def main(option):
start_time = time.time()
cfg = load_config("demo/pose_cfg_multi.yaml")
dataset = create_dataset(cfg)
sm = SpatialModel(cfg)
sm.load()
tf.reset_default_graph()
draw_multi = PersonDraw()
sess, inputs, outputs = predict.setup_pose_prediction(cfg)
fps_time = 0
# Read image from file
slopes = {}
k = 0
cap = cv2.VideoCapture("http://192.168.43.31:8081")
cap_user = cv2.VideoCapture('/dev/video0')
cap = cap_user
i = 0
while (True):
ret, orig_frame = cap.read()
ret2, orig_frame_user = cap_user.read()
if i % 25 == 0:
#frame=orig_frame
frame = cv2.resize(orig_frame, (0, 0), fx=0.50, fy=0.50)
user_frame = cv2.resize(orig_frame_user, (0, 0), fx=0.50, fy=0.50)
co1 = run_predict(frame, sess, outputs, inputs, cfg, dataset, sm,
draw_multi)
print("CO1 ", co1)
user_co1 = run_predict(user_frame, sess, outputs, inputs, cfg,
dataset, sm, draw_multi)
print("USER_CO1 ", user_co1)
print("CO1 ", co1)
k = None
try:
slope_reqd, slope_user = slope_calc(co1, user_co1)
k, s = compare_images(slope_reqd, slope_user, 0.75)
except IndexError:
#if len(co1)!=len(user_co1):
print("Except condition")
pass
vibrate(k)
frame = cv2.resize(frame, (0, 0), fx=2.0, fy=2.0)
user_frame = cv2.resize(user_frame, (0, 0), fx=2.0, fy=2.0)
cv2.putText(user_frame,
"FPS: %f" % (1.0 / (time.time() - fps_time)), (10, 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
cv2.imshow('user_frame', user_frame)
cv2.imshow('frame', frame)
fps_time = time.time()
#visualize.waitforbuttonpress()
if cv2.waitKey(10) == ord('q'):
break
elapsed = time.time() - start_time
cap.release()
cap_user.release()
cv2.destroyAllWindows()
def main(option):
cfg = load_config("demo/pose_cfg_multi.yaml")
dataset = create_dataset(cfg)
sm = SpatialModel(cfg)
sm.load()
tf.reset_default_graph()
draw_multi = PersonDraw()
sess, inputs, outputs = predict.setup_pose_prediction(cfg)
fps_time = 0
# Read image from file
cap = cv2.VideoCapture('msgifs/icon4.gif')
cap_user = cv2.VideoCapture('user.mp4')
i = 0
while (True):
ret, orig_frame = cap.read()
ret2, orig_frame_user = cap_user.read()
if i % 25 == 0:
frame = cv2.resize(orig_frame, (0, 0), fx=0.50, fy=0.50)
user_frame = cv2.resize(orig_frame_user, (0, 0), fx=0.50, fy=0.50)
co1 = run_predict(frame, sess, inputs, outputs, cfg, dataset, sm,
draw_multi)
user_co1 = run_predict(user_frame, sess, inputs, outputs, cfg,
dataset, sm, draw_multi)
try:
slope_reqd = slope_calc(co1)
slope_user = slope_calc(user_co1)
compare_images(slope_reqd, slope_user, 0.1)
except IndexError:
#if len(co1)!=len(user_co1):
#messagebox.showinfo("Title", "Please adjust camera to show your keypoints")
pass
#frame = cv2.resize(frame, (0, 0), fx=2.0, fy=2.0)
#user_frame = cv2.resize(user_frame, (0, 0), fx=2.0, fy=2.0)
cv2.putText(user_frame,
"FPS: %f" % (1.0 / (time.time() - fps_time)), (10, 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
cv2.imshow('user_frame', user_frame)
cv2.imshow('frame', frame)
fps_time = time.time()
#visualize.waitforbuttonpress()
if cv2.waitKey(10) == ord('q'):
break
cap.release()
cap_user.release()
cv2.destroyAllWindows()
cap_user.release()
font = ImageFont.truetype("./font/NotoSans-Bold.ttf", 12)
import random
# for object-tracker
import dlib
# import video_pose
####################
cfg = load_config("demo/pose_cfg_multi.yaml")
dataset = create_dataset(cfg)
sm = SpatialModel(cfg)
sm.load()
draw_multi = PersonDraw()
# Load and setup CNN part detector
sess, inputs, outputs = predict.setup_pose_prediction(cfg)
##########
## Get the source of video
parser = ap.ArgumentParser()
parser.add_argument('-f', "--videoFile", help="Path to Video File")
parser.add_argument('-w', "--videoWidth", help="Width of Output Video")
