def extract_heatmap_info(heatmap_avg, param_thre1=0.1):
all_peaks = []
peak_counter = 0
for part in range(18):
map_ori = heatmap_avg[:, :, part]
map_gau = gaussian_filter(map_ori, sigma=3)
map_left = np.zeros(map_gau.shape)
map_left[1:, :] = map_gau[:-1, :]
map_right = np.zeros(map_gau.shape)
map_right[:-1, :] = map_gau[1:, :]
map_up = np.zeros(map_gau.shape)
map_up[:, 1:] = map_gau[:, :-1]
map_down = np.zeros(map_gau.shape)
map_down[:, :-1] = map_gau[:, 1:]
peaks_binary = np.logical_and.reduce(
(map_gau >= map_left, map_gau >= map_right, map_gau >= map_up,
map_gau >= map_down, map_gau > param_thre1))
peaks = zip(np.nonzero(peaks_binary)[1],
np.nonzero(peaks_binary)[0]) # note reverse
peaks = list(peaks)
peaks_with_score = [x + (map_ori[x[1], x[0]], ) for x in peaks]
ids = range(peak_counter, peak_counter + len(peaks))
peaks_with_score_and_id = [
peaks_with_score[i] + (ids[i], ) for i in range(len(ids))
]
all_peaks.append(peaks_with_score_and_id)
peak_counter += len(peaks)
return all_peaks
def extract_heatmap_info(heatmap_avg, param_thre1=0.1):
all_peaks = []
peak_counter = 0
for part in range(18):
map_ori = heatmap_avg[:, :, part]
map_gau = gaussian_filter(map_ori, sigma=3)
map_left = np.zeros(map_gau.shape)
map_left[1:, :] = map_gau[:-1, :]
map_right = np.zeros(map_gau.shape)
map_right[:-1, :] = map_gau[1:, :]
map_up = np.zeros(map_gau.shape)
map_up[:, 1:] = map_gau[:, :-1]
map_down = np.zeros(map_gau.shape)
map_down[:, :-1] = map_gau[:, 1:]
peaks_binary = np.logical_and.reduce(
(map_gau >= map_left, map_gau >= map_right, map_gau >= map_up,
map_gau >= map_down, map_gau > param_thre1))
peaks = zip(np.nonzero(peaks_binary)[1], np.nonzero(peaks_binary)[0]) # note reverse
peaks = list(peaks)
peaks_with_score = [x + (map_ori[x[1], x[0]],) for x in peaks]
ids = range(peak_counter, peak_counter + len(peaks))
peaks_with_score_and_id = [peaks_with_score[i] + (ids[i],) for i in range(len(ids))]
all_peaks.append(peaks_with_score_and_id)
peak_counter += len(peaks)
return all_peaks
def get_subsets(connection_all, special_k, all_peaks):
# 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(map_ids)):
if k not in special_k:
partAs = connection_all[k][:, 0]
partBs = connection_all[k][:, 1]
indexA, indexB = np.array(limb_seq[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])
return subset, candidate
def detect(self, minibatch, humans, scales):
ms = minibatch.shape
tic = time.time()
imageToTest = Variable(T.transpose(
T.transpose((torch.from_numpy(minibatch).float() / 256.0) - 0.5, 2,
3), 1, 2),
volatile=True).cuda()
output1, output2 = self.model(imageToTest)
heatmap_avg = nn.UpsamplingBilinear2d((ms[1], ms[2])).cuda()(output2)
paf_avg = nn.UpsamplingBilinear2d((ms[1], ms[2])).cuda()(output1)
heatmap_avg = T.transpose(heatmap_avg, 1, 2)
heatmap_avg = T.transpose(heatmap_avg, 2, 3)
heatmap_avg = heatmap_avg.cpu().data.numpy()
#print 'heatmap shape : ' , heatmap_avg.shape
paf_avg = paf_avg.cpu().data.numpy()
heatmap_avg = heatmap_avg[:, :, :, :-1]
map = gaussian_filter1d(heatmap_avg, sigma=3, axis=2)
map = gaussian_filter1d(map, sigma=3, axis=1)
