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show_plot.py
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show_plot.py
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import os
import random
import sys
import time
import traceback
from datetime import datetime
import numpy as np
import pandas as pd
from bokeh.client import push_session
from bokeh.driving import cosine
from bokeh.models import Label
from bokeh.plotting import curdoc, figure, output_file, show
from imageio import imread
from joblib import Parallel, delayed
from scipy.misc import imresize
from toolz import partial
import face_api
import get_words
import train
def read_image(
filename="faces/01F_DI_O.png",
percent_scale=0.5,
center=None,
width=None,
height=None,
):
rgba = imread(filename).astype("uint8")
if center and width and height:
c = (int(center[0] * rgba.shape[0] / 100), int(center[1] * rgba.shape[1] / 100))
w = int(4 * width / 100 * rgba.shape[1])
h = int(1.5 * height / 100 * rgba.shape[0])
horiz = [c[0] - w // 2, c[0] + w // 2]
vert = [c[1] - h // 2, c[1] + h // 2]
horiz = np.clip(horiz, 0, rgba.shape[0])
vert = np.clip(vert, 0, rgba.shape[1])
print("--------------")
print(rgba.shape)
# rgba = rgba[horiz[0]:horiz[1], vert[0]:vert[1]]
print(rgba.shape)
else:
rgba = imresize(rgba, percent_scale)
if rgba.shape[-1] == 3:
a = 255 * np.ones(rgba[..., 0].shape, dtype="uint8")
out = np.zeros(rgba[..., 0].shape + (4,), dtype="uint8")
out[..., :3] = rgba
out[..., 3] = a
rgba = out
img = np.empty(rgba[:, :, 0].shape, dtype="uint32")
view = img.view(dtype=np.uint8).reshape((rgba.shape))
view[..., :4] = rgba[..., :4]
return img
def generate_initial_plot(test=False, n_imgs=-1, img_width=0.5, dim=None):
# output_file("embedding.html")
e = pd.read_csv("train-files/embedding.csv", index_col=0)
e = e.reindex(np.random.permutation(e.index))
x_lim = (-1.1, 1.3)
y_lim = (-1.2, 1.3)
d = []
for filename in e.index:
person, emotion, mouth = filename.split("_")
coords = [e.T[filename][k] for k in ["x", "y"]]
d += [
{
"person": person,
"emotion": emotion,
"mouth": mouth,
"filename": filename,
"coords": coords,
}
]
df = pd.DataFrame(d)
d = dict(df.T)
if dim is None:
height = 600
dim = (int(height * 1.6), height)
width, height = dim
p = figure(plot_width=width, plot_height=height, x_range=x_lim, y_range=y_lim)
emotions = {"happy": (-1, -1), "calm": (-1, 1), "sad": (1, 0.5), "rage": (1, -1)}
for emotion, (x, y) in emotions.items():
w = Label(
x=x,
y=y,
text=emotion,
text_color="red",
text_font_size="22pt",
# border_line_color='black', border_line_alpha=0.5,
background_fill_color="white",
background_fill_alpha=0.8,
)
p.add_layout(w)
# locs = pd.read_csv('training_feature_locs.csv')
for i, filename in enumerate(e.index[:n_imgs]):
if any([face in filename for face in ["40M_AN_O", "20M_FE_O"]]):
continue
loc = e.loc[filename, :]
img = read_image(filename="faces/" + filename + ".png")
img = img[::-1, :] # upside down because jpeg
p.image_rgba(
image=[img],
x=[loc[0]],
y=[loc[1]],
dw=[0.62 * img_width],
dh=[1.2 * img_width],
)
return p
def predict(url, verbose=False):
if verbose:
print("Entering show_plot.predict")
print("Finding face feature vectors...")
x = face_api.distances(url)
if verbose:
print("Model predicting...")
y = train.model.predict(x.reshape(1, -1))
if np.linalg.norm(y) > 1:
y /= np.linalg.norm(y)
# y = np.clip(y, -1, 1)
if verbose:
print("Exiting show_plot.predict")
return y.flat[:]
def update_plot(img_name="webcam.png"):
print("Press {enter, space} to read webcam.png")
# getch = Getch()
# key = ord(getch()) # input()
if key in {13, 32}:
# print("Taking picture...")
# os.system("automator take_webcam_photo.workflow")
# print("Picture taken")
_update_plot()
def _update_plot():
global img_name
crop_image = True
img = read_image(img_name)
img = img[::-1, :]
print("Uploading image...")
try:
y = predict(img_name, verbose=True)
except:
err = sys.exc_info()[0]
print("Error embedding face")
print("**** EXCEPTION! show_plot.py#L95, error = \n{}".format(err))
print(traceback.format_exc())
crop_image = False
y = np.random.randn(2)
y /= np.linalg.norm(y) * 2
print("Finding the words...")
words = get_words.find_words(y)
print(words)
# ds_words.data.update(x=y[0], y=y[1], text=", ".join(words))
w.x = y[0]
w.y = y[1]
w.text = ", ".join(words)
ds.data.update(x=[y[0]], y=[y[1]], image=[img])
print(y, w)
if __name__ == "__main__":
np.random.seed(42)
webcam_img = False
# session = push_session(curdoc())
# no_show = True
# output_server('embedding')
p = generate_initial_plot(test=True, n_imgs=50, width=0.3)
if True:
# import pdb; pdb.set_trace()
img_name = "webcam.png"
# img = run applescript to take image
img = read_image(img_name)
img = img[::-1, :]
# take img and coords and update plot
y = [0, 0]
width = 0.30
e = p.image_rgba(
image=[img], x=[y[0]], y=[y[1]], dw=[1.3 * width * 0.82], dh=[1.3 * width]
)
words = ["neutral", "calm"]
w = Label(
x=0,
y=0,
text=", ".join(words),
border_line_color="red",
border_line_alpha=0.5,
background_fill_color="white",
background_fill_alpha=0.8,
)
# word_text = w.data_source
p.add_layout(w)
# ds_words = w.data_source
ds = e.data_source
# Old implementation for 2016 WISciFest
session = push_session(curdoc())
session.show(p)
curdoc().add_periodic_callback(update_plot, 1000)
session.loop_until_closed()