parser.add_argument('-o', "--videoType", help="Extension of Output Video")
def test_net(visualise, cache_scoremaps, development):
logging.basicConfig(level=logging.INFO)
cfg = load_config()
dataset = create_dataset(cfg)
dataset.set_shuffle(False)
sm = SpatialModel(cfg)
sm.load()
draw_multi = PersonDraw()
from_cache = "cached_scoremaps" in cfg
if not from_cache:
sess, inputs, outputs = setup_pose_prediction(cfg)
if cache_scoremaps:
out_dir = cfg.scoremap_dir
if not os.path.exists(out_dir):
os.makedirs(out_dir)
pairwise_stats = dataset.pairwise_stats
num_images = dataset.num_images if not development else min(
10, dataset.num_images)
coco_results = []
for k in range(num_images):
print('processing image {}/{}'.format(k, num_images - 1))
batch = dataset.next_batch()
cache_name = "{}.mat".format(batch[Batch.data_item].coco_id)
if not from_cache:
outputs_np = sess.run(outputs,
feed_dict={inputs: batch[Batch.inputs]})
scmap, locref, pairwise_diff = extract_cnn_output(
outputs_np, cfg, pairwise_stats)
if cache_scoremaps:
if visualise:
img = np.squeeze(batch[Batch.inputs]).astype('uint8')
pose = argmax_pose_predict(scmap, locref, cfg.stride)
arrows = argmax_arrows_predict(scmap, locref,
pairwise_diff, cfg.stride)
visualize.show_arrows(cfg, img, pose, arrows)
visualize.waitforbuttonpress()
continue
out_fn = os.path.join(out_dir, cache_name)
dict = {
'scoremaps': scmap.astype('float32'),
'locreg_pred': locref.astype('float32'),
'pairwise_diff': pairwise_diff.astype('float32')
}
scipy.io.savemat(out_fn, mdict=dict)
continue
else:
# cache_name = '1.mat'
full_fn = os.path.join(cfg.cached_scoremaps, cache_name)
mlab = scipy.io.loadmat(full_fn)
scmap = mlab["scoremaps"]
locref = mlab["locreg_pred"]
pairwise_diff = mlab["pairwise_diff"]
detections = extract_detections(cfg, scmap, locref, pairwise_diff)
unLab, pos_array, unary_array, pwidx_array, pw_array = eval_graph(
sm, detections)
person_conf_multi = get_person_conf_multicut(sm, unLab, unary_array,
pos_array)
if visualise:
img = np.squeeze(batch[Batch.inputs]).astype('uint8')
# visualize.show_heatmaps(cfg, img, scmap, pose)
"""
# visualize part detections after NMS
visim_dets = visualize_detections(cfg, img, detections)
plt.imshow(visim_dets)
plt.show()
visualize.waitforbuttonpress()
"""
# """
visim_multi = img.copy()
draw_multi.draw(visim_multi, dataset, person_conf_multi)
plt.imshow(visim_multi)
plt.show()
visualize.waitforbuttonpress()
# """
if cfg.use_gt_segm:
coco_img_results = pose_predict_with_gt_segm(
scmap, locref, cfg.stride, batch[Batch.data_item].gt_segm,
batch[Batch.data_item].coco_id)
coco_results += coco_img_results
if len(coco_img_results):
dataset.visualize_coco(coco_img_results,
batch[Batch.data_item].visibilities)
if cfg.use_gt_segm:
with open('predictions_with_segm.json', 'w') as outfile:
json.dump(coco_results, outfile)
sess.close()
from nnet import predict
from util import visualize
from dataset.pose_dataset import data_to_input
from multiperson.detections import extract_detections
from multiperson.predict import SpatialModel, eval_graph, get_person_conf_multicut
from multiperson.visualize import PersonDraw, visualize_detections
import matplotlib.pyplot as plt
cfg = load_config("demo/pose_cfg_multi.yaml")
dataset = create_dataset(cfg)
sm = SpatialModel(cfg)
sm.load()
draw_multi = PersonDraw()
# Load and setup CNN part detector
sess, inputs, outputs = predict.setup_pose_prediction(cfg)
# Read image from file
file_name_ext = sys.argv[1] ## example: test_single_03.png
file_name = file_name_ext.split('.')[0] ## example: test_single_03
file_name_input = 'testset/' + file_name_ext
image = imread(file_name_input, mode='RGB')
image_batch = data_to_input(image)
def main():