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 > self.param_['thre1']))
peaks = np.nonzero(peaks_binary)
for i in range(peaks[0].shape[0]):
human_idx = int(peaks[0][i])
x = int(peaks[1][i] / scales[human_idx])
y = int(peaks[2][i] / scales[human_idx])
#x = peaks[1][i]
#y = peaks[2][i]
joint = peaks[3][i]
humans[human_idx].joints[joint * 2] = x
humans[human_idx].joints[joint * 2 + 1] = y
return humans
def extract_paf_info(img_raw, paf_avg, all_peaks, param_thre2=0.05, param_thre3=0.5):
connection_all = []
special_k = []
mid_num = 10
for k in range(len(map_ids)):
score_mid = paf_avg[:, :, [x - 19 for x in map_ids[k]]]
candA = all_peaks[limb_seq[k][0] - 1]
candB = all_peaks[limb_seq[k][1] - 1]
nA = len(candA)
nB = len(candB)
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))
startend = list(startend)
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)
score_with_dist_prior += min(0.5 * img_raw.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([])
return special_k, connection_all
def get_subsets(connection_all, special_k, all_peaks):
# 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(map_ids)):
if k not in special_k:
partAs = connection_all[k][:, 0]
partBs = connection_all[k][:, 1]
indexA, indexB = np.array(limb_seq[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])
return subset, candidate
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)
raw_key_points = []
for item in all_peaks:
if item != []:
raw_key_points.append(item[0][0:2])
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
def extract_paf_info(img_raw,
paf_avg,
all_peaks,
param_thre2=0.05,
param_thre3=0.5):
connection_all = []
special_k = []
mid_num = 10
for k in range(len(map_ids)):
score_mid = paf_avg[:, :, [x - 19 for x in map_ids[k]]]
candA = all_peaks[limb_seq[k][0] - 1]
candB = all_peaks[limb_seq[k][1] - 1]
nA = len(candA)
nB = len(candB)
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))
startend = list(startend)
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)
score_with_dist_prior += min(
0.5 * img_raw.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([])
return special_k, connection_all
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 post_process(oriImg, canvas, paf_avg, all_peak_idxs, nonzero_vals,
dont_draw):
all_peaks = [[] for x in range(N_JOINTS)]
for i in xrange(all_peak_idxs.shape[0]):
all_peaks[all_peak_idxs[i, 0]].append(
(all_peak_idxs[i, 2], all_peak_idxs[i, 1], nonzero_vals[i], i))
PEAKT = time.time()
connection_all = []
special_k = []
mid_num = 10
KTTT = time.time()
# don't really know how this works
for k in xrange(len(mapIdx)):
idxs = []
for x in mapIdx[k]:
idxs.append(x - 19)
score_mid = paf_avg[idxs, :, :]
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])
if norm == 0:
continue
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.zeros(len(startend))
vec_y = np.zeros(len(startend))
for I in xrange(len(startend)):
vec_x[I] = score_mid[0,
int(round(startend[I][1])),
int(round(startend[I][0]))]
vec_y[I] = score_mid[1,
int(round(startend[I][1])),
int(round(startend[I][0]))]
score_midpts = np.multiply(vec_x, vec[0]) + np.multiply(
vec_y, vec[1])
