import csv
import glob
import json
import math
import numpy
import os.path
from scipy.optimize import curve_fit
from Utils import getJsonData
import Image, ImageDraw
normalized = getJsonData("../data/results/normalized.json")
imgsize = 500
img = Image.new("RGB", (imgsize, imgsize), "black")
draw = ImageDraw.Draw(img)
for frame in normalized["frames"]:
# show a single frame
if frame["animationPercent"] < 1:
continue
for vertex in frame["vertices"]:
color = "blue"
if vertex["name"] == "knee-front":
color = "green"
if vertex["name"] == "foot-front":
color = "yellow"
if vertex["name"] == "waist":
color = "red"
}
for vidx in range(len(frame1["vertices"])):
newf["vertices"].append(copy.deepcopy(frame1["vertices"][vidx]))
for coordinate in ["x", "y"]:
meanValue = None
if frame1["vertices"][vidx][coordinate] != None:
meanValue = numpy.mean([frame1["vertices"][vidx][coordinate], frame2["vertices"][vidx][coordinate]])
newf["vertices"][vidx][coordinate] = meanValue
newFrames.append(newf)
frames.extend(newFrames)
initialData = getJsonData(files[0])
finalData = {"vertices" : initialData["vertices"], "lines" : initialData["lines"], "frames" : []}
minStepWidth = 10000
maxStepWidth = 0
for filename in files:
data = getJsonData(filename)
fileProperties = properties[os.path.basename(os.path.splitext(filename)[0])]
normalizeAnimation(data, fileProperties)
if allowInterpolatedData: addInterpolatedData(data)
minStepWidth = min(data["stepWidth"], minStepWidth)
maxStepWidth = max(data["stepWidth"], maxStepWidth)