = request.FILES['video_content']
fs = FileSystemStorage()
filename = fs.save(upload_file.name, upload_file)
vn = 'media/{}'.format(filename)
vimeo_url = upload_vimeo_video(vn)
video_duration_min = get_video_duration(vn)
remove_file(filename)
if vimeo_url is not None and video_duration_min is not None:
# save and update Video data
form.instance.video_duration = video_duration_min
form.instance.video_content = vimeo_url
form.instance.user_id = user
# save details
form.save()
form_video = VideoForm(user_id=user)
messages.success(request, "The video details were saved!")
else:
messages.error(request, "We could not upload your video to Vimeo. Please try again")
# else, check if we receive a request to create a session
elif "sessions_submit" in request.POST:
# create a form instance and populate it with data from the request:
form = SessionForm(request.POST)
# check whether it's valid:
if form.is_valid():
print('creating new session')
form.instance.created_date = datetime.now()
form.instance.user_id = user
form.save()
messages.success(request, "The session was saved!")
# redirect to a new URL:
return redirect(reverse('upload_video'))
args = {
"upload_a_video_form": form_video,
"sessions_form": form_session
}
return render(request, "psiPhightApp/upload_video.html", args)
def blank(request):
"""
Generate a blank view.
"""
return render(request, "psiPhightApp/blank.html")
@csrf_exempt
@requires_csrf_token
def video_stats(request):
"""
Process JSON object with data after VideoPose3D inference and Google Cloud functions work. Prepare it for
future display and add it as a Stat object
@:return create Stat object
@:param HTTP request
"""
try:
# receive JSON object
json_object = json.loads(request.body)
# arguments
video_id = json_object['video_id']
vimeo_video_url = json_object['vimeo_video_url']
vimeo_video_url_id = vimeo_video_url.split('/')[-1]
# validation
video_target = Video.objects.get(pk=video_id) # check the video exist
validate_is_a_vimeo_video(vimeo_video_url) # check the vimeo url is valid
user_associated = video_target.user_id
stat_video_already_taken = Stat.objects.filter(video_id=video_target).exists()
# if there is a Stat object using the video id, throw an error
if stat_video_already_taken:
raise ValueAlreadyInUseError('Already in use')
# extract and transform angles
json_object_updated = process_angle_measurements(json_object)
punches_count = json_object_updated['total_punches']
# avg punches per min: multiply number of punches by total number of minutes the video is
avg_punches_min = punches_count / video_target.video_duration
# variance for head off centerline
variance_head_off_centerline = np.var(json_object_updated['head_off_centerline'])
# get session to attach
session = video_target.session_id
# save to Stats models and persist data
saved_stats = Stat.objects.create(stats_1=vimeo_video_url_id, stats_2=json_object_updated,
json_original=json_object,
avg_punches_min=avg_punches_min,
head_off_centerline=variance_head_off_centerline, video_id=video_target,
session_id=session,
user_id=user_associated)
# return HTTP response
return HttpResponse("The stats object was successfully created: \n {}".format(saved_stats))
except ValueAlreadyInUseError as valid_e:
print(traceback.format_exc())
print(valid_e)
return HttpResponseBadRequest("The video id is already in use")
except (KeyError, ValueError) as input_error:
print(traceback.format_exc())
print(input_error)
return HttpResponseBadRequest("The keys or values are not permitted or there is something wrong with the "
"request body. Please check them and try again")
except (Video.DoesNotExist, ValidationError) as valid_data_error:
print(traceback.format_exc())
print(valid_data_error)
return HttpResponseNotFound("We could not find the video id you referenced or its Vimeo URL link. Verify that "
"you are referencing an existing Vimeo video or the right video object")
Raw
02_test_apis.py
import os
import time
from unittest import TestCase
import requests
import vimeo
# Vimeo API
token = os.environ['ACCESS_TOKEN_VIMEO']
client_id = os.environ['CLIENT_ID_VIMEO']
client_secret = os.environ['CLIENT_SECRET_VIMEO']
class APITests(TestCase):
"""
The class tests the dependencies and APIs the project uses. Specifically, it tests the credentials of each
dependency and if the API is functional/if successful API can me made.
"""
# external variable
counter = 0
@classmethod
def setUpClass(cls):
# setting counter to limit the number of times we attempt to re-make an API call after a timeout.
cls.counter = 0
def setUp(self):
# setup run before every test method.
self.client = vimeo.VimeoClient(
token=token, # '{access_token}',
key=client_id, # '{client_id}',
secret=client_secret # '{client_secret}'
)
def test_vimeo_api_credentials(self):
"""
Used to test that the API is working and the client was configured correctly.
:param uri_vimeo: URI to use for testing.
:return: result of comparing 200 code (success request) with actual status code received.
url: https://api.vimeo.com/tutorial
"""
print("Method: test_vimeo_api_credentials")
self.counter += 1
try:
# make API call
uri_test = 'https://api.vimeo.com/tutorial'
response = self.client.get(uri_test)
response_code = response.status_code
time.sleep(10)
# assert that we received a 200 (success/OK) code
self.assertEqual(response_code, 200)
except (requests.exceptions.ReadTimeout, requests.exceptions.ConnectTimeout):
# if this is the fifth attempt, return an error response and fail the test, otherwise, try again
if self.counter < 5:
self.test_vimeo_api_credentials()
else:
self.assertTrue(False)
Raw
02_test_calculations.py
import json
from django.test import TestCase
from psiphightApp.util import process_angle_measurements, get_number_of_punches
class CalculationTests(TestCase):
"""
Tests the core logic and calculation methods of the project that are generally used for stats generation.
"""
def setUp(self):
# setup to run before every test method
pass
def test_process_angle_measurements(self):
"""
Test the process angle measurements that proccessed an input json object and updates it after appending
values and performing calculation on them.
:return: result of asserting that the JSON object generated is the same as the expected.
"""
print("Method: test_process_angle_measurements")
# read json files: initial input and expected output
with open('resources/test_json_output.json') as f:
data = json.load(f)
with open('resources/test_expected_processed_output.json') as f:
expected = json.load(f)
# process it
result = process_angle_measurements(data)
# compare expected result vs actual result
self.assertEqual(result, expected)
def test_get_number_of_punches(self):
"""
Test that the method returns the number of punches in JSON object.