score_with_dist_prior = np.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=get_third_item,
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))
cand_tmp = []
for sublist in all_peaks:
for item in sublist:
cand_tmp.append(item)
candidate = np.asarray(cand_tmp)
# don't really know how this works either
for k in xrange(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] = np.sum(
candidate[connection_all[k][i, :2].astype(int),
2]) + connection_all[k][i][2]
subset = np.vstack([subset, row])
#print subset.shape
# 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)
N_BONES = N_JOINTS - 1
if dont_draw:
res = {'found_ppl': []}
for n in range(len(subset)):
skel = {}
for i in range(N_BONES):
index = subset[n][np.array(limbSeq[i]) - 1]
if -1 in index:
continue
Y = candidate[index.astype(int), 0]
X = candidate[index.astype(int), 1]
bone_name = bone_map[i]
skel[bone_name] = ((X[0], Y[0]), (X[1], Y[1]))
res['found_ppl'].append(skel)
return res
p = time.time()
canvas = canvas.copy()
for i in range(N_JOINTS):
for j in range(len(all_peaks[i])):
cv2.circle(canvas,
all_peaks[i][j][0:2],
4,
colors[i],
thickness=-1)
stickwidth = 4
N_BONES = N_JOINTS - 1
for i in range(N_BONES):
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])
cv2.putText(canvas, str(i), (int(mY), int(mX)),
cv2.FONT_HERSHEY_PLAIN, 1, (255, 255, 255), 3)
cv2.putText(canvas, str(i), (int(mY), int(mX)),
cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 0), 2)
canvas = cv2.addWeighted(canvas, 0.4, cur_canvas, 0.6, 0)
print('drawing took: ', time.time() - p)
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 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
def get_pose(image, gpu):
T.set_num_threads(T.get_num_threads())
weight_name = './model/pose_model.pth'
(y, x, _) = image.shape
blocks = {}
# find connection in the specified sequence, center 29 is in the position 15
limbSeq = [[2,3], [2,6], [3,4], [4,5], [6,7], [7,8], [2,9], [9,10], \
[10,11], [2,12], [12,13], [13,14], [2,1], [1,15], [15,17], \
[1,16], [16,18], [3,17], [6,18]]
# the middle joints heatmap correpondence
mapIdx = [[31,32], [39,40], [33,34], [35,36], [41,42], [43,44], [19,20], [21,22], \
[23,24], [25,26], [27,28], [29,30], [47,48], [49,50], [53,54], [51,52], \
[55,56], [37,38], [45,46]]
# the joints between corresponding limbs
joints = [
[12, 0],
[12, 1],
[12, 6],
[12, 9],
[12, 13],
[12, 15], #connected to the Nose-Neck
[1, 4],
[4, 5], #connect LShoulder-LElbow-LWrist
[0, 2],
[2, 3], #connect RShoulder-RElbow-RWrist
[6, 7],
[7, 8], #connect RHip-RKnee-RAnkle
[9, 10],
[10, 11], #connect LHip-LKnee-LAnkle
[13, 14], #connect REye-REar
[15, 16] #connect LEye-LEar
]
# visualize
colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0], \
[0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255], \
[170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85]]
block0 = [{
'conv1_1': [3, 64, 3, 1, 1]
}, {
'conv1_2': [64, 64, 3, 1, 1]
}, {
'pool1_stage1': [2, 2, 0]
}, {
'conv2_1': [64, 128, 3, 1, 1]
}, {
'conv2_2': [128, 128, 3, 1, 1]
}, {
'pool2_stage1': [2, 2, 0]
}, {
'conv3_1': [128, 256, 3, 1, 1]
}, {
'conv3_2': [256, 256, 3, 1, 1]
}, {
'conv3_3': [256, 256, 3, 1, 1]
