@ -295,26 +295,51 @@ def compare_colors(color_a, color_b):
def recognize_time_of_day ( frame ) :
""" Returns time of day, score, all scores. """
COLORS = {
" day " : ( 89 , 236 , 239 ) ,
" dusk " : ( 199 , 162 , 205 ) ,
" night " : ( 1 , 1 , 1 ) ,
" dawn " : ( 51 , 59 , 142 ) ,
""" Determine time-of-day from a sky pixel and a dashboard pixel
Uses the colour of a pixel in the sky and of a pixel on the dashboard to determine time - of - day
or whether the game is on the score screen . """
sky_colours = {
' score ' : ( 119 , 119 , 119 ) ,
' day ' : ( 82 , 218 , 217 ) ,
' dusk ' : ( 225 , 152 , 184 ) , # estimated from previous years
' night ' : ( 0 , 0 , 0 ) , # estimated from previous years
' dawn ' : ( 56 , 53 , 125 ) , # estimated from previous years
}
sample = frame . getpixel ( ( 177 , 255 ) )
scores = [
( tod , compare_colors ( sample , color ) )
for tod , color in COLORS . items ( )
]
best , score = max ( scores , key = lambda t : t [ 1 ] )
return best , score , scores
dash_colours = {
' score ' : ( 181 , 181 , 150 ) ,
' day ' : ( 147 , 0 , 2 ) ,
' dusk ' : ( 118 , 0 , 0 ) , # estimated from previous years
' night ' : ( 68 , 0 , 0 ) , # estimated from previous years
' dawn ' : ( 118 , 0 , 0 ) , # estimated from previous years
}
threshold = 20 # use stronger constraint once we have dusk, night and dawn footage
# these are for 720p; will need to multiple by 3/2 for 1080p
sky_pixel = frame . getpixel ( ( 1076 , 128 ) )
dash_pixel = frame . getpixel ( ( 630 , 576 ) )
MAX_DIST = 6 * * 0.5 * 255
sky_distances = [ ]
dash_distances = [ ]
matches = [ ( None , MAX_DIST ) ]
for time in sky_colours :
sky_distance = sum ( ( a - b ) * * 2 for a , b in zip ( sky_pixel , sky_colours [ time ] ) ) * * 0.5
sky_distances . append ( sky_distance )
dash_distance = sum ( ( a - b ) * * 2 for a , b in zip ( dash_pixel , dash_colours [ time ] ) ) * * 0.5
dash_distances . append ( dash_distance )
if sky_distance < threshold and dash_distance < threshold :
matches . append ( ( time , ( sky_distance * * 2 + dash_distance * * 2 ) * * 0.5 ) )
best , distance = min ( matches , key = lambda m : m [ 1 ] )
return best , distance
def extract_segment ( prototypes , segment ) :
ODO_SCORE_THRESHOLD = 0.01
CLOCK_SCORE_THRESHOLD = 0.01
TOD_SCORE_THRESHOLD = 0.9
frame_data = b " " . join ( extract_frame ( [ segment ] , segment . start ) )
frame = Image . open ( BytesIO ( frame_data ) )
odometer , score , _ = recognize_odometer ( prototypes , frame )
@ -323,9 +348,7 @@ def extract_segment(prototypes, segment):
clock , score , _ = recognize_clock ( prototypes , frame )
if score < CLOCK_SCORE_THRESHOLD :
clock = None
tod , score , _ = recognize_time_of_day ( frame )
if score < TOD_SCORE_THRESHOLD :
tod = None
tod , distance = recognize_time_of_day ( frame )
return odometer , clock , tod