:return: the result of asserting that the expected number of detected punches is the same as the returned.
"""
print("Method: test_get_number_of_punches")
# read json files: initial input and expected output
with open('resources/test_json_output.json') as f:
data = json.load(f)
with open('resources/test_expected_processed_output.json') as f:
expected = json.load(f)
# execute, get total number of punches
total = get_number_of_punches(data['keypoints'])
# compare expected result vs actual result
self.assertEqual(total, expected['total_punches'])
Raw
02_test_client.py
# test video_stats
import json
from django.contrib.auth.models import User
from django.test import Client
from django.test import TestCase
from django.urls import reverse
from psiphightApp.models import Session, Video, Stat
from psiphightApp.util import generate_general_stats_dict
class ClientTests(TestCase):
"""
Tests that the core client requests and logic for them are working as expected.
"""
@classmethod
def setUpClass(cls):
super(ClientTests, cls).setUpClass()
# json_original
with open('resources/test_json_output.json') as f:
json_original = json.load(f)
# json_output
with open('resources/test_expected_processed_output.json') as f:
json_output = json.load(f)
cls.json_original = json_original
cls.json_output = json_output
def setUp(self):
self.client = Client()
created_user = User.objects.create(username="test-user_video", password="12345test.")
created_session = Session.objects.create(session_name="test_stat_session", session_category=0,
user_id=created_user)
created_video = Video.objects.create(video_name="test_stat_video_creation", video_content='test_video.mp4',
video_duration=5,
session_id=created_session, user_id=created_user)
Video.objects.create(video_name="test_stat_video", video_content='test_video.mp4',
video_duration=5,
session_id=created_session, user_id=created_user)
Stat.objects.create(stats_1='221016247', stats_2=self.json_output, json_original=self.json_original,
avg_punches_min=40, head_off_centerline=15, video_id=created_video, user_id=created_user,
session_id=created_session)
# test video_stats
def test_video_stats(self):
"""
Test that the client request used for receiving the JSON output after processing a video file is able to
create a Stat object with its data and return a successful response on correct input.
:return: the result of asserting that the request status was 200 and that the Stat object that was created
after making the client request is in accordance with the input.
"""
print("Method: test_video_stats")
# read input and update it
video = Video.objects.get(video_name='test_stat_video')
json_body = self.json_original
json_body['video_id'] = video.pk
# make post request
response = self.client.post(
reverse('video_stats'), json.dumps(json_body), content_type="application/json")
# validate response
status_code = response.status_code
create_stat = Stat.objects.get(video_id=video)
# assertions: we assert that the instace was created and successful
self.assertTrue(status_code, 200)
self.assertTrue(create_stat.video_id, video)
self.assertEqual(create_stat.json_original, json_body)
self.assertEqual(create_stat.avg_punches_min, 0)
# test first row stats
def test_first_row_stats(self):
"""
Tests that the data to be displayed to the stats views are correct calculations and responses to the filters
are being made.
:return: the result of asserting that the expected result/calculations were generated and returned.
"""
print("Method: test_first_row_stats")
# args
video = Video.objects.get(video_name='test_stat_video_creation')
user = User.objects.get(username='test-user_video')
# execute test
first_row_stats = generate_general_stats_dict(user, None, video, True)
# results
self.assertEqual(first_row_stats['num_elements'], 0)
self.assertEqual(first_row_stats['video_min_proc'], 5)
self.assertEqual(first_row_stats['avg_punches_min'], 40)
self.assertEqual(first_row_stats['avg_head_off_centerline'], '15.00')
Raw
02_test_expected_processed_output.json
{
"right_sh_el": [
37.888379632247634,
38.07199670812766,
38.102396833384645
],
"left_sh_el": [
38.811272881072156,
38.88708327801736,
38.880888104886274
],
"torso_delta": [
28.38234958601501,
27.555765181242283,
27.718435116139435
],
"head_delta": [
13.1126774713489,
13.121600841245842,
13.218144476437224
],
"right_hip_ft": [
71.37056034046682,
71.21208205631183,
70.9462606552942
],
"left_hip_ft": [
81.73013681687075,
81.6271816126898,
81.60892580527467
],
"base": [
0.16142725346071798,
0.1637152076995246,
0.16606093935528704
],
"guard": [
0.012211642318488036,
0.0122000203307612,
0.01223619365929929
],
"head_off_centerline": [
12.462954343628851,
12.606850439477018,
12.715490028054182
],
"total_punches": 1,
"punches": {
"jab": 1
}
}
Raw
02_test_interface.py
import time
from telnetlib import EC
from unittest import skip
from django.test import LiveServerTestCase
from selenium import webdriver
from selenium.common.exceptions import NoSuchElementException
from selenium.webdriver import Keys
from selenium.webdriver.common.by import By
from selenium.webdriver.support import expected_conditions as EC
# global variables
from selenium.webdriver.support.select import Select
from selenium.webdriver.support.wait import WebDriverWait
from psiphightApp.models import Video
# selenium setup
from psiphightApp.util import fetch_vimeo_video_data
options = webdriver.ChromeOptions()
options.add_argument("start-maximized");
options.add_argument("disable-infobars")
options.add_argument("--disable-extensions")
selenium = webdriver.Chrome(chrome_options=options, executable_path='C:/Users/Usuario/chromedriver')
# test user
username = 'carlos-123'
password = 'ycZC9NiNHrfwz4@'
# test object for the stats
JSON_object_stats = None # TODO: we need to add an actual dummy JSON object with test data
# common paths
main_url = 'http://127.0.0.1:8000/main_dashboard/'
base_url = 'http://127.0.0.1:8000/'
sessions_url = 'http://127.0.0.1:8000/sessions/'
videos_url = 'http://127.0.0.1:8000/upload_video/'
def login_user():
"""
Method used to make attempt login our default test user.
