def handle_one(oriImg):
# for visualize
canvas = np.copy(oriImg)
imageToTest = Variable(T.transpose(
T.transpose(T.unsqueeze(torch.from_numpy(oriImg).float(), 0), 2, 3), 1,
2),
volatile=True).cuda()
print oriImg.shape
scale = model_['boxsize'] / float(oriImg.shape[0])
print scale
h = int(oriImg.shape[0] * scale)
w = int(oriImg.shape[1] * scale)
pad_h = 0 if (h % model_['stride']
== 0) else model_['stride'] - (h % model_['stride'])
pad_w = 0 if (w % model_['stride']
== 0) else model_['stride'] - (w % model_['stride'])
new_h = h + pad_h
new_w = w + pad_w
imageToTest = cv2.resize(oriImg, (0, 0),
fx=scale,
fy=scale,
interpolation=cv2.INTER_CUBIC)
imageToTest_padded, pad = util.padRightDownCorner(imageToTest,
model_['stride'],
model_['padValue'])
imageToTest_padded = np.transpose(
np.float32(imageToTest_padded[:, :, :, np.newaxis]),
(3, 2, 0, 1)) / 256 - 0.5
feed = Variable(T.from_numpy(imageToTest_padded)).cuda()
output1, output2 = model(feed)
heatmap = nn.UpsamplingBilinear2d(
(oriImg.shape[0], oriImg.shape[1])).cuda()(output2)
paf = nn.UpsamplingBilinear2d(
(oriImg.shape[0], oriImg.shape[1])).cuda()(output1)
print heatmap.size()
print paf.size()
print type(heatmap)
heatmap_avg = T.transpose(T.transpose(heatmap[0], 0, 1), 1,
2).data.cpu().numpy()
paf_avg = T.transpose(T.transpose(paf[0], 0, 1), 1, 2).data.cpu().numpy()
all_peaks = []
peak_counter = 0
#maps =
for part in range(18):
map_ori = heatmap_avg[:, :, part]
map = gaussian_filter(map_ori, sigma=3)
map_left = np.zeros(map.shape)
map_left[1:, :] = map[:-1, :]
map_right = np.zeros(map.shape)
map_right[:-1, :] = map[1:, :]
map_up = np.zeros(map.shape)
map_up[:, 1:] = map[:, :-1]
map_down = np.zeros(map.shape)
map_down[:, :-1] = map[:, 1:]
peaks_binary = np.logical_and.reduce(
(map >= map_left, map >= map_right, map >= map_up, map >= map_down,
map > param_['thre1']))
# peaks_binary = T.eq(
# peaks = zip(T.nonzero(peaks_binary)[0],T.nonzero(peaks_binary)[0])
peaks = zip(np.nonzero(peaks_binary)[1],
np.nonzero(peaks_binary)[0]) # note reverse
peaks_with_score = [x + (map_ori[x[1], x[0]], ) for x in peaks]
id = range(peak_counter, peak_counter + len(peaks))
peaks_with_score_and_id = [
peaks_with_score[i] + (id[i], ) for i in range(len(id))
]
all_peaks.append(peaks_with_score_and_id)
peak_counter += len(peaks)
connection_all = []
special_k = []
mid_num = 10
for k in range(len(mapIdx)):
score_mid = paf_avg[:, :, [x - 19 for x in mapIdx[k]]]
candA = all_peaks[limbSeq[k][0] - 1]
candB = all_peaks[limbSeq[k][1] - 1]
nA = len(candA)
nB = len(candB)
indexA, indexB = limbSeq[k]
if (nA != 0 and nB != 0):
connection_candidate = []
for i in range(nA):
for j in range(nB):
vec = np.subtract(candB[j][:2], candA[i][:2])
norm = math.sqrt(vec[0] * vec[0] + vec[1] * vec[1])
vec = np.divide(vec, norm)
startend = zip(np.linspace(candA[i][0], candB[j][0], num=mid_num), \
np.linspace(candA[i][1], candB[j][1], num=mid_num))
vec_x = np.array([score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 0] \
for I in range(len(startend))])
vec_y = np.array([score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 1] \
for I in range(len(startend))])
score_midpts = np.multiply(vec_x, vec[0]) + np.multiply(
vec_y, vec[1])
score_with_dist_prior = sum(
score_midpts) / len(score_midpts) + min(
0.5 * oriImg.shape[0] / norm - 1, 0)
criterion1 = len(
