import os
from predictAction import predictAction
from computeMHI import computeMHI
from huMoments import huMoments
basedir = './'
actions = ['botharms', 'crouch', 'leftarmup', 'punch', 'rightkick']
allMHIs_counter = 0
allMHIs = np.empty((480,640,20), dtype=np.float32)
allMoments = np.empty((20,7), dtype=np.float64)
for actionnum in range(len(actions)):
subdirname = basedir + actions[actionnum] + '/'
subdir = os.listdir(subdirname)
for seqnum in range(len(subdir)):
allMHIs[:,:,allMHIs_counter] = computeMHI(subdirname + subdir[seqnum])
allMoments[allMHIs_counter,:] = huMoments(allMHIs[:,:,allMHIs_counter])
allMHIs_counter += 1
allLabels = [i for i in xrange(1,6) for j in xrange(4)]
allLabels = np.array(allLabels)
#%%
actions = ['botharms', 'crouch', 'leftarmup', 'punch', 'rightkick']
#actions = ['leftarmup']
#trainMHI = np.empty((480,640,20), dtype=np.float32)
#trainMHI_counter = 0
np.save("huVectors", allMoments)
np.save("allMHIs", allMHIs)
allMoments = allMoments/np.linalg.norm(allMoments)
confusionMatrix = np.zeros((5,5))
for actionnum in range(len(actions)):
subdirname = basedir + actions[actionnum] + '/'
allMHIs_counter = allMHIs_counter + 1
allMHIs[:,:,allMHIs_counter] = computeMHI(subdirname + subdir[seqnum])
cv2.imshow(actions[actionnum]+"- %d"% seqnum, allMHIs[:,:,allMHIs_counter])
# cv2.imshow("mask", motion_mask)
if 0xFF & cv2.waitKey(1) == 27:
break
#cv2.destroyAllWindows()
#%%
allHuMoments = np.empty((20,7), dtype=np.float32)
for i in range(allMHIs.shape[2]):
allHuMoments[i,:] = huMoments(allMHIs[:,:,i])
#%%
#testMHI = computeMHI("./leftarmup/leftarm-up-p2-1")
testMHI = computeMHI("./punch/punch-p1-1")
testMoments = huMoments(testMHI)
actions = ['botharms', 'crouch', 'leftarmup', 'punch', 'rightkick']
#actions = ['leftarmup']
trainMHI = np.empty((480,640,20), dtype=np.float32)
trainMHI_counter = 0
for actionnum in range(len(actions)):
subdirname = basedir + actions[actionnum] + '/'
subdir = os.listdir(subdirname)
for seqnum in range(len(subdir)):
# cycle through all sequences for this action category
trainMHI[:,:,trainMHI_counter] = computeMHI(subdirname + subdir[seqnum])
actions = ['botharms', 'crouch', 'leftarmup', 'punch', 'rightkick']
allMHIs = list()
for action in actions:
subdirname = basedir + action + '/'
subdirs = os.listdir(subdirname)
for seq in subdirs:
# cycle through all sequences for this action category
directory = subdirname + seq
mhi = computeMHI(directory)
allMHIs.append(mhi)
allMHIs = np.stack(allMHIs, axis=2)
np.save('allMHIs.npy', allMHIs)
print 'saved MHIs'
allHus = list()
allMHIs = np.load('allMHIs.npy')
_, _, numSeqs = np.shape(allMHIs)
for i in range(numSeqs):
mhi = allMHIs[:, :, i]
huVec = huMoments(mhi)
allHus.append(huVec)
allHus = np.stack(allHus)
np.save('huVectors.npy', allHus)
print 'saved Hus'
if __name__ == "__main__":
actions = ['botharms', 'crouch', 'leftarmup', 'punch', 'rightkick']
trainDirectoryNames = []
trainMoments = np.asarray(np.load('huVectors.npy'))
K = 4
for action in actions:
directory_name = './PS5_Data/' + action + '/'
trainDirectoryNames = trainDirectoryNames + [
directory_name + subdirectory_name
for subdirectory_name in os.listdir(directory_name)
]
trainDirectoryNames = np.reshape(trainDirectoryNames, (-1, 1))
nMHI = showNearestMHIs(huMoments(np.load('botharms-up-p1-1_MHI.npy')),
trainMoments, trainDirectoryNames, K)
plt.title("Chosen MHI - botharms-up-p1-1_MHI")
plt.imshow(nMHI[:, :, 0])
plt.show()
for ith_MHI in range(1, K + 1):
plt.title("Nearest Neighbor " + str(ith_MHI) +
" to botharms-up-p1-1 MHI")
plt.imshow(nMHI[:, :, ith_MHI])
plt.show()
nMHI = showNearestMHIs(huMoments(np.load('crouch-p1-1_MHI.npy')),
trainMoments, trainDirectoryNames, K)
plt.title("Chosen MHI - crouch-p1-1_MHI")
plt.imshow(nMHI[:, :, 0])
plt.show()
return nMHI
if __name__ == "__main__":
actions = ['botharms', 'crouch', 'leftarmup', 'punch', 'rightkick']
trainDirectoryNames = []
trainMoments = np.asarray(np.load('huVectors.npy'))
K = 4
for action in actions:
directory_name = './PS5_Data/' + action + '/'
trainDirectoryNames = trainDirectoryNames + [
directory_name + subdirectory_name
for subdirectory_name in os.listdir(directory_name)
]
trainDirectoryNames = np.reshape(trainDirectoryNames, (-1, 1))
nMHI = showNearestMHIs(huMoments(np.load('botharms-up-p1-1_MHI.npy')),
trainMoments, trainDirectoryNames, K)
figure = plot.figure(frameon=False)
axis = figure.add_subplot(1, 1, 1)
axis.imshow(nMHI[:, :, 0])
axis.set_title("Chosen MHI - botharms-up-p1-1_MHI")
figure.add_axes(axis)
plot.show()
for ith_MHI in range(1, K + 1):
figure = plot.figure(ith_MHI)
axis = figure.add_subplot(1, 1, 1)
axis.imshow(nMHI[:, :, ith_MHI])
axis.set_title("Nearest Neighbor " + str(ith_MHI) +
" to botharms-up-p1-1 MHI")
figure.add_axes(axis)
plot.show()
