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app.py
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app.py
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from textwrap import dedent
import dash
import dash_core_components as dcc
import dash_html_components as html
import dash_player as player
import numpy as np
import pandas as pd
import plotly.graph_objs as go
from dash.dependencies import Input, Output, State
DEBUG = True
FRAMERATE = 24.0
app = dash.Dash(__name__)
server = app.server
app.scripts.config.serve_locally = True
app.config['suppress_callback_exceptions'] = True
def load_data(path):
"""Load data about a specific footage (given by the path). It returns a dictionary of useful variables such as
the dataframe containing all the detection and bounds localization, the number of classes inside that footage,
the matrix of all the classes in string, the given class with padding, and the root of the number of classes,
rounded."""
# Load the dataframe containing all the processed object detections inside the video
video_info_df = pd.read_csv(path)
# The list of classes, and the number of classes
classes_list = video_info_df["class_str"].value_counts().index.tolist()
n_classes = len(classes_list)
# Gets the smallest value needed to add to the end of the classes list to get a square matrix
root_round = np.ceil(np.sqrt(len(classes_list)))
total_size = root_round ** 2
padding_value = int(total_size - n_classes)
classes_padded = np.pad(classes_list, (0, padding_value), mode='constant')
# The padded matrix containing all the classes inside a matrix
classes_matrix = np.reshape(classes_padded, (int(root_round), int(root_round)))
# Flip it for better looks
classes_matrix = np.flip(classes_matrix, axis=0)
data_dict = {
"video_info_df": video_info_df,
"n_classes": n_classes,
"classes_matrix": classes_matrix,
"classes_padded": classes_padded,
"root_round": root_round
}
if DEBUG:
print(f'{path} loaded.')
return data_dict
def markdown_popup():
return html.Div(
id='markdown',
className="model",
style={'display': 'none'},
children=(
html.Div(
className="markdown-container",
children=[
html.Div(
className='close-container',
children=html.Button(
"Close",
id="markdown_close",
n_clicks=0,
className="closeButton",
style={'border': 'none', 'height': '100%'}
)
),
html.Div(
className='markdown-text',
children=[dcc.Markdown(
children=dedent(
'''
##### What am I looking at?
This app enhances visualization of objects detected using state-of-the-art Mobile Vision Neural Networks.
Most user generated videos are dynamic and fast-paced, which might be hard to interpret. A confidence
heatmap stays consistent through the video and intuitively displays the model predictions. The pie chart
lets you interpret how the object classes are divided, which is useful when analyzing videos with numerous
and differing objects.
##### More about this dash app
The purpose of this demo is to explore alternative visualization methods for Object Detection. Therefore,
the visualizations, predictions and videos are not generated in real time, but done beforehand. To read
more about it, please visit the [project repo](https://github.com/plotly/dash-object-detection).
'''
))
]
)
]
)
)
)
# Main App
app.layout = html.Div(
children=[
html.Div(
id='top-bar',
className='row',
style={'backgroundColor': '#fa4f56',
'height': '5px',
}
),
html.Div(
className='container',
children=[
html.Div(
id='left-side-column',
className='eight columns',
style={'display': 'flex',
'flexDirection': 'column',
'flex': 1,
'height': 'calc(100vh - 5px)',
'backgroundColor': '#F2F2F2',
'overflow-y': 'scroll',
'marginLeft': '0px',
'justifyContent': 'flex-start',
'alignItems': 'center'},
children=[
html.Div(
id='header-section',
children=[
html.H4(
'Object Detection Explorer'
),
html.P(
'To get started, select a footage you want to view, and choose the display mode (with or without'
' bounding boxes). Then, you can start playing the video, and the visualization will '
'be displayed depending on the current time.'