start_time=time.time()
print("main hai")
tf.reset_default_graph()
cfg = load_config("demo/pose_cfg_multi.yaml")
dataset = create_dataset(cfg)
sm = SpatialModel(cfg)
sm.load()
draw_multi = PersonDraw()
# Load and setup CNN part detector
sess, inputs, outputs = predict.setup_pose_prediction(cfg)
# Read image from file
dir=os.listdir("stick")
k=0
cap=cv2.VideoCapture(0)
i=0
while (cap.isOpened()):
if i%20 == 0:
ret, orig_frame= cap.read()
if ret==True:
frame = cv2.resize(orig_frame, (0, 0), fx=0.30, fy=0.30)
image= frame
sse=0
mse=0
image_batch = data_to_input(frame)
# Compute prediction with the CNN
outputs_np = sess.run(outputs, feed_dict={inputs: image_batch})
scmap, locref, pairwise_diff = predict.extract_cnn_output(outputs_np, cfg, dataset.pairwise_stats)
detections = extract_detections(cfg, scmap, locref, pairwise_diff)
unLab, pos_array, unary_array, pwidx_array, pw_array = eval_graph(sm, detections)
person_conf_multi = get_person_conf_multicut(sm, unLab, unary_array, pos_array)
img = np.copy(image)
#coor = PersonDraw.draw()
visim_multi = img.copy()
co1=draw_multi.draw(visim_multi, dataset, person_conf_multi)
plt.imshow(visim_multi)
plt.show()
visualize.waitforbuttonpress()
#print("this is draw : ", co1)
if k==1:
qwr = np.zeros((1920,1080,3), np.uint8)
cv2.line(qwr, co1[5][0], co1[5][1],(255,0,0),3)
cv2.line(qwr, co1[7][0], co1[7][1],(255,0,0),3)
cv2.line(qwr, co1[6][0], co1[6][1],(255,0,0),3)
cv2.line(qwr, co1[4][0], co1[4][1],(255,0,0),3)
cv2.line(qwr, co1[9][0], co1[9][1],(255,0,0),3)
cv2.line(qwr, co1[11][0], co1[11][1],(255,0,0),3)
cv2.line(qwr, co1[8][0], co1[8][1],(255,0,0),3)
cv2.line(qwr, co1[10][0], co1[10][1],(255,0,0),3)
# In[9]:
cv2.imshow('r',qwr)
qwr2="stick/frame"+str(k)+".jpg"
qw1 = cv2.cvtColor(qwr, cv2.COLOR_BGR2GRAY)
qw2= cv2.cvtColor(qwr2, cv2.COLOR_BGR2GRAY)
fig = plt.figure("Images")
images = ("Original", qw1), ("Contrast", qw2)
for (i, (name, image)) in enumerate(images):
ax = fig.add_subplot(1, 3, i + 1)
ax.set_title(name)
plt.imshow(hash(tuple(image)))
# compare the images
s,m=compare_images(qw1, qw2, "Image1 vs Image2")
k+=1
sse=s
mse=m
else:
break
elapsed= time.time()-start_time
#print("sse score : ", sse)
print("Mean squared error : ", elapsed/100)
cap.release()
cv2.destroyAllWindows()
def video2poseframe(video_name):
import numpy as np
sys.path.append(os.path.dirname(__file__) + "/../")
from scipy.misc import imread, imsave
from config import load_config
from dataset.factory import create as create_dataset
from nnet import predict
from util import visualize
from dataset.pose_dataset import data_to_input
from multiperson.detections import extract_detections
from multiperson.predict import SpatialModel, eval_graph, get_person_conf_multicut
from multiperson.visualize import PersonDraw, visualize_detections
import matplotlib.pyplot as plt
from PIL import Image, ImageDraw
import random
cfg = load_config("demo/pose_cfg_multi.yaml")
dataset = create_dataset(cfg)
sm = SpatialModel(cfg)
sm.load()
# Load and setup CNN part detector
sess, inputs, outputs = predict.setup_pose_prediction(cfg)
################
video = read_video(video_name)
video_frame_number = int(video.duration * video.fps) ## duration: second / fps: frame per second
video_frame_ciphers = math.ceil(math.log(video_frame_number, 10)) ## ex. 720 -> 3
if not os.path.exists('testset/' + video_name):
os.makedirs('testset/' + video_name)
for i in range(0, video_frame_number):
image = video.get_frame(i/video.fps)
######################
image_batch = data_to_input(image)
# Compute prediction with the CNN
outputs_np = sess.run(outputs, feed_dict={inputs: image_batch})
scmap, locref, pairwise_diff = predict.extract_cnn_output(outputs_np, cfg, dataset.pairwise_stats)
