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()
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()
# Load and setup CNN part detector
sess, inputs, outputs = predict.setup_pose_prediction(cfg)
# Read image from file
file_name = "demo/image_multi.png"
image = imageio.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, 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)
visim_multi = img.copy()
fig = plt.imshow(visim_multi)
draw_multi.draw(visim_multi, dataset, person_conf_multi)
fig.axes.get_xaxis().set_visible(False)
fig.axes.get_yaxis().set_visible(False)
plt.show()
visualize.waitforbuttonpress()
draw_multi = PersonDraw()
# Load and setup CNN part detector
sess, inputs, outputs = predict.setup_pose_prediction(cfg)
# Read image from file
file_name = "demo/image_multi.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, 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)
visim_multi = img.copy()
fig = plt.imshow(visim_multi)
draw_multi.draw(visim_multi, dataset, person_conf_multi)
fig.axes.get_xaxis().set_visible(False)
fig.axes.get_yaxis().set_visible(False)
plt.show()
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()
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 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()