np.nonzero(score_midpts > param_['thre2'])
[0]) > 0.8 * len(score_midpts)
criterion2 = score_with_dist_prior > 0
if criterion1 and criterion2:
connection_candidate.append([
i, j, score_with_dist_prior,
score_with_dist_prior + candA[i][2] + candB[j][2]
])
connection_candidate = sorted(connection_candidate,
key=lambda x: x[2],
reverse=True)
connection = np.zeros((0, 5))
for c in range(len(connection_candidate)):
i, j, s = connection_candidate[c][0:3]
if (i not in connection[:, 3] and j not in connection[:, 4]):
connection = np.vstack(
[connection, [candA[i][3], candB[j][3], s, i, j]])
if (len(connection) >= min(nA, nB)):
break
connection_all.append(connection)
else:
special_k.append(k)
connection_all.append([])
# last number in each row is the total parts number of that person
# the second last number in each row is the score of the overall configuration
subset = -1 * np.ones((0, 20))
candidate = np.array([item for sublist in all_peaks for item in sublist])
for k in range(len(mapIdx)):
if k not in special_k:
partAs = connection_all[k][:, 0]
partBs = connection_all[k][:, 1]
indexA, indexB = np.array(limbSeq[k]) - 1
for i in range(len(connection_all[k])): #= 1:size(temp,1)
found = 0
subset_idx = [-1, -1]
for j in range(len(subset)): #1:size(subset,1):
if subset[j][indexA] == partAs[i] or subset[j][
indexB] == partBs[i]:
subset_idx[found] = j
found += 1
if found == 1:
j = subset_idx[0]
if (subset[j][indexB] != partBs[i]):
subset[j][indexB] = partBs[i]
subset[j][-1] += 1
subset[j][-2] += candidate[partBs[i].astype(int),
2] + connection_all[k][i][2]
elif found == 2: # if found 2 and disjoint, merge them
j1, j2 = subset_idx
print "found = 2"
membership = ((subset[j1] >= 0).astype(int) +
(subset[j2] >= 0).astype(int))[:-2]
if len(np.nonzero(membership == 2)[0]) == 0: #merge
subset[j1][:-2] += (subset[j2][:-2] + 1)
subset[j1][-2:] += subset[j2][-2:]
subset[j1][-2] += connection_all[k][i][2]
subset = np.delete(subset, j2, 0)
else: # as like found == 1
subset[j1][indexB] = partBs[i]
subset[j1][-1] += 1
subset[j1][-2] += candidate[
partBs[i].astype(int), 2] + connection_all[k][i][2]
# if find no partA in the subset, create a new subset
elif not found and k < 17:
row = -1 * np.ones(20)
row[indexA] = partAs[i]
row[indexB] = partBs[i]
row[-1] = 2
row[-2] = sum(
candidate[connection_all[k][i, :2].astype(int),
2]) + connection_all[k][i][2]
subset = np.vstack([subset, row])
# delete some rows of subset which has few parts occur
deleteIdx = []
for i in range(len(subset)):
if subset[i][-1] < 4 or subset[i][-2] / subset[i][-1] < 0.4:
deleteIdx.append(i)
subset = np.delete(subset, deleteIdx, axis=0)
# canvas = cv2.imread(test_image) # B,G,R order
for i in range(18):
for j in range(len(all_peaks[i])):
cv2.circle(canvas,
all_peaks[i][j][0:2],
4,
colors[i],
thickness=-1)
stickwidth = 4
for i in range(17):
for n in range(len(subset)):
index = subset[n][np.array(limbSeq[i]) - 1]
if -1 in index:
continue
cur_canvas = canvas.copy()
Y = candidate[index.astype(int), 0]
X = candidate[index.astype(int), 1]
mX = np.mean(X)
mY = np.mean(Y)
length = ((X[0] - X[1])**2 + (Y[0] - Y[1])**2)**0.5
angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1]))
polygon = cv2.ellipse2Poly(
(int(mY), int(mX)), (int(length / 2), stickwidth), int(angle),
0, 360, 1)
cv2.fillConvexPoly(cur_canvas, polygon, colors[i])
canvas = cv2.addWeighted(canvas, 0.4, cur_canvas, 0.6, 0)
