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analyze.py
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analyze.py
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################################################
## 2. analyze hitting points
## get data of which object they are looking at
################################################
import matplotlib.pyplot as plt
import csv
import os
import sys
import json
from shapely.geometry import Point, Polygon
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import patches
################################################
# global variables
################################################
interval = 500000 # half second
scene_dict = {0:'p1', 1:'p2', 2:'scene1', 3:'scene2', 4:'scene3', 5:'scene4', 6:'scene5', 7:'scene6', 8:'scene7', 9:'scene8', 10:'scene9', 11:'scene10', 12:'scene11', 13:'scene12'}
id_set_dict = {'A': 1, 'B':2}
stamp1 = pd.read_csv("stamps/stamp1.csv")
stamp2 = pd.read_csv("stamps/stamp2.csv")
count = 0
################################################
# remove string does not contain sym from list
################################################
def remove_from_list(alist, sym):
for item in alist:
if sym not in item:
alist.remove(item)
################################################
# include string contain sym from list
################################################
def include_to_list(alist, sym):
temp = []
for item in alist:
if sym in item:
temp.append(item)
return temp
################################################
# convert a list of list to a list of tuple
################################################
def convert_to_tuple_list(alist):
temp = []
for item in alist:
temp.append(tuple(item))
return temp
################################################
# convert rectangle(2 points) to polygon (4points)
################################################
def convert_to_box(points):
temp = []
t1 = points[0]
t2 = points[1]
if t1[0] < t2[0]:
x1 = t1[0]
x2 = t2[0]
else:
x2 = t1[0]
x1 = t2[0]
if t1[1] < t2[1]:
y1 = t1[1]
y2 = t2[1]
else:
y2 = t1[1]
y1 = t2[1]
temp.append((x1, y1))
temp.append((x2, y1))
temp.append((x2, y2))
temp.append((x1, y2))
return temp
# print(os.listdir('/Users/yuanguo/MHC/BEARS LAB/images/demo1/scene1/flip'))
# assert (False)
annot_dir = '/Users/yuanguo/MHC/BEARS LAB/images/demo'
data_dir_name = '/Users/yuanguo/MHC/BEARS LAB/data/PUF WSU DATA 2'
print ("the folder is %s" % (data_dir_name))
tobii_eye_folder_name = 'EYE2'
tobii_path = ''
participants_ids = os.listdir(data_dir_name)
# opens eye tracking data folder which contains
# 2d hitting points we just generated
for par_id in participants_ids:
# files are 001, 002
if len(par_id) == 3:
single_par_folder = os.listdir(data_dir_name + '/' + par_id)
hit_folder_path = data_dir_name + '/' + par_id +'/'+ par_id+'_HIT'
print(hit_folder_path)
if not os.path.exists(hit_folder_path):
os.makedirs(hit_folder_path)
for single_file in single_par_folder:
if tobii_eye_folder_name in single_file:
tobii_path = data_dir_name + '/' + par_id + '/' + single_file
print('current in : '+tobii_path) #/.../WSU_ED_001_EYE
file_list = os.listdir(tobii_path)
remove_from_list(file_list, 'csv')
file_list.sort() # sort by time
print('This folder has ', len(file_list), ' files: ',file_list )
for i in range(2, len(file_list)):
# read timestamp and hitting point csv file
time_eye_dict = {}
f = open(tobii_path + '/' + file_list[i], 'r', encoding="utf-8")
reader = csv.reader(f)
start_timestamp = 0
for row in reader:
if start_timestamp ==0:
start_timestamp = int(row[0])
time_eye_dict[int(row[0])] = [float(row[1]), float(row[2])]
# read annotation files
result = pd.read_csv("result.csv")
# find which set this participant was using setA or setB
set = result['set'][np.where(result['id'] == int(par_id))[0][0]]
print('id: ',par_id,', set: ', set, ', i: ', i)
if set == 'A':
df = stamp1
else:
df = stamp2
annot_dir_curr = annot_dir + str(id_set_dict[set])
# find which scene
curr_scene = scene_dict[i]
annot_dir_curr = annot_dir_curr + '/'+curr_scene + '/' + 'flip'
poly_file_names = os.listdir(annot_dir_curr)
poly_json_file_names = include_to_list(poly_file_names, 'json')
poly_json_file_names.sort()
result_scene_dict = {'timestamp':[], 'image':[]}
for key, value in time_eye_dict.items():
hitting_point = Point(value[0], value[1])
# plt.scatter([value[0]], [value[1]], s=0.5, color='b')
curr_img = 'image'
curr_time = (key - start_timestamp) / interval
curr_time = int(curr_time)
num = ''
if curr_time <= df['start1'][i]*2:
num= str(curr_time).zfill(2)
elif df['start1'][i]*2 < curr_time < df['end1'][i]*2:
num= str(int(df['start1'][i]*2)).zfill(2)
elif df['end1'][i]*2 <= curr_time <= df['start2'][i]*2:
num= str(int(curr_time - 2*(df['end1'][i] - df['start1'][i]))).zfill(2)
elif df['start2'][i]*2 < curr_time < df['end2'][i]*2:
num = str(int(df['start2'][i]*2- 2*(df['end1'][i] - df['start1'][i]))).zfill(2)
elif curr_time >= df['end2'][i]*2 and df['end2'][i] != 0:
num = str(int(curr_time - 2 * (df['end2'][i] - df['start2'][i]) - 2*(df['end1'][i] - df['start1'][i]))).zfill(2)
elif curr_time >= df['end1'][i]*2 and df['start2'][i] == 0:
num = str(int(curr_time - 2*(df['end1'][i] - df['start1'][i]))).zfill(2)
result_scene_dict['timestamp'].append(key)
result_scene_dict['image'].append(num)
if os.path.exists(annot_dir_curr + '/' + curr_img+num + '.json'):
curr_img += num
else:
curr_img = prev
###
flag_img = False
if os.path.exists('pics/'+curr_scene+'_'+curr_img+'.png') == False:
flag_img = True
plt.figure()
im = plt.imread(annot_dir_curr + '/' + curr_img + '.jpg')
fig, ax = plt.subplots(1)
ax.imshow(im)
plt.scatter([value[0]], [value[1]], s=1, color='g')
####
with open(annot_dir_curr + '/' + curr_img + '.json') as img_f:
d = json.load(img_f)
for shape in d['shapes']:
points = shape['points']
points = convert_to_tuple_list(points)
label = shape['label']
if len(points) > 2:
poly = Polygon(points)
else:
poly = Polygon(convert_to_box(points))
if hitting_point.within(poly) == True:
flag = 'True'
else:
flag = 'False'
if label in result_scene_dict:
while len(result_scene_dict[label]) < (len(result_scene_dict['timestamp'])-1):
result_scene_dict[label].append('NA')
result_scene_dict[label].append(flag)
else:
result_scene_dict[label] = ['NA']*(len(result_scene_dict['timestamp'])-1)
result_scene_dict[label].append(flag)
####
if flag_img ==True:
x, y = poly.exterior.coords.xy
draw_points = np.array([x, y], np.int32).T
polygon_shape = patches.Polygon(draw_points, linewidth=1, edgecolor='r', facecolor='none')
ax.add_patch(polygon_shape)
####
prev = curr_img
########
if flag_img ==True:
plt.savefig('pics/' + curr_scene+'_'+curr_img + '.png')
plt.close(fig)
count += 1
########
curr_df = pd.DataFrame(result_scene_dict)
curr_df.to_csv(hit_folder_path + '/'+file_list[i].split('.')[0]+'_'+curr_scene+'.csv', sep=',', encoding='utf-8')
# assert (False)
print('finish')