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import copy
import eyekit as ek
import numpy as np
import pandas as pd
from PIL import Image
MEASURES_DICT = {
"number_of_fixations": [],
"initial_fixation_duration": [],
"first_of_many_duration": [],
"total_fixation_duration": [],
"gaze_duration": [],
"go_past_duration": [],
"second_pass_duration": [],
"initial_landing_position": [],
"initial_landing_distance": [],
"landing_distances": [],
"number_of_regressions_in": [],
}
def get_fix_seq_and_text_block(
dffix,
trial,
x_txt_start=None,
y_txt_start=None,
font_face="Courier New",
font_size=None,
line_height=None,
use_corrected_fixations=True,
correction_algo="warp",
):
if use_corrected_fixations and correction_algo is not None:
fixations_tuples = [
(
(x[1]["x"], x[1][f"y_{correction_algo}"], x[1]["corrected_start_time"], x[1]["corrected_end_time"])
if x[1]["corrected_start_time"] < x[1]["corrected_end_time"]
else (x[1]["x"], x[1]["y"], x[1]["corrected_start_time"], x[1]["corrected_end_time"] + 1)
)
for x in dffix.iterrows()
]
else:
fixations_tuples = [
(
(x[1]["x"], x[1]["y"], x[1]["corrected_start_time"], x[1]["corrected_end_time"])
if x[1]["corrected_start_time"] < x[1]["corrected_end_time"]
else (x[1]["x"], x[1]["y"], x[1]["corrected_start_time"], x[1]["corrected_end_time"] + 1)
)
for x in dffix.iterrows()
]
try:
fixation_sequence = ek.FixationSequence(fixations_tuples)
except Exception as e:
print(e)
print(f"Creating fixation failed for {trial['trial_id']} {trial['filename']}")
return dffix
if "display_coords" in trial:
display_coords = trial["display_coords"]
else:
display_coords = (0, 0, 1920, 1080)
screen_size = ((display_coords[2] - display_coords[0]), (display_coords[3] - display_coords[1]))
y_diffs = np.unique(trial["line_heights"])
if len(y_diffs) == 1:
y_diff = y_diffs[0]
else:
y_diff = np.min(y_diffs)
chars_list = trial["chars_list"]
max_line = int(chars_list[-1]["assigned_line"])
words_on_lines = {x: [] for x in range(int(max_line) + 1)}
[words_on_lines[x["assigned_line"]].append(x["char"]) for x in chars_list]
sentence_list = ["".join([s for s in v]) for idx, v in words_on_lines.items()]
if x_txt_start is None:
x_txt_start = float(chars_list[0]["char_xmin"])
if y_txt_start is None:
y_txt_start = float(chars_list[0]["char_ymax"])
if font_face is None and "font" in trial:
font_face = trial["font"]
elif font_face is None:
font_face = "DejaVu Sans Mono"
if font_size is None and "font_size" in trial:
font_size = trial["font_size"]
elif font_size is None:
font_size = float(y_diff * 0.333) # pixel to point conversion
if line_height is None:
line_height = float(y_diff)
textblock = ek.TextBlock(
sentence_list,
position=(float(x_txt_start), float(y_txt_start)),
font_face=font_face,
line_height=line_height,
font_size=font_size,
anchor="left",
align="left",
)
# eyekit_plot(textblock, fixation_sequence, screen_size)
ek.io.save(fixation_sequence, f'results/fixation_sequence_eyekit_{trial["trial_id"]}.json', compress=False)
ek.io.save(textblock, f'results/textblock_eyekit_{trial["trial_id"]}.json', compress=False)
return fixation_sequence, textblock, screen_size
def eyekit_plot(textblock, fixation_sequence, screen_size):
img = ek.vis.Image(*screen_size)
img.draw_text_block(textblock)
for word in textblock.words():
img.draw_rectangle(word, color="hotpink")
img.draw_fixation_sequence(fixation_sequence)
img.save("temp_eyekit_img.png", crop_margin=200)
img_png = Image.open("temp_eyekit_img.png")
return img_png
def plot_with_measure(textblock, fixation_sequence, screen_size, measure, use_characters=False):
eyekitplot_img = eyekit_plot(textblock, fixation_sequence, screen_size)
eyekitplot_img = ek.vis.Image(*screen_size)
eyekitplot_img.draw_text_block(textblock)
if use_characters:
measure_results = getattr(ek.measure, measure)(textblock.characters(), fixation_sequence)
enum = textblock.characters()
else:
measure_results = getattr(ek.measure, measure)(textblock.words(), fixation_sequence)
enum = textblock.words()
for word in enum:
eyekitplot_img.draw_rectangle(word, color="lightseagreen")
x = word.onset
y = word.y_br - 3
label = f"{measure_results[word.id]}"
eyekitplot_img.draw_annotation((x, y), label, color="lightseagreen", font_face="Arial bold", font_size=15)
eyekitplot_img.draw_fixation_sequence(fixation_sequence, color="gray")
eyekitplot_img.save("multiline_passage_piccol.png", crop_margin=100)
img_png = Image.open("multiline_passage_piccol.png")
return img_png
def get_eyekit_measures(_txt, _seq, get_char_measures=False):
measures = copy.deepcopy(MEASURES_DICT)
words = []
for w in _txt.words():
words.append(w.text)
for m in measures.keys():
measures[m].append(getattr(ek.measure, m)(w, _seq))
word_measures_df = pd.DataFrame(measures)
word_measures_df["word_number"] = np.arange(0, len(words))
word_measures_df["word"] = words
first_column = word_measures_df.pop("word")
word_measures_df.insert(0, "word", first_column)
first_column = word_measures_df.pop("word_number")
word_measures_df.insert(0, "word_number", first_column)
if get_char_measures:
measures = copy.deepcopy(MEASURES_DICT)
characters = []
for c in _txt.characters():
characters.append(c.text)
for m in measures.keys():
measures[m].append(getattr(ek.measure, m)(c, _seq))
character_measures_df = pd.DataFrame(measures)
character_measures_df["char_number"] = np.arange(0, len(characters))
character_measures_df["character"] = characters
first_column = character_measures_df.pop("character")
character_measures_df.insert(0, "character", first_column)
first_column = character_measures_df.pop("char_number")
character_measures_df.insert(0, "char_number", first_column)
else:
character_measures_df = None
return word_measures_df, character_measures_df
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