}, {
'conv3_4': [256, 256, 3, 1, 1]
}, {
'pool3_stage1': [2, 2, 0]
}, {
'conv4_1': [256, 512, 3, 1, 1]
}, {
'conv4_2': [512, 512, 3, 1, 1]
}, {
'conv4_3_CPM': [512, 256, 3, 1, 1]
}, {
'conv4_4_CPM': [256, 128, 3, 1, 1]
}]
blocks['block1_1'] = [{
'conv5_1_CPM_L1': [128, 128, 3, 1, 1]
}, {
'conv5_2_CPM_L1': [128, 128, 3, 1, 1]
}, {
'conv5_3_CPM_L1': [128, 128, 3, 1, 1]
}, {
'conv5_4_CPM_L1': [128, 512, 1, 1, 0]
}, {
'conv5_5_CPM_L1': [512, 38, 1, 1, 0]
}]
blocks['block1_2'] = [{
'conv5_1_CPM_L2': [128, 128, 3, 1, 1]
}, {
'conv5_2_CPM_L2': [128, 128, 3, 1, 1]
}, {
'conv5_3_CPM_L2': [128, 128, 3, 1, 1]
}, {
'conv5_4_CPM_L2': [128, 512, 1, 1, 0]
}, {
'conv5_5_CPM_L2': [512, 19, 1, 1, 0]
}]
for i in range(2, 7):
blocks['block%d_1' % i] = [{
'Mconv1_stage%d_L1' % i: [185, 128, 7, 1, 3]
}, {
'Mconv2_stage%d_L1' % i: [128, 128, 7, 1, 3]
}, {
'Mconv3_stage%d_L1' % i: [128, 128, 7, 1, 3]
}, {
'Mconv4_stage%d_L1' % i: [128, 128, 7, 1, 3]
}, {
'Mconv5_stage%d_L1' % i: [128, 128, 7, 1, 3]
}, {
'Mconv6_stage%d_L1' % i: [128, 128, 1, 1, 0]
}, {
'Mconv7_stage%d_L1' % i: [128, 38, 1, 1, 0]
}]
blocks['block%d_2' % i] = [{
'Mconv1_stage%d_L2' % i: [185, 128, 7, 1, 3]
}, {
'Mconv2_stage%d_L2' % i: [128, 128, 7, 1, 3]
}, {
'Mconv3_stage%d_L2' % i: [128, 128, 7, 1, 3]
}, {
'Mconv4_stage%d_L2' % i: [128, 128, 7, 1, 3]
}, {
'Mconv5_stage%d_L2' % i: [128, 128, 7, 1, 3]
}, {
'Mconv6_stage%d_L2' % i: [128, 128, 1, 1, 0]
}, {
'Mconv7_stage%d_L2' % i: [128, 19, 1, 1, 0]
}]
def make_layers(cfg_dict):
layers = []
for i in range(len(cfg_dict) - 1):
one_ = cfg_dict[i]
for k, v in one_.items():
if 'pool' in k:
layers += [
nn.MaxPool2d(kernel_size=v[0],
stride=v[1],
padding=v[2])
]
else:
conv2d = nn.Conv2d(in_channels=v[0],
out_channels=v[1],
kernel_size=v[2],
stride=v[3],
padding=v[4])
layers += [conv2d, nn.ReLU(inplace=True)]
one_ = cfg_dict[-1].keys()
k = list(one_)[0]
v = cfg_dict[-1][k]
conv2d = nn.Conv2d(in_channels=v[0],
out_channels=v[1],
kernel_size=v[2],
stride=v[3],
padding=v[4])
layers += [conv2d]
return nn.Sequential(*layers)
layers = []
for i in range(len(block0)):
one_ = block0[i]
#for k,v in one_.iteritems():
for k, v in one_.items():
if 'pool' in k:
layers += [
nn.MaxPool2d(kernel_size=v[0], stride=v[1], padding=v[2])
]
else:
conv2d = nn.Conv2d(in_channels=v[0],
out_channels=v[1],
kernel_size=v[2],
stride=v[3],
padding=v[4])
layers += [conv2d, nn.ReLU(inplace=True)]
models = {}
models['block0'] = nn.Sequential(*layers)
#for k,v in blocks.iteritems():
for k, v in blocks.items():
models[k] = make_layers(v)
model = pose_model(models)
model.load_state_dict(torch.load(weight_name))
model.cuda(gpu)
model.float()
model.eval()
param_, model_ = config_reader()
#torch.nn.functional.pad(img pad, mode='constant', value=model_['padValue'])
tic = time.time()
oriImg = image
imageToTest = Variable(T.transpose(
T.transpose(T.unsqueeze(torch.from_numpy(oriImg).float(), 0), 2, 3), 1,
2),
volatile=True).cuda(gpu)
multiplier = [
x * model_['boxsize'] / oriImg.shape[0] for x in param_['scale_search']
]
heatmap_avg = torch.zeros(
(len(multiplier), 19, oriImg.shape[0], oriImg.shape[1])).cuda(gpu)
