def showNearestMHIs(num, hv, MHIs, labels):
# length = len(hv)
# num = 5
test_m = []
test_l = []
train_m = hv
train_l = labels
for i in range(1):
rand = num
test_m.append(hv[rand,:])
test_l.append(labels[rand,0])
train_m = np.delete(train_m,rand, 0)
train_l = np.delete(train_l,rand, 0)
pred_labs = predictAction(test_m[0], train_m, np.ravel(train_l), debug = False, k = 4, return_all = True)
print(pred_labs)
plt.imshow(MHIs[num,:,:])
plt.title("original")
fig = plt.figure()
j = 1
for f in pred_labs:
print('f', f)
im = MHIs[f,:,:]
a = fig.add_subplot(1, 4, j)
imgplot = plt.imshow(im, cmap = cm.Greys_r)
a.set_title(str(j))
j += 1
def predict_Leave1out(num,hv,labels):
test_m = []
test_l = []
train_m = hv
train_l = labels
rand = num
test_m.append(hv[rand,:])
test_l = labels[rand,0]
train_m = np.delete(train_m,rand, 0)
train_l = np.delete(train_l,rand, 0)
pred_lab = predictAction(test_m[0], train_m, np.ravel(train_l), k = 1)
return int(pred_lab), int(test_l)
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] + '/'
subdir = os.listdir(subdirname)
for seqnum in range(len(subdir)):
# testMHI = computeMHI(subdirname + subdir[seqnum])
# testMoments = huMoments(testMHI)
testMoments = allMoments[actionnum*4 + seqnum]
trainMHI = np.delete(allMHIs, (actionnum*4 + seqnum), axis=2)
trainMoments = np.delete(allMoments, (actionnum*4 + seqnum), axis=0)
trainLabels = np.delete(allLabels, (actionnum*4 + seqnum), axis=0)
print actions[actionnum], " and subdir is ", seqnum, " with complete ", subdirname + subdir[seqnum]
# confusionMatrix[actionnum][predictAction(testMoments, trainMoments, trainLabels)] += 1
confusionMatrix[actionnum][predictAction(testMoments, allMoments, allLabels)-1] += 1
print confusionMatrix
for i in range(5):
print "Mean Recognition Rate for "+actions[i] + " class is ", confusionMatrix[i][i] * 25, "%"
#%%
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])
trainMHI_counter = trainMHI_counter + 1
trainMoments = np.empty((trainMHI.shape[2],7), dtype=np.float32)
for i in range(trainMHI.shape[2]):
trainMoments[i,:] = huMoments(trainMHI[:,:,i])
trainLabels = [i for i in xrange(1,6) for j in xrange(4)]
trainLabels = np.array(trainLabels)
print predictAction(testMoments, trainMoments, trainLabels)
if __name__ == '__main__':
with open("huVectors.npy", "rb") as f:
huVectors = pkl.load(f)
num_seq = len(allLabels)
num_actions = 5
confusion_matrix = np.zeros((num_actions, num_actions), dtype='int')
for test_idx in range(num_seq):
mask = np.arange(num_seq) != test_idx
train_indices = np.arange(num_seq)[mask]
test_moments = huVectors[test_idx]
test_label = allLabels[test_idx]
train_moments = huVectors[train_indices]
train_labels = [allLabels[idx] for idx in train_indices]
predicted_label = predictAction(test_moments, train_moments,
train_labels)[0]
confusion_matrix[test_label - 1][predicted_label - 1] += 1
print(confusion_matrix)
orr = 0
for i in range(5):
orr += confusion_matrix[i][i]
orr = orr / len(allLabels)
clr = []
for i in range(5):
clr.append(confusion_matrix[i][i] / 4)
print(orr, clr)
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j,
i,
format(cm[i, j], fmt),
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.tight_layout()
plt.show()
# load data
# each row is a hu
hus = np.load('huVectors.npy')
labels = [1] * 4 + [2] * 4 + [3] * 4 + [4] * 4 + [5] * 4
cm = np.zeros((5, 5))
actions = ['botharms', 'crouch', 'leftarmup', 'punch', 'rightkick']
for i in range(20):
testVec = hus[i]
trainData = np.delete(hus, i, axis=0)
trainLabels = np.delete(labels, i, axis=0)
prediction = predictAction(testVec, trainData, trainLabels)
cm[i / 4, prediction - 1] = cm[i / 4, prediction - 1] + 1
plot_confusion_matrix(cm, actions, normalize=True)