return canvas
def handle_one(oriImg):
# for visualize
canvas = np.copy(oriImg)
imageToTest = Variable(T.transpose(T.transpose(T.unsqueeze(torch.from_numpy(oriImg).float(),0),2,3),1,2),volatile=True).cuda()
print oriImg.shape
scale = model_['boxsize'] / float(oriImg.shape[0])
print scale
h = int(oriImg.shape[0]*scale)
w = int(oriImg.shape[1]*scale)
pad_h = 0 if (h%model_['stride']==0) else model_['stride'] - (h % model_['stride'])
pad_w = 0 if (w%model_['stride']==0) else model_['stride'] - (w % model_['stride'])
new_h = h+pad_h
new_w = w+pad_w
imageToTest = cv2.resize(oriImg, (0,0), fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC)
imageToTest_padded, pad = util.padRightDownCorner(imageToTest, model_['stride'], model_['padValue'])
imageToTest_padded = np.transpose(np.float32(imageToTest_padded[:,:,:,np.newaxis]), (3,2,0,1))/256 - 0.5
feed = Variable(T.from_numpy(imageToTest_padded)).cuda()
output1,output2 = model(feed)
heatmap = nn.UpsamplingBilinear2d((oriImg.shape[0], oriImg.shape[1])).cuda()(output2)
paf = nn.UpsamplingBilinear2d((oriImg.shape[0], oriImg.shape[1])).cuda()(output1)
print heatmap.size()
print paf.size()
print type(heatmap)
heatmap_avg = T.transpose(T.transpose(heatmap[0],0,1),1,2).data.cpu().numpy()
paf_avg = T.transpose(T.transpose(paf[0],0,1),1,2).data.cpu().numpy()
all_peaks = []
peak_counter = 0
#maps =
for part in range(18):
map_ori = heatmap_avg[:,:,part]
map = gaussian_filter(map_ori, sigma=3)
map_left = np.zeros(map.shape)
map_left[1:,:] = map[:-1,:]
map_right = np.zeros(map.shape)
map_right[:-1,:] = map[1:,:]
map_up = np.zeros(map.shape)
map_up[:,1:] = map[:,:-1]
map_down = np.zeros(map.shape)
map_down[:,:-1] = map[:,1:]
peaks_binary = np.logical_and.reduce((map>=map_left, map>=map_right, map>=map_up, map>=map_down, map > param_['thre1']))
# peaks_binary = T.eq(
# peaks = zip(T.nonzero(peaks_binary)[0],T.nonzero(peaks_binary)[0])
peaks = zip(np.nonzero(peaks_binary)[1], np.nonzero(peaks_binary)[0]) # note reverse
peaks_with_score = [x + (map_ori[x[1],x[0]],) for x in peaks]
id = range(peak_counter, peak_counter + len(peaks))
peaks_with_score_and_id = [peaks_with_score[i] + (id[i],) for i in range(len(id))]
all_peaks.append(peaks_with_score_and_id)
peak_counter += len(peaks)
connection_all = []
special_k = []
mid_num = 10
for k in range(len(mapIdx)):
score_mid = paf_avg[:,:,[x-19 for x in mapIdx[k]]]
candA = all_peaks[limbSeq[k][0]-1]
candB = all_peaks[limbSeq[k][1]-1]
nA = len(candA)
nB = len(candB)
indexA, indexB = limbSeq[k]
if(nA != 0 and nB != 0):
connection_candidate = []
for i in range(nA):
for j in range(nB):
vec = np.subtract(candB[j][:2], candA[i][:2])
norm = math.sqrt(vec[0]*vec[0] + vec[1]*vec[1])
vec = np.divide(vec, norm)
startend = zip(np.linspace(candA[i][0], candB[j][0], num=mid_num), \
np.linspace(candA[i][1], candB[j][1], num=mid_num))
vec_x = np.array([score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 0] \
for I in range(len(startend))])
vec_y = np.array([score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 1] \
for I in range(len(startend))])
score_midpts = np.multiply(vec_x, vec[0]) + np.multiply(vec_y, vec[1])
score_with_dist_prior = sum(score_midpts)/len(score_midpts) + min(0.5*oriImg.shape[0]/norm-1, 0)
criterion1 = len(np.nonzero(score_midpts > param_['thre2'])[0]) > 0.8 * len(score_midpts)
