def _get_outputs(multiplier,
img,
heatmaps,
pafs,
numkeypoints=CF.NUM_KEYPOINTS,
numlims=CF.NUM_LIMBS):
"""Computes the averaged heatmap and paf for the given image
:param multiplier:
:param origImg: numpy array, the image being processed
:param model: pytorch model
:returns: numpy arrays, the averaged paf and heatmap
"""
heatmap_avg = np.zeros((img.shape[0], img.shape[1], numkeypoints + 1))
paf_avg = np.zeros((img.shape[0], img.shape[1], numlims * 2))
heatmaps = heatmaps.transpose(0, 2, 3, 1)
pafs = pafs.transpose(0, 2, 3, 1)
# print('len(multiplier)', len(multiplier))
for m in range(len(multiplier)):
scale = multiplier[m]
inp_size = scale * img.shape[0]
# padding
im_cropped, im_scale, real_shape = im_transform.crop_with_factor(
img, inp_size, factor=8, is_ceil=True)
# print('im_cropped', im_cropped.shape, heatmaps.shape)
heatmap = heatmaps[m, :int(im_cropped.shape[0] /
8), :int(im_cropped.shape[1] / 8), :]
heatmap = cv2.resize(heatmap,
None,
fx=8,
fy=8,
interpolation=cv2.INTER_CUBIC)
heatmap = heatmap[0:real_shape[0], 0:real_shape[1], :]
heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0]),
interpolation=cv2.INTER_CUBIC)
paf = pafs[m, :int(im_cropped.shape[0] / 8), :int(im_cropped.shape[1] /
8), :]
paf = cv2.resize(paf, None, fx=8, fy=8, interpolation=cv2.INTER_CUBIC)
paf = paf[0:real_shape[0], 0:real_shape[1], :]
paf = cv2.resize(paf, (img.shape[1], img.shape[0]),
interpolation=cv2.INTER_CUBIC)
heatmap_avg = heatmap_avg + heatmap / len(multiplier)
paf_avg = paf_avg + paf / len(multiplier)
return paf_avg, heatmap_avg
def get_outputs(multiplier, img, model, preprocess):
"""Computes the averaged heatmap and paf for the given image
:param multiplier:
:param origImg: numpy array, the image being processed
:param model: pytorch model
:returns: numpy arrays, the averaged paf and heatmap
"""
heatmap_avg = np.zeros((img.shape[0], img.shape[1], 19))
paf_avg = np.zeros((img.shape[0], img.shape[1], 38))
max_scale = multiplier[-1]
max_size = max_scale * img.shape[0]
# padding
max_cropped, _, _ = im_transform.crop_with_factor(img,
max_size,
factor=8,
is_ceil=True)
batch_images = np.zeros(
(len(multiplier), 3, max_cropped.shape[0], max_cropped.shape[1]))
for m in range(len(multiplier)):
scale = multiplier[m]
inp_size = scale * img.shape[0]
# padding
im_croped, im_scale, real_shape = im_transform.crop_with_factor(
img, inp_size, factor=8, is_ceil=True)
if preprocess == 'rtpose':
im_data = rtpose_preprocess(im_croped)
elif preprocess == 'vgg':
im_data = vgg_preprocess(im_croped)
elif preprocess == 'inception':
im_data = inception_preprocess(im_croped)
elif preprocess == 'ssd':
im_data = ssd_preprocess(im_croped)
batch_images[m, :, :im_data.shape[1], :im_data.shape[2]] = im_data
# several scales as a batch
batch_var = torch.from_numpy(batch_images).cuda().float()
predicted_outputs, _ = model(batch_var)
output1, output2 = predicted_outputs[-2], predicted_outputs[-1]
heatmaps = output2.cpu().data.numpy().transpose(0, 2, 3, 1)
pafs = output1.cpu().data.numpy().transpose(0, 2, 3, 1)
for m in range(len(multiplier)):
scale = multiplier[m]
inp_size = scale * img.shape[0]
# padding
im_cropped, im_scale, real_shape = im_transform.crop_with_factor(
img, inp_size, factor=8, is_ceil=True)
heatmap = heatmaps[m, :int(im_cropped.shape[0] /
8), :int(im_cropped.shape[1] / 8), :]
heatmap = cv2.resize(heatmap,
None,
fx=8,
fy=8,
interpolation=cv2.INTER_CUBIC)
heatmap = heatmap[0:real_shape[0], 0:real_shape[1], :]
heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0]),
interpolation=cv2.INTER_CUBIC)
paf = pafs[m, :int(im_cropped.shape[0] / 8), :int(im_cropped.shape[1] /
8), :]
paf = cv2.resize(paf, None, fx=8, fy=8, interpolation=cv2.INTER_CUBIC)
paf = paf[0:real_shape[0], 0:real_shape[1], :]
paf = cv2.resize(paf, (img.shape[1], img.shape[0]),
interpolation=cv2.INTER_CUBIC)
heatmap_avg = heatmap_avg + heatmap / len(multiplier)
paf_avg = paf_avg + paf / len(multiplier)
return paf_avg, heatmap_avg
feature_extractor5_1 = FeatureExtractor(model.model5_1)
feature_extractor5_2 = FeatureExtractor(model.model5_2)
feature_extractor6_1 = FeatureExtractor(model.model6_1)
feature_extractor6_2 = FeatureExtractor(model.model6_2)
images = os.listdir(image_dir)
# iterate all val images
feed_size = 368
for a_path in images:
one_path = image_dir + a_path
print(one_path)
oriImg = cv2.imread(one_path)
im_croped, im_scale, real_shape = im_transform.crop_with_factor(
oriImg, feed_size, factor=8, is_ceil=True)
print('size of im crop', im_croped.shape)
im_data = vgg_preprocess(im_croped)
#im_data = im_data.transpose([2, 0, 1]).astype(np.float32)
batch_images = np.expand_dims(im_data, 0)
batch_var = torch.from_numpy(batch_images).cuda().float()
all_outputs = []
outputs0, out1 = feature_extractor(batch_var)
outputs1_1, out1_1 = feature_extractor1_1(out1)