),
html.Button("Learn More", id="learn-more-button", n_clicks=0)
]
),
html.Div(
className='video-outer-container',
children=html.Div(
style={'width': '100%', 'paddingBottom': '56.25%', 'position': 'relative'},
children=player.DashPlayer(
id='video-display',
style={'position': 'absolute', 'width': '100%',
'height': '100%', 'top': '0', 'left': '0', 'bottom': '0', 'right': '0'},
url='https://www.youtube.com/watch?v=gPtn6hD7o8g',
controls=True,
playing=False,
volume=1,
width='100%',
height='100%'
)
)
),
html.Div(
className='control-section',
children=[
html.Div(
className='control-element',
children=[
html.Div(children=["Minimum Confidence Threshold:"], style={'width': '40%'}),
html.Div(dcc.Slider(
id='slider-minimum-confidence-threshold',
min=20,
max=80,
marks={i: f'{i}%' for i in range(20, 81, 10)},
value=30,
updatemode='drag'
), style={'width': '60%'})
]
),
html.Div(
className='control-element',
children=[
html.Div(children=["Footage Selection:"], style={'width': '40%'}),
dcc.Dropdown(
id="dropdown-footage-selection",
options=[
{'label': 'Drone recording of canal festival',
'value': 'DroneCanalFestival'},
{'label': 'Drone recording of car festival', 'value': 'car_show_drone'},
{'label': 'Drone recording of car festival #2',
'value': 'DroneCarFestival2'},
{'label': 'Drone recording of a farm', 'value': 'FarmDrone'},
{'label': 'Lion fighting Zebras', 'value': 'zebra'},
{'label': 'Man caught by a CCTV', 'value': 'ManCCTV'},
{'label': 'Man driving expensive car', 'value': 'car_footage'},
{'label': 'Restaurant Robbery', 'value': 'RestaurantHoldup'}
],
value='car_show_drone',
clearable=False,
style={'width': '60%'}
)
]
),
html.Div(
className='control-element',
children=[
html.Div(children=["Video Display Mode:"], style={'width': '40%'}),
dcc.Dropdown(
id="dropdown-video-display-mode",
options=[
{'label': 'Regular Display', 'value': 'regular'},
{'label': 'Display with Bounding Boxes', 'value': 'bounding_box'},
],
value='bounding_box',
searchable=False,
clearable=False,
style={'width': '60%'}
)
]
),
html.Div(
className='control-element',
children=[
html.Div(children=["Graph View Mode:"], style={'width': '40%'}),
dcc.Dropdown(
id="dropdown-graph-view-mode",
options=[
{'label': 'Visual Mode', 'value': 'visual'},
{'label': 'Detection Mode', 'value': 'detection'}
],
value='visual',
searchable=False,
clearable=False,
style={'width': '60%'}
)
]
)
]
)
]
),
html.Div(
id='right-side-column',
className='four columns',
style={
'height': 'calc(100vh - 5px)',
'overflow-y': 'scroll',
'marginLeft': '1%',
'display': 'flex',
'backgroundColor': '#F9F9F9',
'flexDirection': 'column'
},
children=[
html.Div(
className='img-container',
children=html.Img(
style={'height': '100%', 'margin': '2px'},
src="https://s3-us-west-1.amazonaws.com/plotly-tutorials/logo/new-branding/dash-logo-by-plotly-stripe.png")
),
html.Div(id="div-visual-mode"),
html.Div(id="div-detection-mode")
]
)]),
markdown_popup()
]
)
# Data Loading
@app.server.before_first_request
def load_all_footage():
global data_dict, url_dict
# Load the dictionary containing all the variables needed for analysis
data_dict = {
'james_bond': load_data("data/james_bond_object_data.csv"),
'zebra': load_data("data/Zebra_object_data.csv"),
'car_show_drone': load_data("data/CarShowDrone_object_data.csv"),
'car_footage': load_data("data/CarFootage_object_data.csv"),
'DroneCanalFestival': load_data("data/DroneCanalFestivalDetectionData.csv"),