criterion2 = score_with_dist_prior > 0
if criterion1 and criterion2:
connection_candidate.append([i, j, score_with_dist_prior, score_with_dist_prior+candA[i][2]+candB[j][2]])
connection_candidate = sorted(connection_candidate, key=lambda x: x[2], reverse=True)
connection = np.zeros((0,5))
for c in range(len(connection_candidate)):
i,j,s = connection_candidate[c][0:3]
if(i not in connection[:,3] and j not in connection[:,4]):
connection = np.vstack([connection, [candA[i][3], candB[j][3], s, i, j]])
if(len(connection) >= min(nA, nB)):
break
connection_all.append(connection)
else:
special_k.append(k)
connection_all.append([])
# last number in each row is the total parts number of that person
# the second last number in each row is the score of the overall configuration
subset = -1 * np.ones((0, 20))
candidate = np.array([item for sublist in all_peaks for item in sublist])
for k in range(len(mapIdx)):
if k not in special_k:
partAs = connection_all[k][:,0]
partBs = connection_all[k][:,1]
indexA, indexB = np.array(limbSeq[k]) - 1
for i in range(len(connection_all[k])): #= 1:size(temp,1)
found = 0
subset_idx = [-1, -1]
for j in range(len(subset)): #1:size(subset,1):
if subset[j][indexA] == partAs[i] or subset[j][indexB] == partBs[i]:
subset_idx[found] = j
found += 1
if found == 1:
j = subset_idx[0]
if(subset[j][indexB] != partBs[i]):
subset[j][indexB] = partBs[i]
subset[j][-1] += 1
subset[j][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2]
elif found == 2: # if found 2 and disjoint, merge them
j1, j2 = subset_idx
print "found = 2"
membership = ((subset[j1]>=0).astype(int) + (subset[j2]>=0).astype(int))[:-2]
if len(np.nonzero(membership == 2)[0]) == 0: #merge
subset[j1][:-2] += (subset[j2][:-2] + 1)
subset[j1][-2:] += subset[j2][-2:]
subset[j1][-2] += connection_all[k][i][2]
subset = np.delete(subset, j2, 0)
else: # as like found == 1
subset[j1][indexB] = partBs[i]
subset[j1][-1] += 1
subset[j1][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2]
# if find no partA in the subset, create a new subset
elif not found and k < 17:
row = -1 * np.ones(20)
row[indexA] = partAs[i]
row[indexB] = partBs[i]
row[-1] = 2
row[-2] = sum(candidate[connection_all[k][i,:2].astype(int), 2]) + connection_all[k][i][2]
subset = np.vstack([subset, row])
# delete some rows of subset which has few parts occur
deleteIdx = [];
for i in range(len(subset)):
if subset[i][-1] < 4 or subset[i][-2]/subset[i][-1] < 0.4:
deleteIdx.append(i)
subset = np.delete(subset, deleteIdx, axis=0)
# canvas = cv2.imread(test_image) # B,G,R order
for i in range(18):
for j in range(len(all_peaks[i])):
cv2.circle(canvas, all_peaks[i][j][0:2], 4, colors[i], thickness=-1)
stickwidth = 4
for i in range(17):
for n in range(len(subset)):
index = subset[n][np.array(limbSeq[i])-1]
if -1 in index:
continue
cur_canvas = canvas.copy()
Y = candidate[index.astype(int), 0]
X = candidate[index.astype(int), 1]
mX = np.mean(X)
mY = np.mean(Y)
length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5
angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1]))
polygon = cv2.ellipse2Poly((int(mY),int(mX)), (int(length/2), stickwidth), int(angle), 0, 360, 1)
cv2.fillConvexPoly(cur_canvas, polygon, colors[i])
canvas = cv2.addWeighted(canvas, 0.4, cur_canvas, 0.6, 0)
return canvas
def handle_one(oriImg, dont_draw=False, dont_resize=False):