detections = extract_detections(cfg, scmap, locref, pairwise_diff)
unLab, pos_array, unary_array, pwidx_array, pw_array = eval_graph(sm, detections)
person_conf_multi = get_person_conf_multicut(sm, unLab, unary_array, pos_array)
print('person_conf_multi: ')
print(type(person_conf_multi))
print(person_conf_multi)
# Add library to save image
image_img = Image.fromarray(image)
# Save image with points of pose
draw = ImageDraw.Draw(image_img)
people_num = 0
point_num = 17
print('person_conf_multi.size: ')
print(person_conf_multi.size)
people_num = person_conf_multi.size / (point_num * 2)
people_num = int(people_num)
print('people_num: ')
print(people_num)
point_i = 0 # index of points
point_r = 5 # radius of points
people_real_num = 0
for people_i in range(0, people_num):
point_color_r = random.randrange(0, 256)
point_color_g = random.randrange(0, 256)
point_color_b = random.randrange(0, 256)
point_color = (point_color_r, point_color_g, point_color_b, 255)
point_count = 0
for point_i in range(0, point_num):
if person_conf_multi[people_i][point_i][0] + person_conf_multi[people_i][point_i][1] != 0: # If coordinates of point is (0, 0) == meaningless data
point_count = point_count + 1
if point_count > 5: # If there are more than 5 point in person, we define he/she is REAL PERSON
people_real_num = people_real_num + 1
for point_i in range(0, point_num):
draw.ellipse((person_conf_multi[people_i][point_i][0] - point_r, person_conf_multi[people_i][point_i][1] - point_r, person_conf_multi[people_i][point_i][0] + point_r, person_conf_multi[people_i][point_i][1] + point_r), fill=point_color)
print('people_real_num: ')
print(people_real_num)
video_name_result = 'testset/' + video_name + '/frame_pose_' + str(i).zfill(video_frame_ciphers) + '.jpg'
image_img.save(video_name_result, "JPG")
def video2posevideo(video_name):
time_start = time.clock()
import numpy as np
sys.path.append(os.path.dirname(__file__) + "/../")
from scipy.misc import imread, imsave
from config import load_config
from dataset.factory import create as create_dataset
from nnet import predict
from util import visualize
from dataset.pose_dataset import data_to_input
from multiperson.detections import extract_detections
from multiperson.predict import SpatialModel, eval_graph, get_person_conf_multicut
from multiperson.visualize import PersonDraw, visualize_detections
import matplotlib.pyplot as plt
from PIL import Image, ImageDraw, ImageFont
font = ImageFont.truetype("./font/NotoSans-Bold.ttf", 24)
import random
cfg = load_config("demo/pose_cfg_multi.yaml")
dataset = create_dataset(cfg)
sm = SpatialModel(cfg)
sm.load()
draw_multi = PersonDraw()
# Load and setup CNN part detector
sess, inputs, outputs = predict.setup_pose_prediction(cfg)
################
video = read_video(video_name)
video_frame_number = int(video.duration * video.fps) ## duration: second / fps: frame per second
video_frame_ciphers = math.ceil(math.log(video_frame_number, 10)) ## ex. 720 -> 3
pose_frame_list = []
point_r = 3 # radius of points
point_min = 10 # threshold of points - If there are more than point_min points in person, we define he/she is REAL PERSON
part_min = 3 # threshold of parts - If there are more than part_min parts in person, we define he/she is REAL PERSON / part means head, arm and leg
point_num = 17 # There are 17 points in 1 person
def ellipse_set(person_conf_multi, people_i, point_i):
return (person_conf_multi[people_i][point_i][0] - point_r, person_conf_multi[people_i][point_i][1] - point_r, person_conf_multi[people_i][point_i][0] + point_r, person_conf_multi[people_i][point_i][1] + point_r)
def line_set(person_conf_multi, people_i, point_i, point_j):