"""
# initial setup
login_url = 'http://127.0.0.1:8000/accounts/login/'
# go to login url
selenium.get(login_url)
# find the elements you need to submit form
username_input = selenium.find_element(By.ID, 'id_username')
password_input = selenium.find_element(By.ID, 'id_password')
# populate the form with data
username_input.send_keys(username)
password_input.send_keys(password)
submit = selenium.find_element(By.ID, 'id_submit')
# submit form
submit.send_keys(Keys.RETURN)
def logout_user():
"""
Method used to logout our default test user.
"""
# go to main url
selenium.get(main_url)
# click on log out button
selenium.implicitly_wait(10)
selenium.find_element(By.ID, 'dropdown_toggle_profile').click()
log_out_button = selenium.find_element(By.ID, 'logout_button')
log_out_button.click()
def scroll_down_to_bottom():
"""
Method used to scroll down to bottom of page to interact with elements near the footer.
"""
html = selenium.find_element(By.TAG_NAME, 'html')
selenium.implicitly_wait(15)
html.send_keys(Keys.END)
# @skip("skipping to debug")
class AuthTests(LiveServerTestCase):
"""
Test cases corresponding to the user authentication views and activities: register, login, logout.
"""
@staticmethod
def test_unauthenticated_user_has_restricted_access_to_views():
"""
Given a new user wants to explore the web app for the first time, when the user clicks/accesses the main
page, then they should only have access to the login and sign up pages through the user interface.
"""
# initial setup
failures = 0
# base url, allowed urls, disallowed urls
allowed_urls = ['', 'signup/', 'accounts/login/']
disallowed_urls = ['main_dashboard/']
all_urls = allowed_urls + disallowed_urls
# attempt to visit pages user should and should not be allowed to see, check result;
for path in all_urls:
url = "{0}{1}".format(base_url, path)
selenium.get(url)
current_url = selenium.current_url
print("current url is {0}, url we requested is {1}".format(current_url, url))
if path in allowed_urls and current_url != url:
failures += 1
if path in disallowed_urls and current_url == url:
failures += 1
assert failures == 0
@staticmethod
def test_authenticated_user_has_access_to_views():
"""
Given a new user tries to sign up, when the user clicks on send form data, then the system should look up
their email: if it exists, display an error message. else, send them to the login page.
"""
# check that user has no access to the main dashboard
selenium.get(main_url)
print("current url is {0}, url we requested is {1}".format(selenium.current_url, main_url))
assert main_url != selenium.current_url
# login attempt with existing test user
login_user()
# check that user has access to the main dashboard
selenium.get(main_url)
current_url = selenium.current_url
print("current url is {0}, url we requested is {1}".format(current_url, main_url))
assert main_url == selenium.current_url
# log out
logout_user()
@staticmethod
def test_validate_login_form():
"""
Given a user tries to sign in/login, when the user clicks on send form data, then the system should compare
input with stored data: if it does not exist, display an error message. else, send them to the main dashboard
page.
"""
# check that user can log in with right credentials
login_user()
selenium.get(base_url)
username_span = selenium.find_element(By.ID, 'username-span').text
# assertions
assert base_url == selenium.current_url
assert username_span == username
# log out
logout_user()
# @skip("skipping to debug")
class SessionTests(LiveServerTestCase):
"""
This class contains the test cases corresponding to the Session model related views and functionalities.
"""
@staticmethod
def test_view_displays_correct_data():
"""
Given a logged user wants to view (a) session(s), when they click on the sessions page located on the nav bar,
then all session data by that user should be retrieved. If any data, render page with session details. Else,
show the 'empty page' view.
"""
# login test user
login_user()
# initial setup
selenium.get(main_url)
# go to sessions page and assert that the empty block is displayed if there are no sessions to display
sessions_button = selenium.find_element(By.ID, 'sessions-button')
sessions_button.click()
# if we have no sessions to show, assert that we are displaying the empty block
try:
empty_block = selenium.find_element(By.CLASS_NAME, 'dataTables_empty')
empty_alert = selenium.find_element(By.ID, 'empty-alert-block')
if empty_block:
print('entering empty block')
assert sessions_url == selenium.current_url
assert empty_alert
# otherwise, assert we have at least one session to display when we are not showing the empty block
except NoSuchElementException:
print('entering full block')
sessions_rows = selenium.find_elements(By.CLASS_NAME, 'session-data-tr')
assert sessions_url == selenium.current_url
assert len(sessions_rows) > 0
def test_session_can_be_created_updated_and_deleted(self):
# define variables
session_create_name = 'test-session-by-carlos123-create-method'
session_category = '2'
session_update_name = "UPDATE {session_name}".format(session_name=session_create_name)
# login test user, go to sessions url
login_user()
selenium.get(sessions_url)
# create
self.session_can_be_created(session_create_name, session_category)
# update
self.session_can_be_updated(session_update_name)
# delete
self.session_can_be_deleted(session_update_name)
# log out user
logout_user()
@staticmethod
def session_can_be_created(session_name, session_category):
"""
Given a logged user wants to create a new session, when they click on the create button of the ‘sessions’
page, and try to save input details, then the system should attempt to save the details to the database. If
stored, display a success message. else, display an error message.
"""
# initial
session_was_created = False
# find and click on add button
sessions_add_button = selenium.find_element(By.ID, 'addSessionButton')
WebDriverWait(selenium, 10)
sessions_add_button.click()
# find the elements you need to submit form
session_name_input = selenium.find_element(By.ID, 'id_session_name')
session_category_input = selenium.find_element(By.ID, 'id_session_category')
select = Select(session_category_input)
# wait for elements to be clickable and populate the form with data
WebDriverWait(selenium, 20).until(EC.element_to_be_clickable(session_name_input)).send_keys(
session_name)
select.select_by_value(session_category)
submit = selenium.find_element(By.ID, 'id_sessions_submit')
# submit form
submit.send_keys(Keys.RETURN)
# assert that the session was created
sessions_list_names = selenium.find_elements(By.CLASS_NAME, 'session-name')
for e in sessions_list_names:
print('session name is ', e.text)
if e.text == session_name:
session_was_created = True
assert session_was_created
scroll_down_to_bottom()
@staticmethod
def session_can_be_updated(session_name_updated):
"""
Given a logged user wants to update a session, when they click on the update button of the single session
view of the sessions page, then the system should attempt to save the details to the database. If stored,
display a success message. else, display an error message.