oriImg = np.squeeze(oriImg)
oriImg = np.copy(oriImg)
canvas = np.copy(oriImg)
scale = model_['boxsize'] / float(oriImg.shape[0])
h = int(oriImg.shape[0] * scale)
w = int(oriImg.shape[1] * scale)
pad_h = 0 if (h % model_['stride']
== 0) else model_['stride'] - (h % model_['stride'])
pad_w = 0 if (w % model_['stride']
== 0) else model_['stride'] - (w % model_['stride'])
new_h = h + pad_h
new_w = w + pad_w
if dont_resize:
imageToTest = oriImg
else:
imageToTest = cv2.resize(
oriImg,
(int(scale * oriImg.shape[0]), int(scale * oriImg.shape[1])),
interpolation=cv2.INTER_CUBIC)
imageToTest_padded, pad = util.padRightDownCorner(imageToTest,
model_['stride'],
model_['padValue'])
imageToTest_padded = np.transpose(
np.float32(imageToTest_padded[:, :, :, np.newaxis]),
(3, 2, 0, 1)) / 255.0 - 0.5
feed = Variable(T.from_numpy(imageToTest_padded), volatile=True).cuda()
p = time.time()
output1, output2 = model(feed)
heatmap = nn.UpsamplingBilinear2d(
(oriImg.shape[0], oriImg.shape[1])).cuda()(output2)
paf = nn.UpsamplingBilinear2d(
(oriImg.shape[0], oriImg.shape[1])).cuda()(output1)
pool_t = time.time()
map_ori = heatmap[0][:N_JOINTS]
map_max = max_erode(
F.avg_pool2d(map_ori, 3, stride=1, padding=1)
) # torch.eq(blurred_avg_tensor, max_tensor).float() * blurred_avg_tensor
peaks_binary = map_max > param_['thre1']
all_peak_idxs = torch.nonzero(peaks_binary.data)
if all_peak_idxs.size() == ():
return None
all_peak_idxs = all_peak_idxs.cpu().numpy()
nonzero_vals = map_ori[peaks_binary].data.cpu().numpy()
paf_avg = paf[0].data.cpu().numpy()
return post_process(oriImg, canvas, paf_avg, all_peak_idxs, nonzero_vals,
dont_draw)
def process_image(oriImg, model, model_params, jjac_info):
# for visualize
t0 = time.time()
canvas = np.copy(oriImg)
#imageToTest = Variable(T.transpose(T.transpose(T.unsqueeze(torch.from_numpy(oriImg).float(), 0), 2, 3), 1, 2),
# volatile=True).cuda()
#print oriImg.shape
tic = time.time()
scale = model_params['model_']['boxsize'] / float(oriImg.shape[0])
#print scale
h = int(oriImg.shape[0] * scale)
w = int(oriImg.shape[1] * scale)
pad_h = 0 if (h % model_params['model_']['stride']
== 0) else model_params['model_']['stride'] - (
h % model_params['model_']['stride'])
pad_w = 0 if (w % model_params['model_']['stride']
== 0) else model_params['model_']['stride'] - (
w % model_params['model_']['stride'])
new_h = h + pad_h
new_w = w + pad_w
#print 'scaled width and height ({}, {})'.format(h, w)
imageToTest = cv2.resize(oriImg, (0, 0),
fx=scale,
fy=scale,
interpolation=cv2.INTER_CUBIC)
imageToTest_padded, pad = util.padRightDownCorner(
imageToTest, model_params['model_']['stride'],
model_params['model_']['padValue'])
imageToTest_padded = np.transpose(
np.float32(imageToTest_padded[:, :, :, np.newaxis]),
(3, 2, 0, 1)) / 256 - 0.5
feed = Variable(T.from_numpy(imageToTest_padded)).cuda()
output1, output2 = model(feed)
heatmap = nn.UpsamplingBilinear2d(
(oriImg.shape[0], oriImg.shape[1])).cuda()(output2)
paf = nn.UpsamplingBilinear2d(
(oriImg.shape[0], oriImg.shape[1])).cuda()(output1)
toc = time.time()
model_running_time = toc - tic
#print heatmap.size() # (360, 640, 3)
#print paf.size()
#print type(heatmap)
tic = time.time()
heatmap_avg = T.transpose(T.transpose(heatmap[0], 0, 1), 1,
2).data.cpu().numpy()
#paf_avg = T.transpose(T.transpose(paf[0], 0, 1), 1, 2).data.cpu().numpy()
all_peaks = []
