:param targets: [[x, y, v], ...]
:param widths: [[w1], [w2], ...]
:return:
'''
dist = preds[:, :2] - targets[:, :2]
dist = np.sqrt(dist[:, 0]**2 + dist[:, 1]**2)
targets = np.copy(targets)
targets[targets<0] = 0
if np.sum(targets[:, 2]) == 0:
return 0
ne = np.sum(dist/widths * targets[:, 2]) / np.sum(targets[:, 2])
return ne
if __name__ == '__main__':
from kpda_parser import KPDA
from src.config import Config
import cv2
config = Config('trousers')
db_path = '/home/storage/lsy/fashion/FashionAI_Keypoint_Detection/'
kpda = KPDA(config, db_path, 'train')
for idx in range(kpda.size()):
img = cv2.imread(kpda.get_image_path(idx)) # BGR
# bboxes = [kpda.get_bbox(idx)]
# probs = [1.]
# draw_bbox(img, bboxes, probs, '/home/storage/lsy/fashion/tmp/%d.png' % idx)
keypoints = kpda.get_keypoints(idx)
if np.all(keypoints[:, 2]>0):
print(keypoints)
draw = draw_keypoints(img, keypoints)
cv2.imwrite('/home/storage/lsy/fashion/tmp/%d.png' % idx, draw)
break
y2, x2 = np.unravel_index(si[:, -2], [h, w])
x = (3 * x1 + x2) / 4.
y = (3 * y1 + y2) / 4.
var = np.var(heatmap_th, axis=1)
x[var < 1] = dfx
y[var < 1] = dfy
x = x * stride / scale
y = y * stride / scale
return np.rint(x + 2), np.rint(y + 2)
if __name__ == '__main__':
from kpda_parser import KPDA
import cv2
from src.config import Config
import numpy as np
config = Config()
db_path = '/home/storage/lsy/fashion/FashionAI_Keypoint_Detection/train/'
kpda = KPDA(db_path, 'train')
img_path = kpda.get_image_path(2)
kpts = kpda.get_keypoints(2)
kpts = torch.from_numpy(kpts)
img = cv2.imread(img_path)
image = np.zeros([512, 512, 3])
image[:512, :504, :] = img
cv2.imwrite('/home/storage/lsy/fashion/tmp/img.jpg', image)
ke = KeypointEncoder()
heatmaps, _ = ke.encode(kpts, image.shape[:2], config.hm_stride)
for i, heatmap in enumerate(heatmaps):
heatmap = np.expand_dims(heatmap.numpy() * 255, 2)
cv2.imwrite('/home/storage/lsy/fashion/tmp/map%d.jpg' % i, heatmap)
net = net.cuda()
net = DataParallel(net)
net.eval()
net2 = CascadePyramidNetV9(config)
checkpoint = torch.load(
root_path +
'checkpoints/dress_030_senet_posneu_lgtrain.ckpt') # must before cuda
net2.load_state_dict(checkpoint['state_dict'])
net2 = net2.cuda()
net2 = DataParallel(net2)
net2.eval()
encoder = KeypointEncoder()
nes = []
for idx in tqdm(range(val_kpda.size())):
img_path = val_kpda.get_image_path(idx)
kpts = val_kpda.get_keypoints(idx)
img0 = cv2.imread(img_path) # BGR
img0_flip = cv2.flip(img0, 1)
img_h, img_w, _ = img0.shape
# ----------------------------------------------------------------------------------------------------------------------
scale = config.img_max_size / max(img_w, img_h)
hm_pred = compute_keypoints(config, img0, net, encoder)
hm_pred2 = compute_keypoints(config,
img0_flip,
net,
encoder,
doflip=True)
hm_pred3 = compute_keypoints(config, img0, net2, encoder)
hm_pred4 = compute_keypoints(config,
img0_flip,