"""
# initial setup
session_was_updated = False
# locate session we need to update
session_tr = selenium.find_element(By.CLASS_NAME, 'session-data-tr')
session_dropdown_button = session_tr.find_element(By.CLASS_NAME, 'dropdown-list-session')
WebDriverWait(selenium, 20).until(EC.element_to_be_clickable(session_dropdown_button)).click()
session_update_button = session_tr.find_element(By.CLASS_NAME, 'update-session')
WebDriverWait(selenium, 20).until(EC.element_to_be_clickable(session_update_button)).click()
# populate form with new value
session_name_input = selenium.find_element(By.ID, 'id_session_name')
session_update_button = selenium.find_element(By.ID, 'id_sessions_submit')
WebDriverWait(selenium, 10).until(EC.element_to_be_clickable(session_name_input)).clear()
session_name_input.send_keys(session_name_updated)
session_update_button.click()
# assert that the session was updated
sessions_list_names = selenium.find_elements(By.CLASS_NAME, 'session-name')
for e in sessions_list_names:
print('session name is ', e.text)
if e.text == session_name_updated:
session_was_updated = True
assert session_was_updated
scroll_down_to_bottom()
@staticmethod
def session_can_be_deleted(session_name_to_delete):
"""
Given a logged user wants to delete a session, when they click on the delete button of the single session
view of the sessions page, then the system should attempt to remove the instance from the database. If
removed, display a success message. else, display an error message.
"""
# initial setup
session_was_deleted = True
# locate session we need to update
session_tr = selenium.find_element(By.CLASS_NAME, 'session-data-tr')
session_dropdown_button = session_tr.find_element(By.CLASS_NAME, 'dropdown-list-session')
# open delete modal
WebDriverWait(selenium, 20).until(EC.element_to_be_clickable(session_dropdown_button)).click()
session_delete_button = session_tr.find_element(By.CLASS_NAME, 'delete-session')
session_delete_button.click()
# confirm delete
WebDriverWait(selenium, 20).until(
EC.element_to_be_clickable(selenium.find_element(By.ID, 'deleteSessionButton'))).click()
# assert that the session was deleted
sessions_list_names = selenium.find_elements(By.CLASS_NAME, 'session-name')
for e in sessions_list_names:
print('session name is ', e.text)
if e.text == session_name_to_delete:
session_was_deleted = False
assert session_was_deleted
# @skip("skipping to debug")
class VideosTests(LiveServerTestCase):
"""
This class contains the test cases corresponding to the Video model related views and functionalities.
"""
@staticmethod
def test_video_can_be_created():
"""
Given a logged user wants to create a new video, when they click on the create button of ‘upload a video’
page, and try to save input details, then the system should attempt to save the details to the database. If
stored, display a success message. else, display an error message. The user can only create/upload a video
if at least one session has been created. In this sense, videos have a dependent relationship with sessions.
"""
# login user
login_user()
# create a session
session_create_name = 'test-session-by-carlos123-create-video-method'
session_category = '2'
selenium.get(sessions_url)
SessionTests.session_can_be_created(session_create_name, session_category)
# go to upload a video view
selenium.get(videos_url)
# initial values to input
vimeo_video_url = 'https://vimeo.com/695814554'
video_name = 'video-name-carlos123-test'
video_session_index = 1
# assert we have a valid video url
data = fetch_vimeo_video_data(vimeo_video_url)
video_id = data['video_id']
assert data and video_id > 0
# create video
video_url_input = selenium.find_element(By.ID, 'id_video_content')
video_name_input = selenium.find_element(By.ID, 'id_video_name')
video_session_input = selenium.find_element(By.ID, 'id_session_id')
select_session_id = Select(video_session_input)
# fill form
WebDriverWait(selenium, 10).until(EC.element_to_be_clickable(video_url_input)).clear()
video_url_input.send_keys(vimeo_video_url)
WebDriverWait(selenium, 10).until(EC.element_to_be_clickable(video_name_input)).clear()
video_name_input.send_keys(video_name)
select_session_id.select_by_index(video_session_index)
# submit form
submit = selenium.find_element(By.ID, 'id_upload_a_video_submit')
submit.send_keys(Keys.RETURN)
# assert that the video is created by going to the 'sessions' page after creation
video_success_alert = selenium.find_elements(By.CLASS_NAME, 'alert-success')
assert video_success_alert
# final updates
selenium.get(sessions_url)
scroll_down_to_bottom()
time.sleep(10)
SessionTests.session_can_be_deleted(session_create_name)
logout_user()
class StatsTests(LiveServerTestCase):
"""
This class contains the test cases corresponding to the Video model statistics related views and
functionalities.
"""
@skip("The functionality is not complete")
def test_stats_view_displays_correct_data(self):
"""
Given a logged user wants to view their statistics, when they go to the main dashboard or single dashboard,
then the statistics corresponding to each case should be retrieved, If any data, render the page with
statistics. Else, show the 'empty page' view.