peak_counter = 0
# 13-17 are head
# 10, 11, 12, 13 are legs
# hand_parts = [5, 6, 7, 8, 15, 16]
# 4 is right hand
# todo is it actually left elbow below because inverse? confirm
# 5 was left elbow
# 6 was left shoulder
# 7 is left hand. Right hand only?
# 15 is head. right part of head
body_part_index_to_name = {
'4': 'Right Hand',
'5': 'Left Elbow',
'6': 'Left Shoulder',
'7': 'Left Hand',
'15': 'Left Eye',
'14': '1',
'16': '2',
'17': '3'
}
hand_parts = [4, 7, 15, 16, 17]
# hand_parts = list(range(18))
# for part in range(18):
for part in hand_parts:
map_ori = heatmap_avg[:, :, part]
map = gaussian_filter(map_ori, sigma=3)
map_left = np.zeros(map.shape)
map_left[1:, :] = map[:-1, :]
map_right = np.zeros(map.shape)
map_right[:-1, :] = map[1:, :]
map_up = np.zeros(map.shape)
map_up[:, 1:] = map[:, :-1]
map_down = np.zeros(map.shape)
map_down[:, :-1] = map[:, 1:]
peaks_binary = np.logical_and.reduce(
(map >= map_left, map >= map_right, map >= map_up, map >= map_down,
map > model_params['param_']['thre1']))
# peaks_binary = T.eq(
# peaks = zip(T.nonzero(peaks_binary)[0],T.nonzero(peaks_binary)[0])
peaks = zip(np.nonzero(peaks_binary)[1],
np.nonzero(peaks_binary)[0]) # note reverse
peaks_with_score = [x + (map_ori[x[1], x[0]], ) for x in peaks]
id = range(peak_counter, peak_counter + len(peaks))
peaks_with_score_and_id = [
peaks_with_score[i] + (id[i], ) for i in range(len(id))
]
all_peaks.append(peaks_with_score_and_id)
# print peaks_with_score_and_id
peak_counter += len(peaks)
#print 'heat map peak stuff time is %.5f' % (time.time() - tic)
# connection_all = []
# special_k = []
# mid_num = 10
# for k in range(len(mapIdx)):
# score_mid = paf_avg[:, :, [x - 19 for x in mapIdx[k]]]
# candA = all_peaks[limbSeq[k][0] - 1]
# candB = all_peaks[limbSeq[k][1] - 1]
# nA = len(candA)
# nB = len(candB)
# indexA, indexB = limbSeq[k]
# if (nA != 0 and nB != 0):
# connection_candidate = []
# for i in range(nA):
# for j in range(nB):
# vec = np.subtract(candB[j][:2], candA[i][:2])
# norm = math.sqrt(vec[0] * vec[0] + vec[1] * vec[1])
# vec = np.divide(vec, norm)
#
# startend = zip(np.linspace(candA[i][0], candB[j][0], num=mid_num), \
# np.linspace(candA[i][1], candB[j][1], num=mid_num))
#
# vec_x = np.array([score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 0] \
# for I in range(len(startend))])
# vec_y = np.array([score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 1] \
# for I in range(len(startend))])
#
# score_midpts = np.multiply(vec_x, vec[0]) + np.multiply(vec_y, vec[1])
# score_with_dist_prior = sum(score_midpts) / len(score_midpts) + min(
# 0.5 * oriImg.shape[0] / norm - 1, 0)
# criterion1 = len(np.nonzero(score_midpts > param_['thre2'])[0]) > 0.8 * len(score_midpts)
# criterion2 = score_with_dist_prior > 0
# if criterion1 and criterion2:
# connection_candidate.append(
# [i, j, score_with_dist_prior, score_with_dist_prior + candA[i][2] + candB[j][2]])
#
# connection_candidate = sorted(connection_candidate, key=lambda x: x[2], reverse=True)
# connection = np.zeros((0, 5))
# for c in range(len(connection_candidate)):
# i, j, s = connection_candidate[c][0:3]
