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evaluate_model.py
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evaluate_model.py
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# !/usr/bin/env python3
# coding=utf-8
import matplotlib.pyplot as plt
import time
from pose_estimation import *
from scipy.ndimage.filters import gaussian_filter
# Initialize joint parameters
# find connection in the specified sequence, center 29 is in the position 15
limb_seq = [[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
map_ids = [[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]]
# 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]]
use_gpu = True
box_size = 368
scale_search = [0.5, 1.0, 1.5, 2.0]
param_thre1 = 0.1
param_thre2 = 0.05
param_thre3 = 0.5
param_stride = 8
# # loading float32 model
# state_dict = torch.load('./models/coco_pose_iter_440000.pth.tar')['state_dict']
#
# model = get_pose_model()
# model.load_state_dict(state_dict)
# model.float()
# model.eval()
# Load int8 model
state_dict = torch.load('./openpose_vgg_quant.pth')
model_fp32 = get_pose_model()
model_fp32.qconfig = torch.quantization.get_default_qconfig('fbgemm')
model_fp32_prepared = torch.quantization.prepare(model_fp32)
model_int8 = torch.quantization.convert(model_fp32_prepared)
model_int8.load_state_dict(state_dict)
model = model_int8
model.eval()
start_time = time.time()
# Predict pictures
test_image = './test.jpg'
img_ori = cv2.imread(test_image) # B,G,R order
multiplier = [scale * box_size / img_ori.shape[0] for scale in scale_search]
heatmap_avg = torch.zeros((len(multiplier), 19, img_ori.shape[0], img_ori.shape[1]))
paf_avg = torch.zeros((len(multiplier), 38, img_ori.shape[0], img_ori.shape[1]))
# display the validation pics
plt.figure(figsize=(12, 8))
plt.imshow(img_ori[...,::-1])
for i, scale in enumerate(multiplier):
h = int(img_ori.shape[0] * scale)
w = int(img_ori.shape[1] * scale)
pad_h = 0 if (h % param_stride == 0) else param_stride - (h % param_stride)
pad_w = 0 if (w % param_stride == 0) else param_stride - (w % param_stride)
new_h = h + pad_h
new_w = w + pad_w
img_test = cv2.resize(img_ori, (0, 0), fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC)
img_test_pad, pad = pad_right_down_corner(img_test, param_stride, param_stride)
img_test_pad = np.transpose(np.float32(img_test_pad[:, :, :, np.newaxis]), (3, 2, 0, 1)) / 256 - 0.5
feed = Variable(torch.from_numpy(img_test_pad))
output1, output2 = model(feed)
print(output1.size())
print(output2.size())
heatmap = nn.UpsamplingBilinear2d((img_ori.shape[0], img_ori.shape[1])).cuda()(output2)
paf = nn.UpsamplingBilinear2d((img_ori.shape[0], img_ori.shape[1])).cuda()(output1)
heatmap_avg[i] = heatmap[0].data
paf_avg[i] = paf[0].data
heatmap_avg = torch.transpose(torch.transpose(torch.squeeze(torch.mean(heatmap_avg, 0)), 0, 1), 1, 2)
paf_avg = torch.transpose(torch.transpose(torch.squeeze(torch.mean(paf_avg, 0)), 0, 1), 1, 2)
heatmap_avg = heatmap_avg.cpu().numpy()
paf_avg = paf_avg.cpu().numpy()
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)
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)
indexA, indexB = limb_seq[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))
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) + min(0.5 * img_ori.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(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])
# delete some rows of subset which has few parts occur
del_ids = []
for i in range(len(subset)):
if subset[i][-1] < 4 or subset[i][-2]/subset[i][-1] < 0.4:
del_ids.append(i)
subset = np.delete(subset, del_ids, axis=0)
img_canvas = cv2.imread(test_image) # B,G,R order
for i in range(18):
for j in range(len(all_peaks[i])):
cv2.circle(img_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(limb_seq[i])-1]
if -1 in index:
continue
cur_canvas = img_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])
img_canvas = cv2.addWeighted(img_canvas, 0.4, cur_canvas, 0.6, 0)
print("run time:", time.time()-start_time)
cv2.imwrite('result.png', img_canvas)
plt.figure(figsize=(12, 8))
plt.imshow(img_canvas[...,::-1])
plt.show()