"""
# login test user
login_user()
# initial setup
selenium.get(main_url)
# validate that there are no videos to display
videos = Video.objects.all()
if videos:
# delete all sessions
Video.objects.all().delete()
# go to sessions page and assert that the empty block is displayed
video_card = selenium.find_element(By.ID, 'video-frames')
video_empty_block = video_card.find_element(By.ID, 'videos-empty-block')
assert videos_url == selenium.current_url
assert video_empty_block is not None
# log out user
logout_user()
Raw
02_test_json_output.json
{
"video_id": 108,
"keypoints": {
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6.920710802660324e-06,
-2.0225776097504422e-05,
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0.0935828685760498,
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],
[
0.269800066947937,
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],
[
0.11340804398059845,
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],
[
-0.029780715703964233,
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],
[
-0.01832025870680809,
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],
[
0.06009160727262497,
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],
[
0.019508522003889084,
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],
[
0.06709234416484833,
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],
[
0.172021746635437,
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0.11865895986557007
],
[
0.19488964974880219,
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[
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],
[
-0.14294704794883728,
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],
[
0.045945942401885986,
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]
],
"angles": {
"right_sh_el": 37.888379632247634,
"left_sh_el": 38.811272881072156,
"torso_delta": 28.38234958601501,
"head_delta": 13.1126774713489,
"right_hip_ft": 71.37056034046682,
"left_hip_ft": 81.73013681687075
},
"timestamp": 0,
"base": 0.16142725346071798,
"guard": 0.012211642318488036,
"head_off_centerline": 12.462954343628851,
"offense": "jab",
"defense": "False"
},
"1": {
"keypoints": [
[
6.505953933810815e-06,
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4.3147718997715856e-07
],
[
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[
-0.07808415591716766,
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0.06681951135396957,
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[
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[
0.19592176377773285,
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-0.11861969530582428,
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"right_hip_ft": 71.21208205631183,
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},
"timestamp": 0.134066,
"base": 0.1637152076995246,
"guard": 0.0122000203307612,
"head_off_centerline": 12.606850439477018,
"offense": "False",
"defense": "False"
},
"2": {
"keypoints": [
[
6.527430741698481e-06,
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3.621151449806348e-07
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[
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[
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[
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"right_hip_ft": 70.9462606552942,
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},
"timestamp": 0.268132,
"base": 0.16606093935528704,
"guard": 0.01223619365929929,
"head_off_centerline": 12.715490028054182,
"offense": "False",
"defense": "False"
}
},
"vimeo_video_url": "https://vimeo.com/714242432"
}
Raw
03_base.html
<!DOCTYPE html>
<html lang="en">
<!-- head -->
<head>
<title>{% block title %}Base{% endblock %}- Cryptius</title>
<!-- meta properties -->
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0, shrink-to-fit=no">
<meta name="description"
content="We are Cryptius, and we specialize in delivering state-of-the-art AI, machine learning, data science, and full stack web development solutions to small and medium sized enterprises at an affordable price point. We are nothing without our people, and more importantly the community from which we draw support. In addition to having highly experienced data scientists and machine learning engineers engaged in your projects, we frequently tap into a network of thousands of data scientists and engineers with common interests, goals, and broad skills in all areas of the field.">
<!-- static resources -->
{% load static %}
<link rel="stylesheet" href="{% static 'bootstrap/css/bootstrap.min.css' %}">
<link rel="stylesheet"
href="https://fonts.googleapis.com/css?family=Nunito:200,200i,300,300i,400,400i,600,600i,700,700i,800,800i,900,900i">
<link rel="stylesheet" href="{% static 'fonts/fontawesome-all.min.css' %}">
<link rel="stylesheet" href="{% static 'fonts/font-awesome.min.css' %}">
<link rel="stylesheet" href="{% static 'fonts/fontawesome5-overrides.min.css' %}">
<link rel="stylesheet" href="{% static 'css/main-style.css' %}">
<!-- specific to page css -->
{% block specific_page_css %}
<!-- block -->
{% endblock %}
</head>
<!-- body -->
<body id="page-top">
<div id="wrapper">
<!-- side nav -->
{% block side_nav %}
<!-- block-->
{% include "psiPhightApp/include/side-nav.html" %}
{% endblock %}
<!-- extra content -->
{% block extra_content %}
<!-- block-->
{% endblock %}
<div class="d-flex flex-column" id="content-wrapper">
<div id="content">
<!-- top nav -->
{% block top_nav %}
<!-- block -->
{% include "psiPhightApp/include/top-nav.html" %}
{% endblock %}
{% block container_main %}
<div class="container-fluid container-main">
<h3 class="text-dark mb-4">{% block title_in %}Base{% endblock %}</h3>
<div>
<div>
{% block rows %}
<!-- rows content -->
{% endblock %}
</div>
</div>
</div>
{% endblock %}
</div>
<!-- footer -->
{% block footer %}
{% include "psiPhightApp/include/footer.html" %}
{% endblock %}
</div>
</div>
<a class="border rounded d-inline scroll-to-top" href="#page-top"><i class="fas fa-angle-up"></i></a>
</div>
<!-- scripts -->
<script src="{% static 'bootstrap/js/bootstrap.min.js' %}"></script>
<script src="{% static 'js/bs-init.js' %}"></script>
<script src="{% static 'js/theme.js' %}"></script>
<!-- specific to page scripts -->
{% block specific_page_script %}
<!-- block -->
{% endblock %}
<!-- custom scripts -->
<script src="{% static 'js/main.js' %}"></script>
{% block custom_script %}
<!-- block -->
{% endblock %}
</body>
</html>
Raw
03_single_diagram.html
<!-- base template -->
{% extends 'psiPhightApp/extend/base.html' %}
<!-- page title -->
{% block title %}Single video diagram - Cryptius{% endblock %}
<!-- internal page title -->
{% block title_in %}Single video diagram{% endblock %}
<!-- extra content -->
{% block extra_content %}
{% include "psiPhightApp/include/modal-slider-description.html" %}
{% endblock %}
<!-- custom content -->
{% block rows %}
<!-- general stats -->
{% include "psiPhightApp/include/general-stats-row.html" with first_row_stats=first_row_stats hide_num_elements=1 %}
<div class="row mb-5 justify-content-center">
<div class="col-11 text-center d-flex justify-content-center">
<div class="card shadow mb-4">
<div class="card-header d-flex justify-content-between align-items-center">
<h6 class="text-primary fw-bold m-0">Expected performance</h6>
</div>
<div class="card-body">
<div>
<center> <!-- TODO: replace center with a better practice -->
<iframe src="https://player.vimeo.com/video/{{ 3D_reproduction_video }}"
width="640"
height="300"
allow="autoplay; fullscreen; picture-in-picture"
allowfullscreen></iframe>
</center>
</div>
</div>
</div>
</div>
</div>
<div>
<!-- main diagrams -->
{% include "psiPhightApp/include/main-diagram-slider.html" with plotly_divs=plotly_divs %}
</div>
{% endblock %}
Raw
04_gcloud_functions.py
import json
import math
from collections import defaultdict
import numpy as np
from google.api_core.exceptions import NotFound
from google.cloud import storage
from google.cloud.exceptions import GoogleCloudError
from numpy import random
storage_client = storage.Client()
# <---------- 3D keypoints methods ---------->
def upload_keypoints_to_cloud_storage(file_path):
# setup
bucket_name_upload = 'psiphight-3d-keypoints-bucket'
# keypoints
keypoints = np.load(file_path)
nitems = len(keypoints)
bytes_content = keypoints.tobytes()
print(keypoints)
# save result to bucket
destination_blob_name = '{}-keypoints'.format(nitems)
bucket_upload = storage_client.bucket(bucket_name_upload)
blob = bucket_upload.blob(destination_blob_name)
# contents to upload
blob.upload_from_string(bytes_content)
print('\nThe file {} was successfully created'.format(destination_blob_name))
# <---------- Angle measurements methods ---------->
def find_vec_angles(a, b):
"""
This function contains the math needed to find and calculate the angles. It takes 'a', and 'b' as an input,
that are two intersecting lines with the angle being the space. Each keypoint, 'a' and 'b', correspond to an
array that is equivalent to a group of vectors for the x, y and z dimensions.
