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eval_camera.py
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import sys
sys.path.insert(0, 'src')
import transform
import numpy as np
import tensorflow as tf
import cv2
import argparse
import time
import dlib
import math
from imutils.video import VideoStream
from src.client import send_image
def setup_parser():
"""Options for command-line input."""
parser = argparse.ArgumentParser(description="""Use a trained fast style
transfer model to filter webcam feed.""")
parser.add_argument('--capture_device', type=int, default=0)
parser.add_argument('--fullscreen', action="store_true", default=False)
parser.add_argument('--vertical', action="store_true", default=False)
parser.add_argument('--timeout_style', help='How many seconds to wait before switching to next style', default=30)
parser.add_argument('--timeout_face', help='How many seconds to wait before taking photo', default=5)
parser.add_argument('--timeout_qr', help='How many seconds to show output image with qr', default=10)
parser.add_argument('--server_url', help='Server url for uploading images', default="http://magicmirror.cs.ut.ee/uploadImage")
parser.add_argument('--stylize_preview', action="store_true", default=False)
parser.add_argument('--detect_faces', action="store_true", default=False)
return parser
def read_orig_image(index):
orig_im = cv2.imread("./styles/"+styles[index]+".jpg")
factory = 240. / orig_im.shape[0]
factorx = 240. / orig_im.shape[1]
factor = min(factorx, factory)
orig_im = cv2.resize(orig_im, (0, 0), fx=factor, fy=factor, interpolation=cv2.INTER_AREA)
orig_im = np.pad(orig_im, ((y_new - 400 - orig_im.shape[0] + 30, 0), (0, x_new - orig_im.shape[1]), (0,0)), 'constant')
text_size_ln1 = cv2.getTextSize(titles[index],cv2.FONT_HERSHEY_SIMPLEX,1,0)[0]
text_size_ln2 = cv2.getTextSize("by "+authors[index],cv2.FONT_HERSHEY_SIMPLEX,1,0)[0]
cv2.putText(orig_im, titles[index], (orig_im.shape[1]-text_size_ln1[0], orig_im.shape[0]-(10+2*text_size_ln1[1])), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 1, lineType=cv2.LINE_AA)
cv2.putText(orig_im, "by "+authors[index], (orig_im.shape[1]-text_size_ln2[0], orig_im.shape[0]-10), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 1, lineType=cv2.LINE_AA)
return orig_im
# displays clock-similar animation next to original style image
def show_timer(start_time, timeout, orig_im, radius, color, reverse):
center = (orig_im.shape[1]-(radius+3), 30+radius)
if reverse:
cv2.circle(orig_im, center, radius, (0,0,0), thickness=-1, lineType=cv2.LINE_AA)
cv2.circle(orig_im, center, radius, color, thickness=1, lineType=cv2.LINE_AA)
cv2.ellipse(orig_im, center, (radius, radius), -90, 0, 360 - 360/timeout*math.floor(time.time() - start_time), color, -1)
else:
cv2.circle(orig_im, center, radius, color, thickness=1, lineType=cv2.LINE_AA)
cv2.ellipse(orig_im, center, (radius, radius), -90, 0, 360/timeout*math.floor(time.time() - start_time), color, -1)
def clear_timer(orig_im, radius):
center = (orig_im.shape[1]-(radius+3), 30+radius)
radius += 3
cv2.circle(orig_im, center, radius, (0,0,0), thickness=-1, lineType=cv2.LINE_AA)
def pad_im(img):
padx = (540 - img.shape[1]) // 2
pady = (960 - img.shape[0]) // 2 #requires adjusting, so the logo can fit at the top
return np.pad(img, ((pady, pady), (padx, padx), (0, 0)), "constant")
def add_logo(img):
logo = cv2.imread("ut_logo.png")
# check if resizing factors are not too small
logo = cv2.resize(logo, (0, 0), fx=0.35, fy=0.35, interpolation=cv2.INTER_AREA)
y_offset = 10
x_offset = img.shape[1]//2 - logo.shape[1]//2
img[y_offset:y_offset + logo.shape[0], x_offset:x_offset + logo.shape[1]] = logo
return img
def add_qr(qr_img, dest_img):
qr_ndarray = np.array(qr_img, dtype=np.float32) * 255
qr = cv2.cvtColor(qr_ndarray, cv2.COLOR_GRAY2BGR)
dest_img[30:30+qr.shape[0], 351:(351+qr.shape[1]), :] = qr
def stylize_frame(frame):
img_4d = frame[np.newaxis, :]
# Our operations on the frame come here
img_out = sess.run(Y, feed_dict={X: img_4d})
img_out = np.clip(img_out, 0, 255)
img_out = np.squeeze(img_out).astype(np.uint8)
return cv2.cvtColor(img_out, cv2.COLOR_BGR2RGB)
def stylize_and_output(cap, sess, saver, next):
default_radius = 13
print('Loading up model...')
saver.restore(sess, "./models/"+styles[next]+".ckpt")
print('Begin filtering...')
