def test_net(visualise, cache_scoremaps):
logging.basicConfig(level=logging.INFO)
cfg = load_config()
dataset = create_dataset(cfg)
dataset.set_shuffle(False)
dataset.set_test_mode(True)
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)
num_images = dataset.num_images
predictions = np.zeros((num_images, ), dtype=np.object)
for k in range(num_images):
print('processing image {}/{}'.format(k, num_images - 1))
batch = dataset.next_batch()
outputs_np = sess.run(outputs, feed_dict={inputs: batch[Batch.inputs]})
scmap, locref, pairwise_diff = extract_cnn_output(outputs_np, cfg)
pose = argmax_pose_predict(scmap, locref, cfg.stride)
pose_refscale = np.copy(pose)
pose_refscale[:, 0:2] /= cfg.global_scale
predictions[k] = pose_refscale
if visualise:
img = np.squeeze(batch[Batch.inputs]).astype('uint8')
visualize.show_heatmaps(cfg, img, scmap, pose)
visualize.waitforbuttonpress()
if cache_scoremaps:
base = os.path.basename(batch[Batch.data_item].im_path)
raw_name = os.path.splitext(base)[0]
out_fn = os.path.join(out_dir, raw_name + '.mat')
scipy.io.savemat(out_fn,
mdict={'scoremaps': scmap.astype('float32')})
out_fn = os.path.join(out_dir, raw_name + '_locreg' + '.mat')
if cfg.location_refinement:
scipy.io.savemat(
out_fn, mdict={'locreg_pred': locref.astype('float32')})
scipy.io.savemat('predictions.mat', mdict={'joints': predictions})
sess.close()
def test_net(visualise, cache_scoremaps):
logging.basicConfig(level=logging.INFO)
cfg = load_config()
dataset = create_dataset(cfg)
dataset.set_shuffle(False)
dataset.set_test_mode(True)
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)
num_images = dataset.num_images
predictions = np.zeros((num_images,), dtype=np.object)
for k in range(num_images):
print('processing image {}/{}'.format(k, num_images-1))
batch = dataset.next_batch()
outputs_np = sess.run(outputs, feed_dict={inputs: batch[Batch.inputs]})
scmap, locref, pairwise_diff = extract_cnn_output(outputs_np, cfg)
pose = argmax_pose_predict(scmap, locref, cfg.stride)
pose_refscale = np.copy(pose)
pose_refscale[:, 0:2] /= cfg.global_scale
predictions[k] = pose_refscale
if visualise:
img = np.squeeze(batch[Batch.inputs]).astype('uint8')
visualize.show_heatmaps(cfg, img, scmap, pose)
visualize.waitforbuttonpress()
if cache_scoremaps:
base = os.path.basename(batch[Batch.data_item].im_path)
raw_name = os.path.splitext(base)[0]
out_fn = os.path.join(out_dir, raw_name + '.mat')
scipy.io.savemat(out_fn, mdict={'scoremaps': scmap.astype('float32')})
out_fn = os.path.join(out_dir, raw_name + '_locreg' + '.mat')
if cfg.location_refinement:
scipy.io.savemat(out_fn, mdict={'locreg_pred': locref.astype('float32')})
scipy.io.savemat('predictions.mat', mdict={'joints': predictions})
sess.close()
def disp_pic(new=False, where="your_file4"):
'''
Displays image
'''
if (new):
new_pic("QWERTY12345")
where = "QWERTY12345"
image = imread(where + ".PNG", mode='RGB')
image_batch = data_to_input(image)
# Compute prediction with the CNN
outputs_np = sess.run(outputs, feed_dict={inputs: image_batch})
scmap, locref, _ = predict.extract_cnn_output(outputs_np, cfg)
# Extract maximum scoring location from the heatmap, assume 1 person
pose = predict.argmax_pose_predict(scmap, locref, cfg.stride)
visualize.show_heatmaps(cfg, image, scmap, pose)
visualize.waitforbuttonpress()
from dataset.pose_dataset import data_to_input
cfg = load_config("./demo/pose_cfg.yaml")
# Load and setup CNN part detector
sess, inputs, outputs = predict.setup_pose_prediction(cfg)
# Read image from file
#file_name ="demo/ttt.jpg"
file_name = "./demo/girl_back.jpg"
# sex=1 for male, sex=0 for female
sex = 1
# if image is front image, bf_flag=0, else 1
bf_flag = 1
image = imread(file_name, mode='RGB')
image_batch = data_to_input(image)
# Compute prediction with the CNN
outputs_np = sess.run(outputs, feed_dict={inputs: image_batch})