:param a: first array with vectors x, y, and z.
:param b: second array with vectors x, y, and z.
:return: theta, the angle calculated after receiving the intersecting lines.
"""
numerator = np.dot(a, b)
mag_a = math.sqrt(sum(i ** 2 for i in a))
mag_b = math.sqrt(sum(i ** 2 for i in b))
anything = numerator / (mag_a * mag_b)
theta = math.acos(anything)
# convert to degrees
theta = (theta * 180) / math.pi
# find the co-terminal angle where 0 < theta < 360
if theta > 360:
revs = math.floor(theta / 360)
return theta - 360 * revs
else:
return theta
def generate_angle_measurements(keypoints):
"""
This function is needed to calculate the angle measurements for body keypoints. As an input, we take the
keypoints that were predicted with the help of VideoPose3D for each of the frames of the videos, and we
calculate the angles between each of the 3 dimensional vectors.
:return: angles
"""
# extract measurement values by angle and save to dictionary
angle_labels = ['right_sh_el', 'left_sh_el', 'torso_delta', 'head_delta', 'right_hip_ft', 'left_hip_ft']
angle_tuples = [(11, 12), (14, 15), (8, 0), (10, 7), (4, 6), (1, 3)]
angles = {}
# set keys and add empty array
for angle in angle_labels:
angles[angle] = []
# per each array (corresponding to video frame), in keypoints,
for frame in keypoints:
# calculate all 6 angle measurements
for idx, label in enumerate(angle_labels):
angles[label].append(find_vec_angles(frame[angle_tuples[idx][0]], frame[angle_tuples[idx][1]]))
# out
return angles
def merged_logic(event):
"""
Triggered by the creation of an object in Cloud Storage bucket. Receives a text file with 3D keypoints,
retrieves the contents of the file with the Google Cloud API, generates a
new file with the angle measurements.
:arg:event (dict): Event payload.
:arg:context (google.cloud.functions.Context): Metadata for the event.
:return: text file with the angle measurements as a dictionary.
"""
# logs
print('\n--- A new object was uploaded to bucket {}, we will attempt to retrieve its data'.format(event['bucket']))
# print('Event ID: {}'.format(context.event_id))
print('Bucket: {}'.format(event['bucket']))
print('Object name: {}'.format(event['name']))
print('Created: {}'.format(event['timeCreated']))
# Cloud Storage SDK
bucket_name_upload = 'psiphight-angle-measurements-bucket'
try:
# variables needed to access the object that triggered the function
event_id = 123 # replace with context.event_id
bucket_name_download = event['bucket']
bucket_main = storage_client.bucket(bucket_name_download)
blob_name = event['name']
# get object, download its contents as bytes, decode and encode to load with Numpy (np)
blob = bucket_main.blob(blob_name)
contents_raw = blob.download_as_bytes(raw_download=True)
# string/bytes to dictionary and Numpy file
decoded = json.loads(contents_raw.decode())
keypoints = np.array(decoded['keypoints'])
# logs
print('\nAfter loading to create a Numpy object from buffer: {}'.format(keypoints[0:5]))
# calculate angles, return object, convert object to bytes
angle_measurements = generate_angle_measurements(keypoints)
decoded['angles'] = angle_measurements
angle_measurements_encode_data = json.dumps(decoded)
# save result to bucket
destination_blob_name = '{}-{}-angle-measurements'.format(event_id, blob_name)
bucket_upload = storage_client.bucket(bucket_name_upload)
blob = bucket_upload.blob(destination_blob_name)
# contents to upload
blob.upload_from_string(angle_measurements_encode_data)
print('\nThe file {} was successfully created'.format(destination_blob_name))
except NotFound:
print('Not found: the item you were trying to retrieve could not be found. Make sure your bucket and specified '
'object exist')
except ValueError:
print('Value error: verify that the file contains the array size as part of the file/object name you uploaed '
'and that the contents correspond to a numpy array')
except (GoogleCloudError, ConnectionError):
print('Upload error: We could not upload your file with the angle measurements')
# <---------- JSON object generation methods ---------->
def construct_json_object():
"""
Triggered by the creation of an object in Cloud Storage bucket. It receives a text file that can be parsed to a
JSON object and extracts the keypoints, frames and angles for a particular video to generate final output.
:arg:event (dict): Event payload.
:arg:context (google.cloud.functions.Context): Metadata for the event.
:return: text file with the angle measurements as a dictionary.