# init original style image
orig_im = read_orig_image(next)
face_start_time = 0
style_start_time = 0
qr_img = None
timer_color = (200,200,200)
while(True):
# Capture frame-by-frame
#ret, frame = cap.read()
frame = cap.read()
img_out = frame
orig_frame = frame
if args.stylize_preview:
img_out = stylize_frame(frame)
if args.vertical:
frame = np.swapaxes(frame, 0, 1)
img_out = np.swapaxes(img_out, 0, 1)
with_style = np.concatenate((img_out, orig_im), axis=0)
with_style = pad_im(with_style)
add_logo(with_style)
# Display the resulting frame
cv2.imshow('result', with_style)
if args.detect_faces:
# If face detected, start countdown to take a picture
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
rects = detector(gray, 0)
else:
show_timer(style_start_time, args.timeout_style, orig_im, default_radius, timer_color, False)
rects = []
if len(rects) > 0:
if face_start_time == 0:
face_start_time = time.time()
style_start_time = time.time()
show_timer(face_start_time, args.timeout_face, orig_im, default_radius, timer_color, False)
# Timeout passes
if time.time() - face_start_time > args.timeout_face:
img_old = np.swapaxes(orig_frame, 0, 1)
img_old = np.concatenate((img_old, orig_im), axis=0)
img_old = pad_im(img_old)
if not args.stylize_preview:
cv2.imshow('result', img_old)
cv2.waitKey(1)
stylized_im = stylize_frame(orig_frame)
img_out = np.swapaxes(stylized_im, 0, 1)
with_style = np.concatenate((img_out, orig_im), axis=0)
with_style = pad_im(with_style)
add_logo(with_style)
if args.stylize_preview:
for f in np.arange(0., 1.05, 0.05):
img = 255. * f + with_style * (1 - f)
cv2.imshow('result', img.astype(np.uint8))
cv2.waitKey(1)
for f in np.arange(0., 1.05, 0.05):
img = with_style * f + 255. * (1 - f)
cv2.imshow('result', img.astype(np.uint8))
cv2.waitKey(10)
else:
for f in np.arange(0, 1.05, 0.05):
img = with_style * f + img_old * (1 - f)
cv2.imshow('result', img.astype(np.uint8))
cv2.waitKey(20)
# Send output image to server
clear_timer(orig_im, default_radius)
output_im = np.concatenate((img_out, orig_im), axis=0)
qr_img = send_image(pad_im(output_im), args.server_url)
# Show image with QR and timer
freeze_start = time.time()
while(time.time() - args.timeout_qr < freeze_start):
clear_timer(orig_im, default_radius)
#show_timer(freeze_start, args.timeout_qr, orig_im, default_radius, timer_color, True)
add_qr(qr_img, orig_im)
cv2.imshow('result', pad_im(np.concatenate((img_out, orig_im), axis=0)))
cv2.waitKey(1000)
next = (next + 1) % len(styles)
saver.restore(sess, "./models/"+styles[next]+".ckpt")
orig_im = read_orig_image(next)
face_start_time = 0
style_start_time = time.time()
else:
if args.detect_faces:
face_start_time = 0
clear_timer(orig_im, default_radius)
key = cv2.waitKey(10)
if key == ord('d') or time.time() - style_start_time > args.timeout_style:
next = (next + 1) % len(styles)
orig_im = read_orig_image(next)
saver.restore(sess, "./models/"+styles[next]+".ckpt")
style_start_time = time.time()
if key == ord('a'):
next = (next - 1) % len(styles)-1
orig_im = read_orig_image(next)
saver.restore(sess, "./models/"+styles[next]+".ckpt")
style_start_time = time.time()
if key & 0xFF == ord('q'):
break
# When everything done, release the capture
#cap.release()
cap.stop()
sess.close()
cv2.destroyAllWindows()
if __name__ == '__main__':
# Command-line argument parsing.
parser = setup_parser()
args = parser.parse_args()
cap = VideoStream(args.capture_device).start()
frame = cap.read()
y_new, x_new, _ = frame.shape
print('Video resolution is: {0} by {1}'.format(x_new, y_new))
# Create the graph.
g = tf.Graph()
soft_config = tf.compat.v1.ConfigProto(allow_soft_placement=True)
soft_config.gpu_options.allow_growth = True
#soft_config.gpu_options.per_process_gpu_memory_fraction=0.33
shape = [1, y_new, x_new, 3]
# init authors, titles and styles
authors = ["P.Picasso", "A.Akberg", "L.Afremov", "E.Munch", "F.Picabia", "K.Hokusai", "W.Turner", "A. ja M.-K."]
titles = ["La Muse", "Toompea", "Rain Princess", "Scream", "Udnie", "The Wave", "The Shipwreck", "Wave"]
styles = ["la_muse", "akberg", "rain_princess", "scream", "udnie", "wave", "wreck", "new_style"]
# Create face detector
detector = dlib.get_frontal_face_detector()
#detector = dlib.cnn_face_detection_model_v1('mmod_human_face_detector.dat')
if args.vertical:
t = x_new
x_new = y_new
y_new = t
# open graph
with g.as_default():
X = tf.compat.v1.placeholder(tf.float32, shape=shape, name='img_placeholder')
Y = transform.net(X)
# restore the model to the session.
saver = tf.compat.v1.train.Saver()
if args.fullscreen:
cv2.namedWindow("result", cv2.WND_PROP_FULLSCREEN)
cv2.setWindowProperty("result", cv2.WND_PROP_FULLSCREEN, cv2.WINDOW_FULLSCREEN)
next = 0
sess = tf.compat.v1.Session(config=soft_config)
stylize_and_output(cap, sess, saver, next)