return (person_conf_multi[people_i][point_i][0], person_conf_multi[people_i][point_i][1], person_conf_multi[people_i][point_j][0], person_conf_multi[people_i][point_j][1])
def draw_ellipse_and_line(draw, person_conf_multi, people_i, a, b, c, point_color):
draw.ellipse(ellipse_set(person_conf_multi, people_i, a), fill=point_color)
draw.ellipse(ellipse_set(person_conf_multi, people_i, b), fill=point_color)
draw.ellipse(ellipse_set(person_conf_multi, people_i, c), fill=point_color)
draw.line(line_set(person_conf_multi, people_i, a, b), fill=point_color, width=5)
draw.line(line_set(person_conf_multi, people_i, b, c), fill=point_color, width=5)
for i in range(0, video_frame_number):
image = video.get_frame(i/video.fps)
######################
image_batch = data_to_input(image)
# Compute prediction with the CNN
outputs_np = sess.run(outputs, feed_dict={inputs: image_batch})
scmap, locref, pairwise_diff = predict.extract_cnn_output(outputs_np, cfg, dataset.pairwise_stats)
detections = extract_detections(cfg, scmap, locref, pairwise_diff)
unLab, pos_array, unary_array, pwidx_array, pw_array = eval_graph(sm, detections)
person_conf_multi = get_person_conf_multicut(sm, unLab, unary_array, pos_array)
# print('person_conf_multi: ')
# print(type(person_conf_multi))
# print(person_conf_multi)
# Add library to save image
image_img = Image.fromarray(image)
# Save image with points of pose
draw = ImageDraw.Draw(image_img)
people_num = 0
people_real_num = 0
people_part_num = 0
people_num = person_conf_multi.size / (point_num * 2)
people_num = int(people_num)
print('people_num: ' + str(people_num))
for people_i in range(0, people_num):
point_color_r = random.randrange(0, 256)
point_color_g = random.randrange(0, 256)
point_color_b = random.randrange(0, 256)
point_color = (point_color_r, point_color_g, point_color_b, 255)
point_list = []
point_count = 0
point_i = 0 # index of points
part_count = 0 # count of parts in THAT person
# To find rectangle which include that people - list of points x, y coordinates
people_x = []
people_y = []
for point_i in range(0, point_num):
if person_conf_multi[people_i][point_i][0] + person_conf_multi[people_i][point_i][1] != 0: # If coordinates of point is (0, 0) == meaningless data
point_count = point_count + 1
point_list.append(point_i)
# Draw each parts
if (5 in point_list) and (7 in point_list) and (9 in point_list): # Draw left arm
draw_ellipse_and_line(draw, person_conf_multi, people_i, 5, 7, 9, point_color)
part_count = part_count + 1
if (6 in point_list) and (8 in point_list) and (10 in point_list): # Draw right arm
draw_ellipse_and_line(draw, person_conf_multi, people_i, 6, 8, 10, point_color)
part_count = part_count + 1
if (11 in point_list) and (13 in point_list) and (15 in point_list): # Draw left leg
draw_ellipse_and_line(draw, person_conf_multi, people_i, 11, 13, 15, point_color)
part_count = part_count + 1
if (12 in point_list) and (14 in point_list) and (16 in point_list): # Draw right leg
draw_ellipse_and_line(draw, person_conf_multi, people_i, 12, 14, 16, point_color)
part_count = part_count + 1
if point_count >= point_min:
people_real_num = people_real_num + 1
for point_i in range(0, point_num):
if person_conf_multi[people_i][point_i][0] + person_conf_multi[people_i][point_i][1] != 0: # If coordinates of point is (0, 0) == meaningless data
draw.ellipse(ellipse_set(person_conf_multi, people_i, point_i), fill=point_color)
people_x.append(person_conf_multi[people_i][point_i][0])
people_y.append(person_conf_multi[people_i][point_i][1])
# Draw rectangle which include that people
draw.rectangle([min(people_x), min(people_y), max(people_x), max(people_y)], fill=point_color, outline=5)
