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-rw-r--r--cli/app/search/search_class.py9
1 files changed, 5 insertions, 4 deletions
diff --git a/cli/app/search/search_class.py b/cli/app/search/search_class.py
index ccb10cd..970d25f 100644
--- a/cli/app/search/search_class.py
+++ b/cli/app/search/search_class.py
@@ -113,7 +113,7 @@ def find_nearest_vector(sess, generator, opt_fp_in, opt_dims, out_images, out_la
## normalize the Y encoding
# normalized_labels = tf.nn.l2_normalize(input_y)
# tf.reduce_mean(tf.abs(encoding - gen_encoding))
- normalized_labels = input_y / tf.reduce_max(input_y)
+ normalized_labels = input_y / tf.min(1.0, tf.reduce_max(input_y))
normalized_alpha = tf.compat.v1.placeholder(dtype=np.float32, shape=())
clip_labels = tf.assign(input_y, normalized_labels * (1 - normalized_alpha) + input_y * normalized_alpha)
@@ -164,7 +164,7 @@ def find_nearest_vector(sess, generator, opt_fp_in, opt_dims, out_images, out_la
train_step_z = tf.train.AdamOptimizer(z_lr).minimize(loss, var_list=[input_z], name='AdamOpterZ')
train_step_y = tf.train.AdamOptimizer(y_lr).minimize(loss, var_list=[input_y], name='AdamOpterY')
- target_im, fp_frames, fn_base = load_target_image(opt_fp_in)
+ target_im, fp_frames, fn_base = load_target_image(opt_fp_in, opt_video)
# crop image and convert to format for next script
phi_target_for_inversion = resize_and_crop_image(target_im, 512)
@@ -216,13 +216,14 @@ def find_nearest_vector(sess, generator, opt_fp_in, opt_dims, out_images, out_la
out_latent[index] = z_guess
return fp_frames
-def load_target_image(opt_fp_in):
+def load_target_image(opt_fp_in, opt_video):
print("Loading {}".format(opt_fp_in))
fn = os.path.basename(opt_fp_in)
fn_base, ext = os.path.splitext(fn)
fp_frames = "frames_{}_{}".format(fn_base, timestamp())
fp_frames_fullpath = join(app_cfg.DIR_OUTPUTS, fp_frames)
print("Output to {}".format(fp_frames_fullpath))
- os.makedirs(fp_frames_fullpath, exist_ok=True)
+ if opt_video:
+ os.makedirs(fp_frames_fullpath, exist_ok=True)
target_im = imread(opt_fp_in)
return target_im, fp_frames, fn_base