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06_1_plot_statistics.py
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06_1_plot_statistics.py
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# -*- coding: utf-8 -*-
import sys
import os
import xml.etree.ElementTree as et
import matplotlib.pyplot as plt
import math
import numpy
from lib import smooth
OUTPUT_DIR_NAME = "plots"
def main():
os.chdir(sys.argv[1])
try:
os.mkdir(OUTPUT_DIR_NAME)
except OSError:
pass
tree = et.parse("project.xml")
movie = tree.getroot()
fps = float( movie.attrib["fps"] )
frames = float( movie.attrib["frames"] )
os.chdir(OUTPUT_DIR_NAME)
s = frames / fps
m = s / 60.0
print s, "seconds"
h = s / float(60*60)
print "%.2f hours" % h
percent = h / 2.0
print "%.2f %%" % percent
print "%d:%02d" % (math.floor(h), (h-math.floor(h))*60)
# ===== DURATION ==================================================================================================
plt.axis(ymin=0, ymax=10, xmin=0, xmax=3*60)
plt.xlabel("%d mins, %.2f %% of 2 hours" % (s / 60, percent))
lw = 20
plt.plot([0, 2*60], [1, 1], "k-", linewidth=lw, solid_capstyle="butt")
plt.plot([0, h*60], [2, 2], "b-", linewidth=lw, solid_capstyle="butt")
r = 200 / 2
r2 = math.sqrt( percent * r*r )
plt.plot([100], [6], "o", markeredgewidth=0, markersize=2*r2, markerfacecolor="b")
plt.plot([100], [6], "o", markeredgewidth=1, markersize=2*r, markerfacecolor="none")
plt.axis("off")
plt.show()
#plt.savefig(os.path.join(OUTPUT_DIR_NAME, "duration.ps"))
# ===== SHOTS ==================================================================================================
f = open("..\\shots.txt", "r")
values = [[int(values[0]), int(values[1]), int(values[2])] for values in [line.split("\t") for line in f if line]]
f.close()
fig = plt.figure()
ax = fig.add_subplot(111)
plt.ylim(ymin=5, ymax=15.5)
#ax.set_yscale("log")
for i, item in enumerate(values):
#print item
frame_start, frame_end, length = item
y = 10
if i % 2 == 0:
color = (0, 0, 0)
else:
color = (0.5, 0.5, 0.5)
y = 10.5
#ax.hlines(length/100.0, frame_start, frame_end, color=color, lw=100)
ax.hlines(y, frame_start, frame_end, color=color, lw=30)
ax.axis("off")
plt.show()
# ===== TRENDLINES ==================================================================================================
f = open("..\\motion_shot-avg.txt", "r")
values = [[float(values[0]), int(values[1])] for values in [line.split("\t") for line in f if line]]
f.close()
motions, durations = ([a for a, b in values], [b for a, b in values])
durations_sec = [float(d/fps) for d in durations]
print len(durations), "shots"
print "%.1f cuts per minute" % (len(durations)/m)
print "min:", min(durations_sec), "s"
print "max:", max(durations_sec), "s"
print "range:", max(durations_sec)-min(durations_sec), "s"
print "asl:", numpy.mean(durations_sec), "s"
print "std:", numpy.std(durations_sec), "s"
print "var:", numpy.var(durations_sec), "s"
file = open("..\\subtitles.txt")
s = file.read()
file.close()
word_count = len( s.split() )
words_per_minute = word_count / m
print words_per_minute, "words / minute"
WINDOW_LEN = 20
TREND_DEGREE = 1 # polynom 1ten grades
data = numpy.array(WINDOW_LEN*[durations_sec[0]] + durations_sec + WINDOW_LEN*[0])
trend_duration = numpy.polyfit(range(len(data)), data, TREND_DEGREE)
trend_duration = numpy.poly1d(trend_duration)
smooth_data = smooth( data, window_len=WINDOW_LEN, window='hanning' )
plt.axis(ymin=0, ymax=60.0, xmin=0, xmax=len(durations_sec)-1)
plt.plot(smooth_data[WINDOW_LEN:-WINDOW_LEN], "r-", label="shot length (in seconds)")
plt.plot(trend_duration(numpy.arange(len(data))), "m-", label="shot length trend")
plt.legend(loc="upper left")
data = numpy.array(WINDOW_LEN*[motions[0]] + motions + WINDOW_LEN*[0])
trend_motion = numpy.polyfit(range(len(data)), data, TREND_DEGREE)
trend_motion = numpy.poly1d(trend_motion)