if part_count >= part_min:
people_part_num = people_part_num + 1
draw.text((0, 0), 'People(by point): ' + str(people_real_num) + ' (threshold = ' + str(point_min) + ')', (0,0,0), font=font)
draw.text((0, 32), 'People(by line): ' + str(people_part_num) + ' (threshold = ' + str(part_min) + ')', (0,0,0), font=font)
draw.text((0, 64), 'Frame: ' + str(i) + '/' + str(video_frame_number), (0,0,0), font=font)
draw.text((0, 96), 'Total time required: ' + str(round(time.clock() - time_start, 1)) + 'sec', (0,0,0))
print('people_real_num: ' + str(people_real_num))
print('people_part_num: ' + str(people_part_num))
print('frame: ' + str(i))
image_img_numpy = np.asarray(image_img)
pose_frame_list.append(image_img_numpy)
video_pose = ImageSequenceClip(pose_frame_list, fps=video.fps)
video_pose.write_videofile("testset/" + video_name + "_pose.mp4", fps=video.fps)
print("Time(s): " + str(time.clock() - time_start))
def test_net(visualise, cache_scoremaps, development):
logging.basicConfig(level=logging.INFO)
cfg = load_config()
dataset = create_dataset(cfg)
dataset.set_shuffle(False)
sm = SpatialModel(cfg)
sm.load()
draw_multi = PersonDraw()
from_cache = "cached_scoremaps" in cfg
if not from_cache:
sess, inputs, outputs = setup_pose_prediction(cfg)
if cache_scoremaps:
out_dir = cfg.scoremap_dir
if not os.path.exists(out_dir):
os.makedirs(out_dir)
pairwise_stats = dataset.pairwise_stats
num_images = dataset.num_images if not development else min(10, dataset.num_images)
coco_results = []
for k in range(num_images):
print('processing image {}/{}'.format(k, num_images-1))
batch = dataset.next_batch()
cache_name = "{}.mat".format(batch[Batch.data_item].coco_id)
if not from_cache:
outputs_np = sess.run(outputs, feed_dict={inputs: batch[Batch.inputs]})
scmap, locref, pairwise_diff = extract_cnn_output(outputs_np, cfg, pairwise_stats)
if cache_scoremaps:
if visualise:
img = np.squeeze(batch[Batch.inputs]).astype('uint8')
pose = argmax_pose_predict(scmap, locref, cfg.stride)
arrows = argmax_arrows_predict(scmap, locref, pairwise_diff, cfg.stride)
visualize.show_arrows(cfg, img, pose, arrows)
visualize.waitforbuttonpress()
continue
out_fn = os.path.join(out_dir, cache_name)
dict = {'scoremaps': scmap.astype('float32'),
'locreg_pred': locref.astype('float32'),
'pairwise_diff': pairwise_diff.astype('float32')}
scipy.io.savemat(out_fn, mdict=dict)
continue
else:
#cache_name = '1.mat'
full_fn = os.path.join(cfg.cached_scoremaps, cache_name)
mlab = scipy.io.loadmat(full_fn)
scmap = mlab["scoremaps"]
locref = mlab["locreg_pred"]
pairwise_diff = mlab["pairwise_diff"]
detections = extract_detections(cfg, scmap, locref, pairwise_diff)
unLab, pos_array, unary_array, pwidx_array, pw_array = eval_graph(sm, detections)
person_conf_multi = get_person_conf_multicut(sm, unLab, unary_array, pos_array)
if visualise:
img = np.squeeze(batch[Batch.inputs]).astype('uint8')
#visualize.show_heatmaps(cfg, img, scmap, pose)
"""
# visualize part detections after NMS
visim_dets = visualize_detections(cfg, img, detections)
plt.imshow(visim_dets)
plt.show()
visualize.waitforbuttonpress()
"""
# """
visim_multi = img.copy()
draw_multi.draw(visim_multi, dataset, person_conf_multi)
plt.imshow(visim_multi)
plt.show()
visualize.waitforbuttonpress()
# """
if cfg.use_gt_segm:
coco_img_results = pose_predict_with_gt_segm(scmap, locref, cfg.stride, batch[Batch.data_item].gt_segm,
batch[Batch.data_item].coco_id)
coco_results += coco_img_results
if len(coco_img_results):
dataset.visualize_coco(coco_img_results, batch[Batch.data_item].visibilities)
if cfg.use_gt_segm:
with open('predictions_with_segm.json', 'w') as outfile:
json.dump(coco_results, outfile)
sess.close()