scmap, locref, _ = predict.extract_cnn_output(outputs_np, cfg)
# Extract maximum scoring location from the heatmap, assume 1 person
pose = predict.argmax_pose_predict(scmap, locref, cfg.stride)
# Visualise
visualize.show_heatmaps(image, pose, sex, bf_flag)
#visualize.waitforbuttonpress()
sys.path.append(os.path.dirname(__file__) + "/../")
from scipy.misc import imread
from config import load_config
from nnet import predict
from util import visualize
from dataset.pose_dataset import data_to_input
cfg = load_config("demo/pose_cfg.yaml")
# Load and setup CNN part detector
sess, inputs, outputs = predict.setup_pose_prediction(cfg)
# Read image from file
file_name = "demo/0018R.jpg"
image = imread(file_name, mode='RGB')
image_batch = data_to_input(image)
# Compute prediction with the CNN
outputs_np = sess.run(outputs, feed_dict={inputs: image_batch})
scmap, locref, _ = predict.extract_cnn_output(outputs_np, cfg)
# Extract maximum scoring location from the heatmap, assume 1 person
pose = predict.argmax_pose_predict(scmap, locref, cfg.stride)
# Visualise
visualize.show_heatmaps(cfg, image, scmap, pose)
visualize.waitforbuttonpress()
def test_net(visualise, cache_scoremaps):
# 打开python的日志功能
logging.basicConfig(level=logging.INFO)
# 加载配置文件
cfg = load_config()
# 根据配置文件中的信息产生数据读取类的实例
dataset = create_dataset(cfg)
# 不用对数据进行洗牌
dataset.set_shuffle(False)
# 告诉数据读取类没有类标，即处于测试模式
dataset.set_test_mode(True)
# 该函数返回session，输入算子，输出算子
sess, inputs, outputs = setup_pose_prediction(cfg)
# 是否需要保存测试过程中的heatmap
if cache_scoremaps:
# 保存heatmap的目录
out_dir = cfg.scoremap_dir
# 目录不存在则创建
if not os.path.exists(out_dir):
os.makedirs(out_dir)
# 图片个数
num_images = dataset.num_images
# 预测的关节坐标都保存在这里
predictions = np.zeros((num_images, ), dtype=np.object)
for k in range(num_images):
print('processing image {}/{}'.format(k, num_images - 1))
# 获得一批数据
batch = dataset.next_batch()
# 进行预测
outputs_np = sess.run(outputs, feed_dict={inputs: batch[Batch.inputs]})
# 得到heatmap和精细化的heatmap
scmap, locref = extract_cnn_output(outputs_np, cfg)
# 获得最终的关节坐标
'''
pose = [ [ pos_f8[::-1], [scmap[maxloc][joint_idx]] ] .... ..... .... ]
用我的话说就是下面的结构
pose = [ [关节的坐标, 关节坐标的置信度] .... ..... .... ]
'''
pose = argmax_pose_predict(scmap, locref, cfg.stride)
pose_refscale = np.copy(pose)
# 除以尺度，就能恢复到未经过缩放的图像的坐标系上去
# 注意0:2是左开右闭的区间只取到了0和1
pose_refscale[:, 0:2] /= cfg.global_scale
predictions[k] = pose_refscale
if visualise:
# 获取图片
img = np.squeeze(batch[Batch.inputs]).astype('uint8')
# 显示heatmap
visualize.show_heatmaps(cfg, img, scmap, pose)
# 等待按键按下
visualize.waitforbuttonpress()
if cache_scoremaps:
# 保存heatmap
base = os.path.basename(batch[Batch.data_item].im_path)
raw_name = os.path.splitext(base)[0]
out_fn = os.path.join(out_dir, raw_name + '.mat')
scipy.io.savemat(out_fn,
mdict={'scoremaps': scmap.astype('float32')})
# 保存精细化关节定位的heatmap
out_fn = os.path.join(out_dir, raw_name + '_locreg' + '.mat')
if cfg.location_refinement:
scipy.io.savemat(
out_fn, mdict={'locreg_pred': locref.astype('float32')})
# 将最终预测的关节坐标保存起来
scipy.io.savemat('predictions.mat', mdict={'joints': predictions})
sess.close()
from scipy.misc import imread
from config import load_config
from nnet import predict
from util import visualize
from dataset.pose_dataset import data_to_input
cfg = load_config("demo/pose_cfg.yaml")
# Load and setup CNN part detector
sess, inputs, outputs = predict.setup_pose_prediction(cfg)
# Read image from file
file_name = "demo/image.png"
image = imread(file_name, mode='RGB')
image_batch = data_to_input(image)
# Compute prediction with the CNN
outputs_np = sess.run(outputs, feed_dict={inputs: image_batch})
scmap, locref, _ = predict.extract_cnn_output(outputs_np, cfg)
# Extract maximum scoring location from the heatmap, assume 1 person
pose = predict.argmax_pose_predict(scmap, locref, cfg.stride)
# Visualise
visualize.show_heatmaps(cfg, image, scmap, pose)
visualize.waitforbuttonpress()