"""
# setup
bucket_name_download = 'psiphight-angle-measurements-bucket'
bucket_main = storage_client.bucket(bucket_name_download)
event_id = 123 # event['id']
blob_name = '4730805695243995-1559-keypoints-json.txt-angle-measurements' # event['name']
# get object, download its contents as bytes, decode and encode to load with Numpy (np)
blob = bucket_main.blob(blob_name)
contents_raw = blob.download_as_bytes(raw_download=True)
# string/bytes to dictionary
decoded = json.loads(contents_raw.decode())
# init
timestamps = decoded['frames']
keypoints = decoded['keypoints']
angles = decoded['angles']
vid_frame_data = defaultdict(dict)
# idx = element number
for idx, timestamp in enumerate(timestamps):
# frame of the video
vid_frame_data[idx]["timestamp"] = timestamp
# add the keypoints --> a list of 17 lists
vid_frame_data[idx]["keypoints"] = keypoints[idx]
# add the angles
frame_angles = defaultdict(dict)
for angle in angles.keys():
frame_angles[angle] = angles[angle][idx]
vid_frame_data[idx]["angles"] = dict(frame_angles)
# logs
print(vid_frame_data[5])
# upload output to bucket
bucket_name_upload = 'psiphight-json-output-bucket'
json_out = json.dumps(vid_frame_data)
# save result to bucket
destination_blob_name = '{}-final-json-output'.format(event_id)
bucket_upload = storage_client.bucket(bucket_name_upload)
blob = bucket_upload.blob(destination_blob_name)
# contents to upload
blob.upload_from_string(json_out)
print('\nThe file {} was successfully created'.format(destination_blob_name))
if __name__ == '__main__':
# sample data simulate a real GCloud context
file_path_sample = 'output-keypoints-3d.npy'
random_id = random.randint(1105)
context_sample = {'event_id': random_id}
# sample data to simulate a real GCloud event
bucket_sample = 'my-new-bucket-nathaly-toledo'
event_sample = {'bucket': bucket_sample, 'name': object_name, 'timeCreated': '----'}
# generate_text_file_with_keypoints()
# generate_angle_measurements(keypoints)
# merged_logic(event_sample, context_sample)
# upload_keypoints_to_cloud_storage(file_path)
# write_json_file_to_cloud(None, None)
# read_json_string_from_cloud()
construct_json_object()
Raw
04_vimeo_functions.py
import traceback
import requests
import vimeo
import os
import uuid
import cv2
# setup Vimeo
from urllib3.exceptions import MaxRetryError, NewConnectionError
from vimeo.exceptions import UploadAttemptCreationFailure
token = os.environ['ACCESS_TOKEN_VIMEO']
client_id = os.environ['CLIENT_ID_VIMEO']
client_secret = os.environ['CLIENT_SECRET_VIMEO']
client = vimeo.VimeoClient(
token=token, # '{access_token}',
key=client_id, # '{client_id}',
secret=client_secret # '{client_secret}'
)
# uri: https://api.vimeo.com/tutorial
def test_api(uri_vimeo):
"""
Used to test that the API is working and the client was configured correctly.
:param uri_vimeo: URI to use for testing.
:return: None
"""
response = client.get(uri_vimeo)
print(response.json())
def remove_file(filename):
# init
file_path = 'media/{}'.format(filename)
# delete file
if os.path.exists(file_path):
print('Deleting file ', file_path)
os.remove(file_path)
def get_video_duration(filename):
"""
Get video duration in minutes with opencv.
:return duration value in minutes
"""
# init
video = cv2.VideoCapture(filename)
# video duration in milliseconds
frame_count = video.get(cv2.CAP_PROP_FRAME_COUNT)
fps = video.get(cv2.CAP_PROP_FPS)
duration = frame_count / fps
# out: round division down to the nearest whole number after calculating the min
return int(duration / 60)
def upload_vimeo_video(video_path):
"""
Given a path to a video file, it attempts to upload it to the Vimeo platform via their API with a POST request.
After successful upload, it will return the URL link to the video on Vimeo. On failure, it will return None.
:param video_path: path to the video file to upload.
:return: None, if failed. The URL to the video on Vimeo, if successful.
"""
try:
print('VIDEO_PATH ', video_path)
print('VALUE TYPE ', type(video_path))
vimeo_video_name = str(uuid.uuid4())
# url to make request
url = client.upload(video_path, data={
'name': vimeo_video_name,
'description': 'Sample output video we can use to test PsiPhight app. The video exemplifies a training '
'session. '
})
# make API request
response = client.get(url + '?fields=link').json()
video_link = response['link']
print('JSON RESPONSE ', response)
# output
return video_link
except FileNotFoundError as file_error:
print(traceback.format_exc())
print('[ERROR] File not found. Check the path you specified. The error was {}\n'.format(file_error))
return None
except (ConnectionError, requests.exceptions.ConnectionError, MaxRetryError, NewConnectionError, socket.gaierror) as connect_error:
print(traceback.format_exc())
print('[ERROR] Connection error: {}\n'.format(connect_error))
return None
except UploadAttemptCreationFailure as vimeo_exception:
print(traceback.format_exc())
print('[ERROR] Something happened while uploading your video. Please check your credentials and the request '
'arguments: {}\n', vimeo_exception)
return None
if __name__ == '__main__':
# test the Vimeo API
uri_test = 'https://api.vimeo.com/tutorial'
vt = '../media/{}'.format('205494563_yAGTi2V.mp4')
# v = get_video_duration(vt)
# print(v)
remove_file(vt)
Raw
05_pre_push
#!/bin/sh
# activating virtual environment
echo "Activating virtual environment: $( pwd; )...
";
source venv/Scripts/activate;
# running unit test files
echo "Running checks for the project: $( pwd; )...
";
OUTPUT=$(python manage.py test psiPhightApp/tests/unit/)
EXIT=$?
# print results
echo "
The output of the process was:
${OUTPUT}
";
echo "
The exit code of the process was: ${EXIT}";
# evaluate if changes should be pushed or not to the branch
if [ $EXIT -ne 0 ]; then
echo "ERROR: We recommend you check your code, manually run the tests to debug the code, and try again."
