image_path = 'try.jpg'
im = Image.open(image_path)
im = np.array(im)
im = np.delete(im, [1, 2], axis=2)
im = np.array(im) / 255.0
im = np.expand_dims(im, axis=0)
print(im.shape)
model = DenseNet(dense_blocks=5,
dense_layers=-1,
growth_rate=8,
dropout_rate=0.2,
bottleneck=True,
compression=1.0,
weight_decay=1e-4,
depth=40)
model.load_weights("outputs/model-230.h5")
lmarks = model.predict(im)
print(lmarks)
lmarks = lmarks[0]
lmarks[0:8:2] = lmarks[0:8:2] * im.shape[2]
lmarks[1:8:2] = lmarks[1:8:2] * im.shape[1]
print(lmarks)
#print(lmarks)
im = im[0] * 255
im = np.squeeze(im, axis=(2, ))
print(im.shape)
for m in range(0, 8, 2):
cv2.circle(im, (int(lmarks[m]), int(lmarks[m + 1])), 5,
(255, 255, 255), -1)
if __name__ == "__main__":
### prediction..
image_path = 'try3.jpg'
im = Image.open(image_path)
im = np.array(im)
im = np.delete(im, [1, 2], axis=2)
im = np.array(im) / 255.0
im = np.expand_dims(im, axis=0)
print(im.shape)
model = DenseNet(dense_blocks=5, dense_layers=-1, growth_rate=8, dropout_rate=0.2,
bottleneck=True, compression=1.0, weight_decay=1e-4, depth=40)
#model.load_weights("outputs/model-230.h5")
model.load_weights("test")
lmarks = model.predict(im)
print (lmarks)
lmarks= lmarks[0]
lmarks[0:8:2] = lmarks[0:8:2] * im.shape[2]
lmarks[1:8:2] = lmarks[1:8:2] * im.shape[1]
print (lmarks)
#print(lmarks)
im = im[0] * 255
im = np.squeeze(im, axis=(2,))
print(im.shape)
for m in range(0, 8,2):