# if (i not in connection[:, 3] and j not in connection[:, 4]):
# connection = np.vstack([connection, [candA[i][3], candB[j][3], s, i, j]])
# if (len(connection) >= min(nA, nB)):
# break
#
# connection_all.append(connection)
# else:
# special_k.append(k)
# connection_all.append([])
# last number in each row is the total parts number of that person
# the second last number in each row is the score of the overall configuration
# subset = -1 * np.ones((0, 20))
# candidate = np.array([item for sublist in all_peaks for item in sublist])
#
# for k in range(len(mapIdx)):
# if k not in special_k:
# partAs = connection_all[k][:, 0]
# partBs = connection_all[k][:, 1]
# indexA, indexB = np.array(limbSeq[k]) - 1
#
# for i in range(len(connection_all[k])): # = 1:size(temp,1)
# found = 0
# subset_idx = [-1, -1]
# for j in range(len(subset)): # 1:size(subset,1):
# if subset[j][indexA] == partAs[i] or subset[j][indexB] == partBs[i]:
# subset_idx[found] = j
# found += 1
#
# if found == 1:
# j = subset_idx[0]
# if (subset[j][indexB] != partBs[i]):
# subset[j][indexB] = partBs[i]
# subset[j][-1] += 1
# subset[j][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2]
# elif found == 2: # if found 2 and disjoint, merge them
# j1, j2 = subset_idx
# print "found = 2"
# membership = ((subset[j1] >= 0).astype(int) + (subset[j2] >= 0).astype(int))[:-2]
# if len(np.nonzero(membership == 2)[0]) == 0: # merge
# subset[j1][:-2] += (subset[j2][:-2] + 1)
# subset[j1][-2:] += subset[j2][-2:]
# subset[j1][-2] += connection_all[k][i][2]
# subset = np.delete(subset, j2, 0)
# else: # as like found == 1
# subset[j1][indexB] = partBs[i]
# subset[j1][-1] += 1
# subset[j1][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2]
#
# # if find no partA in the subset, create a new subset
# elif not found and k < 17:
# row = -1 * np.ones(20)
# row[indexA] = partAs[i]
# row[indexB] = partBs[i]
# row[-1] = 2
# row[-2] = sum(candidate[connection_all[k][i, :2].astype(int), 2]) + connection_all[k][i][2]
# subset = np.vstack([subset, row])
#
# # delete some rows of subset which has few parts occur
# deleteIdx = [];
# for i in range(len(subset)):
# if subset[i][-1] < 4 or subset[i][-2] / subset[i][-1] < 0.4:
# deleteIdx.append(i)
# subset = np.delete(subset, deleteIdx, axis=0)
# canvas = cv2.imread(test_image) # B,G,R order
found_hand = False
found_head = False
found_2_hands = False
tic = time.time()
# for i in range(18):
for i in range(len(hand_parts)):
for j in range(len(all_peaks[i])):
#cv2.circle(canvas, all_peaks[i][j][0:2], 4, colors[i], thickness=-1)
# Right hand is first in loop. so if it fails we have backup
if body_part_index_to_name[str(hand_parts[i])] == 'Right Hand': #
# BGR order. So blue is below
# cv2.circle(canvas, all_peaks[i][j][0:2], 4, (255, 0, 0), thickness=-1)
#print 'Hand position:', all_peaks[i][j][0:2]
found_hand = True
hand_position = all_peaks[i][j][0:2]
elif body_part_index_to_name[str(hand_parts[i])] == 'Left Hand':
# cv2.circle(canvas, all_peaks[i][j][0:2], 4, (0, 255, 0), thickness=-1)
if not found_hand:
print 'Did not find Right hand but found left hand!'