# exit 1 means unsuccessful, the git push cannot pass
exit 1
fi
# exit 0 means successful, the git push can pass
exit 0
Raw
05_sample_input.md
Raw
05_sample_output.json
{"0": {"keypoints": [[6.920710802660324e-06, -2.0225776097504422e-05, 5.711709150091338e-07], [-0.09358373284339905, 0.023615948855876923, -0.10346446186304092], [-0.0777832418680191, 0.4652405381202698, -0.009631946682929993], [-0.19407522678375244, 0.8200191855430603, 0.1926773339509964], [0.0935828685760498, -0.023622214794158936, 0.10347278416156769], [0.269800066947937, 0.3964669108390808, 0.13304796814918518], [0.11340804398059845, 0.6953498125076294, 0.41291457414627075], [-0.029780715703964233, -0.23100179433822632, -0.04211486130952835], [-0.01832025870680809, -0.46300235390663147, -0.1482483148574829], [0.06009160727262497, -0.5108947157859802, -0.2128428816795349], [0.019508522003889084, -0.6137453317642212, -0.22273391485214233], [0.06709234416484833, -0.4464050233364105, -0.028144119307398796], [0.172021746635437, -0.23028698563575745, 0.11865895986557007], [0.19488964974880219, -0.2456790804862976, -0.12225668132305145], [-0.11894632130861282, -0.37187659740448, -0.2125738263130188], [-0.14294704794883728, -0.09854696691036224, -0.268058180809021], [0.045945942401885986, -0.29384058713912964, -0.2849007248878479]], "angles": {"right_sh_el": 37.888379632247634, "left_sh_el": 38.811272881072156, "torso_delta": 28.38234958601501, "head_delta": 13.1126774713489, "right_hip_ft": 71.37056034046682, "left_hip_ft": 81.73013681687075}, "timestamp": 0, "base": 0.16142725346071798, "guard": 0.012211642318488036, "head_off_centerline": 12.462954343628851, "offense": "False", "defense": "False"}, "1": {"keypoints": [[6.505953933810815e-06, -1.9899141989299096e-05, 4.3147718997715856e-07], [-0.09356966614723206, 0.023992497473955154, -0.10319077968597412], [-0.07808415591716766, 0.46488815546035767, -0.007530802860856056], [-0.1932593286037445, 0.8212000131607056, 0.19353428483009338], [0.09356866776943207, -0.023998815566301346, 0.10319970548152924], [0.27230551838874817, 0.39551103115081787, 0.13466927409172058], [0.1196124404668808, 0.6960179805755615, 0.4146897792816162], [-0.02964569628238678, -0.2302691638469696, -0.042094163596630096], [-0.01800515502691269, -0.46234142780303955, -0.14811928570270538], [0.05994490534067154, -0.5098714232444763, -0.2132720649242401], [0.019639378413558006, -0.6124764680862427, -0.22270093858242035], [0.06681951135396957, -0.44546568393707275, -0.02848190627992153], [0.1709786057472229, -0.22919200360774994, 0.11951424181461334], [0.19592176377773285, -0.24271923303604126, -0.12059171497821808], [-0.11861969530582428, -0.37131866812705994, -0.21161818504333496], [-0.14599978923797607, -0.09792594611644745, -0.2645648419857025], [0.04553411900997162, -0.28914886713027954, -0.2828243374824524]], "angles": {"right_sh_el": 38.07199670812766, "left_sh_el": 38.88708327801736, "torso_delta": 27.555765181242283, "head_delta": 13.121600841245842, "right_hip_ft": 71.21208205631183, "left_hip_ft": 81.6271816126898}, "timestamp": 0.134066, "base": 0.1637152076995246, "guard": 0.0122000203307612, "head_off_centerline": 12.606850439477018, "offense": "False", "defense": "False"}, "2": {"keypoints": [[6.527430741698481e-06, -1.946386691997759e-05, 3.621151449806348e-07], [-0.09476923942565918, 0.02390737645328045, -0.10242360085248947], [-0.0787535309791565, 0.4643770754337311, -0.006077120080590248], [-0.1917765736579895, 0.8242400884628296, 0.1946793496608734], [0.09476811438798904, -0.023913593962788582, 0.10243271291255951], [0.2749785780906677, 0.39426085352897644, 0.1335161030292511], [0.12761346995830536, 0.6959364414215088, 0.41439008712768555], [-0.029569994658231735, -0.2305823713541031, -0.04227100685238838], [-0.017005497589707375, -0.4619055390357971, -0.14873093366622925], [0.05951877683401108, -0.5090497732162476, -0.21485210955142975], [0.02013876475393772, -0.6117618083953857, -0.22420534491539001], [0.06836266815662384, -0.4440973699092865, -0.029696399345993996], [0.17030885815620422, -0.2291170358657837, 0.12056724727153778], [0.19604134559631348, -0.2382211685180664, -0.12053047865629196], [-0.11903964728116989, -0.3716878294944763, -0.21092019975185394], [-0.14971232414245605, -0.09817054867744446, -0.26178979873657227], [0.04435514658689499, -0.2852020561695099, -0.28277623653411865]], "angles": {"right_sh_el": 38.102396833384645, "left_sh_el": 38.880888104886274, "torso_delta": 27.718435116139435, "head_delta": 13.218144476437224, "right_hip_ft": 70.9462606552942, "left_hip_ft": 81.60892580527467}, "timestamp": 0.268132, "base": 0.16606093935528704, "guard": 0.01223619365929929, "head_off_centerline": 12.715490028054182, "offense": "False", "defense": "False"}, "3": {"keypoints": [[6.320940883597359e-06, -1.9427407096372917e-05, 6.687276368211315e-07], [-0.09584323316812515, 0.024367913603782654, -0.10127711296081543], [-0.07952125370502472, 0.46477189660072327, -0.004062356427311897], [-0.18971319496631622, 0.8245671391487122, 0.19930660724639893], [0.09584205597639084, -0.024374227970838547, 0.10128635913133621], [0.27664679288864136, 0.39287689328193665, 0.1320449411869049], [0.13311553001403809, 0.698634922504425, 0.4128236174583435], [-0.029719345271587372, -0.23040057718753815, -0.042479731142520905], [-0.017126023769378662, -0.4617299437522888, -0.14926689863204956], [0.05935797840356827, -0.5091453194618225, -0.21596848964691162], [0.0200106892734766, -0.611527681350708, -0.2253861129283905], [0.0693945437669754, -0.44400787353515625, -0.0309501513838768], [0.17037169635295868, -0.23009467124938965, 0.12181687355041504], [0.19499903917312622, -0.2364753782749176, -0.12194648385047913], [-0.12100064009428024, -0.3726400136947632, -0.21026349067687988], [-0.1542089283466339, -0.0999978631734848, -0.2602704167366028], [0.043053992092609406, -0.2832275927066803, -0.28310662508010864]], "angles": {"right_sh_el": 38.15209596224507, "left_sh_el": 38.648087408591856, "torso_delta": 28.016102681744805, "head_delta": 13.271229493965784, "right_hip_ft": 71.18522589265994, "left_hip_ft": 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