'DroneCarFestival2': load_data("data/DroneCarFestival2DetectionData.csv"),
'FarmDrone': load_data("data/FarmDroneDetectionData.csv"),
'ManCCTV': load_data("data/ManCCTVDetectionData.csv"),
'RestaurantHoldup': load_data("data/RestaurantHoldupDetectionData.csv")
}
url_dict = {
'regular': {
'james_bond': 'https://www.youtube.com/watch?v=g9S5GndUhko',
'zebra': 'https://www.youtube.com/watch?v=TVvtD3AVt10',
'car_show_drone': 'https://www.youtube.com/watch?v=gPtn6hD7o8g',
'car_footage': 'https://www.youtube.com/watch?v=qX3bDxHuq6I',
'DroneCanalFestival': 'https://youtu.be/0oucTt2OW7M',
'DroneCarFestival2': 'https://youtu.be/vhJ7MHsJvwY',
'FarmDrone': 'https://youtu.be/aXfKuaP8v_A',
'ManCCTV': 'https://youtu.be/BYZORBIxgbc',
'RestaurantHoldup': 'https://youtu.be/WDin4qqgpac',
},
'bounding_box': {
'james_bond': 'https://www.youtube.com/watch?v=g9S5GndUhko',
'zebra': 'https://www.youtube.com/watch?v=G2pbZgyWQ5E',
'car_show_drone': 'https://www.youtube.com/watch?v=9F5FdcVmLOY',
'car_footage': 'https://www.youtube.com/watch?v=EhnNosq1Lrc',
'DroneCanalFestival': 'https://youtu.be/6ZZmsnwk2HQ',
'DroneCarFestival2': 'https://youtu.be/2Gr4RQ-JHIs',
'FarmDrone': 'https://youtu.be/pvvW5yZlpyc',
'ManCCTV': 'https://youtu.be/1oMrHLrtOZw',
'RestaurantHoldup': 'https://youtu.be/HOIKOwixYEY',
}
}
# Footage Selection
@app.callback(Output("video-display", "url"),
[Input('dropdown-footage-selection', 'value'),
Input('dropdown-video-display-mode', 'value')])
def select_footage(footage, display_mode):
# Find desired footage and update player video
url = url_dict[display_mode][footage]
return url
# Learn more popup
@app.callback(Output("markdown", "style"),
[Input("learn-more-button", "n_clicks"), Input("markdown_close", "n_clicks")])
def update_click_output(button_click, close_click):
if button_click > close_click:
return {"display": "block"}
else:
return {"display": "none"}
@app.callback(Output("div-visual-mode", "children"),
[Input("dropdown-graph-view-mode", "value")])
def update_output(dropdown_value):
if dropdown_value == "visual":
return [
dcc.Interval(
id="interval-visual-mode",
interval=700,
n_intervals=0
),
html.Div(
children=[
html.P(children="Confidence Level of Object Presence",
className='plot-title'),
dcc.Graph(
id="heatmap-confidence",
style={'height': '45vh', 'width': '100%'}),
html.P(children="Object Count",
className='plot-title'),
dcc.Graph(
id="pie-object-count",
style={'height': '40vh', 'width': '100%'}
)
]
)
]
else:
return []
@app.callback(Output("div-detection-mode", "children"),
[Input("dropdown-graph-view-mode", "value")])
def update_detection_mode(value):
if value == "detection":
return [
dcc.Interval(
id="interval-detection-mode",
interval=700,
n_intervals=0
),
html.Div(
children=[
html.P(children="Detection Score of Most Probable Objects",
className='plot-title'),
dcc.Graph(
id="bar-score-graph",
style={'height': '55vh'}
)
]
)
]
else:
return []
# Updating Figures
@app.callback(Output("bar-score-graph", "figure"),
[Input("interval-detection-mode", "n_intervals")],
[State("video-display", "currentTime"),
State('dropdown-footage-selection', 'value'),
State('slider-minimum-confidence-threshold', 'value')])
def update_score_bar(n, current_time, footage, threshold):
layout = go.Layout(
showlegend=False,
paper_bgcolor='rgb(249,249,249)',
plot_bgcolor='rgb(249,249,249)',
xaxis={
'automargin': True,