smooth_data = smooth(data, window_len=WINDOW_LEN, window='hanning')
plt.xlabel("shot / %d" % (len(durations)))
plt.twinx()
plt.axis(ymin=0, ymax=1.0)
plt.plot(smooth_data[WINDOW_LEN:-WINDOW_LEN], "b-", label="motion (0..1)")
plt.plot(trend_motion(numpy.arange(len(data))), "c-", label="motion trend")
plt.legend(loc="upper right")
plt.show()
# ===== TEST ==================================================================================================
if False:
smooth_duration = 0.5 * smooth_data / numpy.max(smooth_data)
smooth_deriv = 100 * smooth( numpy.diff( smooth_data ), window_len=10*WINDOW_LEN )[WINDOW_LEN:-WINDOW_LEN]
smooth_motion = smooth_data[WINDOW_LEN:-WINDOW_LEN]
'''for x, y in enumerate(smooth_deriv):
m = smooth_motion[x]
if x % 2 == 0:
plt.vlines(x, y-m, y+m, lw=m*2)
plt.plot(smooth_deriv, "w-", lw=1)
mini = min(len(smooth_deriv), len(smooth_motion))
plt.fill_between(range(mini), smooth_deriv[:mini], smooth_deriv[:mini]+smooth_motion[mini], color="y")
plt.axis(ymin=-1, ymax=1, xmin=0, xmax=len(durations_sec)-1)
plt.show()'''
# audio
f = open("..\\smooth_audio.txt", "r")
values = [float(line) for line in f if line]
f.close()
audio_step = float(len(values)) / float(len(smooth_deriv))
audio_counter = 0
fig = plt.figure()
ax = fig.add_subplot(111, polar=True)
STEP = math.ceil(0.01* len(smooth_deriv) / float(2*math.pi) )
for x, y in enumerate(smooth_deriv):
if x % STEP == 0:
x = 2*math.pi * float(x) / len(smooth_deriv)
y += 2
audio_value = 0.75 * values[int( audio_counter * audio_step )]
ax.vlines(x, y+0.01, y+0.01+audio_value, lw=audio_value*2, color="y")
audio_counter += 1
for x, y in enumerate(smooth_deriv):
m = smooth_motion[x]
#d = smooth_duration[x]
if x % STEP == 0:
x = 2*math.pi * float(x) / len(smooth_deriv)
y += 2
ax.vlines(x, y+0.01, y+0.01+m, lw=m*2)
"""audio_value = 0.75 * values[int( audio_counter * audio_step )]
ax.vlines(x, y-0.01, y-0.01-audio_value, lw=audio_value*2)
audio_counter += 1"""
plt.show()
# ===== RADAR ==================================================================================================
asl = numpy.mean(durations)
std = numpy.std(durations)
avg_motion = numpy.mean(motions)
properties = {}
properties["duration"] = percent
#properties["average shot length"] = 0.1 * asl / float(fps)
properties["cuts / minute"] = (len(durations) / m) / 20.0
properties["average motion"] = avg_motion / 0.25
properties["words / minute"] = words_per_minute / 60.0
properties["average loudness"] = 0.5
angle_step = 360.0 / len(properties)
angles = []
for i in range(len(properties)):
angles.append(math.radians(i*angle_step))
fig = plt.figure()
ax = fig.add_subplot(111, polar=True)
ax.set_rmax(5.0)
ax.set_xticks([i*2*math.pi/len(properties) for i in range(len(properties))])
ax.set_xticklabels(properties.keys())
ax.plot(angles, properties.values())
for i in range(len(properties)):
ax.vlines(angles[i], 0, properties[properties.keys()[i]], lw=15)
#ax.axis("off")
plt.show()
"""
# ===== COLOR ==================================================================================================
f = open("colors.txt", "r")
colors = [[int(values[0]), int(values[1]), int(values[2]), int(values[3])] for values in [line.split(", ") for line in f if line]]
f.close()
#print colors
x = numpy.arange(0, 2*math.pi, 2*math.pi/len(colors))
#print x
y = [values[3] for values in [color for color in colors]]
#print y
total = sum(y)
for i, yps in enumerate(y):
faktor = float(yps) / total
y[i] = math.sqrt(faktor * total*total)
fig = plt.figure()
ax = fig.add_subplot(111, polar=True)
for i, color in enumerate(colors):
ax.bar(x[i], y[i], width=0.2*math.pi, edgecolor="none", color=(color[0]/255.0, color[1]/255.0, color[2]/255.0))
ax.axis("off")
plt.show()
"""
#raw_input("- done -")
return
# #########################
if __name__ == "__main__":
main()
# #########################