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
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])
# MODIFIED.
try:
score_with_dist_prior = sum(score_midpts) / len(
score_midpts) + min(
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])
if (norm == 0 or len(score_midpts) == 0):
continue
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)
# for i in range 17:
# for j in range(len(all_peaks[10])):
# canvas = cv2.imread(test_image) # B,G,R order
# for i in [10, 13]:
# for j in range(len(all_peaks[10])):
# cv2.circle(canvas, all_peaks[i][j][0:2], 4, colors[i], thickness=-1)
# if len(all_peaks[13]) >= j:
# center = get_center(all_peaks[10][j][0:2], all_peaks[13][j][0:2])
# heat_map = heat_map_circle(heat_map, center)
# cv2.circle(canvas, center, 4, colors[0], thickness=-1)
# cv2.circle(canvas, all_peaks[10][j][0:2], 4, colors[2], thickness=-1)
#
# cv2.circle(canvas, all_peaks[13][j][0:2], 4, colors[3], thickness=-1)
stickwidth = 4
track_list = []
# return canvas, heat_map
# while True:
# bbox = cv2.selectROI("tracker", canvas)
# print bbox
for n in range(len(subset)):
person_points = []
for i in range(19):
# for i in [7,8,10,11]:
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]
mX = np.mean(X)
mY = np.mean(Y)
cur_canvas = canvas.copy()
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)
person_points.extend([(Y[0], X[0]), (Y[1], X[1])])
bounding_box = find_person_bounding_box(person_points)
track_list.append(tracker(TRACK_TYPE, bounding_box, canvas))
# for i in range(19):
# 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, track_list
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)
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] +
0.0000000000001) # +1 부분 수정
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
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]])