},
yaxis={
'title': 'Score',
'automargin': True,
'range': [0, 1]
}
)
if current_time is not None:
current_frame = round(current_time * FRAMERATE)
if n > 0 and current_frame > 0:
video_info_df = data_dict[footage]["video_info_df"]
# Select the subset of the dataset that correspond to the current frame
frame_df = video_info_df[video_info_df["frame"] == current_frame]
# Select only the frames above the threshold
threshold_dec = threshold / 100 # Threshold in decimal
frame_df = frame_df[frame_df["score"] > threshold_dec]
# Select up to 8 frames with the highest scores
frame_df = frame_df[:min(8, frame_df.shape[0])]
# Add count to object names (e.g. person --> person 1, person --> person 2)
objects = frame_df["class_str"].tolist()
object_count_dict = {x: 0 for x in set(objects)} # Keeps count of the objects
objects_wc = [] # Object renamed with counts
for object in objects:
object_count_dict[object] += 1 # Increment count
objects_wc.append(f"{object} {object_count_dict[object]}")
colors = list('rgb(250,79,86)' for i in range(len(objects_wc)))
# Add text information
y_text = [f"{round(value * 100)}% confidence" for value in frame_df["score"].tolist()]
figure = go.Figure({
'data': [{'hoverinfo': 'x+text',
'name': 'Detection Scores',
'text': y_text,
'type': 'bar',
'x': objects_wc,
'marker': {'color': colors},
'y': frame_df["score"].tolist()}],
'layout': {'showlegend': False,
'autosize': False,
'paper_bgcolor': 'rgb(249,249,249)',
'plot_bgcolor': 'rgb(249,249,249)',
'xaxis': {'automargin': True, 'tickangle': -45},
'yaxis': {'automargin': True, 'range': [0, 1], 'title': {'text': 'Score'}}}
}
)
return figure
return go.Figure(data=[go.Bar()], layout=layout) # Returns empty bar
@app.callback(Output("pie-object-count", "figure"),
[Input("interval-visual-mode", "n_intervals")],
[State("video-display", "currentTime"),
State('dropdown-footage-selection', 'value'),
State('slider-minimum-confidence-threshold', 'value')])
def update_object_count_pie(n, current_time, footage, threshold):
layout = go.Layout(
showlegend=True,
paper_bgcolor='rgb(249,249,249)',
plot_bgcolor='rgb(249,249,249)',
autosize=False,
margin=go.layout.Margin(
l=10,
r=10,
t=15,
b=15
)
)
if current_time is not None:
current_frame = round(current_time * FRAMERATE)
if n > 0 and current_frame > 0:
video_info_df = data_dict[footage]["video_info_df"]
# Select the subset of the dataset that correspond to the current frame
frame_df = video_info_df[video_info_df["frame"] == current_frame]
# Select only the frames above the threshold
threshold_dec = threshold / 100 # Threshold in decimal
frame_df = frame_df[frame_df["score"] > threshold_dec]
# Get the count of each object class
class_counts = frame_df["class_str"].value_counts()
classes = class_counts.index.tolist() # List of each class
counts = class_counts.tolist() # List of each count
text = [f"{count} detected" for count in counts]
# Set colorscale to piechart
colorscale = ['#fa4f56', '#fe6767', '#ff7c79', '#ff908b', '#ffa39d', '#ffb6b0', '#ffc8c3', '#ffdbd7',
'#ffedeb', '#ffffff']
pie = go.Pie(
labels=classes,
values=counts,
text=text,
hoverinfo="text+percent",
textinfo="label+percent",
marker={'colors': colorscale[:len(classes)]}
)
return go.Figure(data=[pie], layout=layout)
return go.Figure(data=[go.Pie()], layout=layout) # Returns empty pie chart
@app.callback(Output("heatmap-confidence", "figure"),