found_hand = True
hand_position = all_peaks[i][j][0:2]
else:
print 'Found two hands'
found_2_hands = True
second_hand_position = all_peaks[i][j][0:2]
#hand_position = all_peaks[i][j][0:2]
elif body_part_index_to_name[str(hand_parts[i])] == 'Left Eye':
# BGR order. So red is below
# cv2.circle(canvas, all_peaks[i][j][0:2], 4, (0, 0, 255), thickness=-1)
#print 'Head position:', all_peaks[i][j][0:2]
found_head = True
head_position = all_peaks[i][j][0:2]
jjac_info['last_x_head_pos'].append(head_position[1])
if len(jjac_info['last_x_head_pos']) > 10:
jjac_info['last_x_head_pos'].pop()
biggest_diff = max(jjac_info['last_x_head_pos']) - min(
jjac_info['last_x_head_pos'])
if biggest_diff > jjac_info['biggest_diff']:
jjac_info['biggest_diff'] = biggest_diff
# espeak_command = 'Largest y range from last_x_head_pos: {}'.format(biggest_diff)
espeak_command = 'Largest range from last positions: {}'.format(
biggest_diff)
print espeak_command
# os.system(espeak_command)
# jjac_info['head_y_range']
elif body_part_index_to_name[str(hand_parts[i])] == '1':
cv2.circle(canvas,
all_peaks[i][j][0:2],
4, (0, 0, 255),
thickness=-1)
elif body_part_index_to_name[str(hand_parts[i])] == '2':
cv2.circle(canvas,
all_peaks[i][j][0:2],
4, (0, 255, 255),
thickness=-1)
elif body_part_index_to_name[str(hand_parts[i])] == '3':
cv2.circle(canvas,
all_peaks[i][j][0:2],
4, (255, 255, 255),
thickness=-1)
else:
# cv2.circle(canvas, all_peaks[i][j][0:2], 4, colors[i], thickness=-1)
cv2.circle(canvas,
all_peaks[i][j][0:2],
4, (128, 128, 128),
thickness=-1)
#global how_many_times_hands_went_over_head, hands_over_head
if found_hand and found_head:
# both hands over head or 1 hand over head
if (found_2_hands and hand_position[1] < head_position[1]
and second_hand_position[1] < head_position[1]
) or hand_position[1] < head_position[1]:
print 'Hand is higher than head {} < {}'.format(
hand_position[1], head_position[1])
# if first time hand over head, then increase jumping jack count
if not jjac_info['hands_over_head']:
jjac_info['hands_over_head'] = True
jjac_info['num_jumping_jacks'] += 1
print 'Number of jumping jacks:', jjac_info[
'num_jumping_jacks']
# speak speech command example
# espeak '1 Jumping Jack'
espeak_command = "espeak {}".format(
'\'{} Jumping jack{}\''.format(
jjac_info['num_jumping_jacks'],
's' if jjac_info['num_jumping_jacks'] > 1 else ''))
# os.system(espeak_command) # slows it down?
else:
jjac_info['hands_over_head'] = False
print 'hand is lower than head {} > {}'.format(
hand_position[1], head_position[1])
#print 'Drawing circles time is %.5f' % (time.time() - tic)
cv2.putText(
canvas,
"No. Jumping Jacks: {} ".format(jjac_info['num_jumping_jacks']),
(0, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, 255)
cv2.putText(
canvas, "Hands over head: {}".format(
'Yes' if jjac_info['hands_over_head'] else 'No'), (0, 45),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, 255)
# stickwidth = 4
# for i in range(17):
# for n in range(len(subset)):
# index = subset[n][np.array(limbSeq[i]) - 1]
# if -1 in index:
# continue
# cur_canvas = canvas.copy()
# Y = candidate[index.astype(int), 0]
# X = candidate[index.astype(int), 1]
# mX = np.mean(X)
# mY = np.mean(Y)
# length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5
# angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1]))
# polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stickwidth), int(angle), 0, 360, 1)
# cv2.fillConvexPoly(cur_canvas, polygon, colors[i])
# canvas = cv2.addWeighted(canvas, 0.4, cur_canvas, 0.6, 0)
print 'cv and print: %.5f, Model time: %.5f, Full processing time is %.5f.' % (
time.time() - tic, model_running_time, time.time() - t0)
return canvas