paf_avg = torch.zeros(
(len(multiplier), 38, oriImg.shape[0], oriImg.shape[1])).cuda(gpu)
#print(heatmap_avg.size())
toc = time.time()
#print('time to load model is %.5f'%(toc-tic))
tic = time.time()
for m in range(len(multiplier)):
scale = multiplier[m]
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(gpu)
output1, output2 = model(feed)
heatmap = nn.UpsamplingBilinear2d(
(oriImg.shape[0], oriImg.shape[1])).cuda(gpu)(output2)
paf = nn.UpsamplingBilinear2d(
(oriImg.shape[0], oriImg.shape[1])).cuda(gpu)(output1)
heatmap_avg[m] = heatmap[0].data
paf_avg[m] = paf[0].data
toc = time.time()
#print('time to forward pass is %.5f'%(toc-tic))
tic = time.time()
heatmap_avg = T.transpose(
T.transpose(T.squeeze(T.mean(heatmap_avg, 0)), 0, 1), 1, 2)
paf_avg = T.transpose(T.transpose(T.squeeze(T.mean(paf_avg, 0)), 0, 1), 1,
2)
heatmap_avg = heatmap_avg.cpu().numpy()
paf_avg = paf_avg.cpu().numpy()
toc = time.time()
#print('time to take averages is %.5f'%(toc-tic))
tic = time.time()
all_peaks = []
peak_counter = 0
#maps =
for part in range(18):
map_ori = heatmap_avg[:, :, part]
map_ak = gaussian_filter(map_ori, sigma=3)
map_left = np.zeros(map_ak.shape)
map_left[1:, :] = map_ak[:-1, :]
map_right = np.zeros(map_ak.shape)
map_right[:-1, :] = map_ak[1:, :]
map_up = np.zeros(map_ak.shape)
map_up[:, 1:] = map_ak[:, :-1]
map_down = np.zeros(map_ak.shape)
map_down[:, :-1] = map_ak[:, 1:]
peaks_binary = np.logical_and.reduce(
(map_ak >= map_left, map_ak >= map_right, map_ak >= map_up,
map_ak >= map_down, map_ak > param_['thre1']))
# peaks_binary = T.eq(
# peaks = zip(T.nonzero(peaks_binary)[0],T.nonzero(peaks_binary)[0])
peaks = list(
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]
some_id = range(peak_counter, peak_counter + len(list(peaks)))
peaks_with_score_and_id = [
peaks_with_score[i] + (some_id[i], ) for i in range(len(some_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])
if norm != 0:
vec = np.divide(vec, norm)
startend = list(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])
if norm != 0:
score_with_dist_prior = sum(score_midpts) / len(
score_midpts) + min(
0.5 * oriImg.shape[0] / norm - 1, 0)
else:
score_with_dist_prior = sum(score_midpts) / len(
score_midpts) + min(0.5 * oriImg.shape[0] - 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]
if (subset[j][indexA] != partAs[i]):
subset[j][indexA] = partAs[i]
subset[j][-1] += 1
subset[j][-2] += candidate[partAs[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)
results = []
stickwidth = 5
for n in range(len(subset)):
limbs = {}
angles = []
position = []
for i in range(17):
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]
limb = ((X[0] - X[1], Y[0] - Y[1]))
limbs[tuple(limbSeq[i])] = limb
#if i == 12:
#position.append(np.mean(X)/x)
#position.append(np.mean(Y)/y)
# 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)
results.append(extract.get_joints(limbs, joints, limbSeq))
#Parallel(n_jobs=1)(delayed(handle_one)(i) for i in range(18))
toc = time.time()
#torch.cuda.empty_cache()
#print('time to feature extract is %.5f'%(toc-tic))
return (results)
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