[Input("interval-visual-mode", "n_intervals")],
[State("video-display", "currentTime"),
State('dropdown-footage-selection', 'value'),
State('slider-minimum-confidence-threshold', 'value')])
def update_heatmap_confidence(n, current_time, footage, threshold):
layout = go.Layout(
showlegend=False,
paper_bgcolor='rgb(249,249,249)',
plot_bgcolor='rgb(249,249,249)',
autosize=False,
margin=go.layout.Margin(
l=10,
r=10,
b=20,
t=20,
pad=4
)
)
if current_time is not None:
current_frame = round(current_time * FRAMERATE)
if n > 0 and current_frame > 0:
# Load variables from the data dictionary
video_info_df = data_dict[footage]["video_info_df"]
classes_padded = data_dict[footage]["classes_padded"]
root_round = data_dict[footage]["root_round"]
classes_matrix = data_dict[footage]["classes_matrix"]
# Select the subset of the dataset that correspond to the current frame
frame_df = video_info_df[video_info_df["frame"] == current_frame]
# Select only the frames above the threshold
threshold_dec = threshold / 100
frame_df = frame_df[frame_df["score"] > threshold_dec]
# Remove duplicate, keep the top result
frame_no_dup = frame_df[["class_str", "score"]].drop_duplicates("class_str")
frame_no_dup.set_index("class_str", inplace=True)
# The list of scores
score_list = []
for el in classes_padded:
if el in frame_no_dup.index.values:
score_list.append(frame_no_dup.loc[el][0])
else:
score_list.append(0)
# Generate the score matrix, and flip it for visual
score_matrix = np.reshape(score_list, (-1, int(root_round)))
score_matrix = np.flip(score_matrix, axis=0)
# We set the color scale to white if there's nothing in the frame_no_dup
if frame_no_dup.shape != (0, 1):
colorscale = [[0, '#f9f9f9'], [1, '#fa4f56']]
else:
colorscale = [[0, '#f9f9f9'], [1, '#f9f9f9']]
hover_text = [f"{score * 100:.2f}% confidence" for score in score_list]
hover_text = np.reshape(hover_text, (-1, int(root_round)))
hover_text = np.flip(hover_text, axis=0)
# Add linebreak for multi-word annotation
classes_matrix = classes_matrix.astype(dtype='|U40')
for index, row in enumerate(classes_matrix):
row = list(map(lambda x: '<br>'.join(x.split()), row))
classes_matrix[index] = row
# Set up annotation text
annotation = []
for y_cord in range(int(root_round)):
for x_cord in range(int(root_round)):
annotation_dict = dict(
showarrow=False,
text=classes_matrix[y_cord][x_cord],
xref='x',
yref='y',
x=x_cord,
y=y_cord
)
if score_matrix[y_cord][x_cord] > 0:
annotation_dict['font'] = {'color': '#F9F9F9', 'size': '11'}
else:
annotation_dict['font'] = {'color': '#606060', 'size': '11'}
annotation.append(annotation_dict)
# Generate heatmap figure
figure = {
'data': [
{'colorscale': colorscale,
'showscale': False,
'hoverinfo': 'text',
'text': hover_text,
'type': 'heatmap',
'zmin': 0,
'zmax': 1,
'xgap': 1,
'ygap': 1,
'z': score_matrix}],
'layout':
{'showlegend': False,
'autosize': False,
'paper_bgcolor': 'rgb(249,249,249)',
'plot_bgcolor': 'rgb(249,249,249)',
'margin': {'l': 10, 'r': 10, 'b': 20, 't': 20, 'pad': 2},
'annotations': annotation,
'xaxis': {'showticklabels': False, 'showgrid': False, 'side': 'top', 'ticks': ''},
'yaxis': {'showticklabels': False, 'showgrid': False, 'side': 'left', 'ticks': ''}
}
}
return figure
# Returns empty figure
return go.Figure(data=[go.Pie()], layout=layout)
# Running the server
if __name__ == '__main__':
app.run_server(dev_tools_hot_reload=False, debug=DEBUG, host='0.0.0.0')