diff --git a/scripts/evaluate.py b/scripts/evaluate.py
index 24d84fb6105a6b4a3485680991d9fa46b3b1cb9d..b9fd44a3476b72035e1608c2f849342d7bec9964 100644
--- a/scripts/evaluate.py
+++ b/scripts/evaluate.py
@@ -16,73 +16,18 @@ import tensorflow as tf
from video_prediction import datasets, models
-def compute_expectation_np(pix_distrib):
- assert pix_distrib.shape[-1] == 1
- pix_distrib = pix_distrib / np.sum(pix_distrib, axis=(-3, -2), keepdims=True)
- height, width = pix_distrib.shape[-3:-1]
- xv, yv = np.meshgrid(np.arange(width), np.arange(height))
- return np.stack([np.sum(yv[:, :, None] * pix_distrib, axis=(-3, -2, -1)),
- np.sum(xv[:, :, None] * pix_distrib, axis=(-3, -2, -1))], axis=-1)
-
-
-def as_heatmap(image, normalize=True):
- import matplotlib.pyplot as plt
- image = np.squeeze(image, axis=-1)
- if normalize:
- image = image / np.max(image, axis=(-2, -1), keepdims=True)
- cmap = plt.get_cmap('viridis')
- heatmap = cmap(image)[..., :3]
- return heatmap
-
-
-def rgb2gray(rgb):
- return np.dot(rgb[..., :3], [0.299, 0.587, 0.114])
-
-
-def resize_and_draw_circle(image, size, center, radius, dpi=128.0, **kwargs):
- import matplotlib.pyplot as plt
- from matplotlib.patches import Circle
- import io
- height, width = size
- fig = plt.figure(figsize=(width / dpi, height / dpi), dpi=dpi)
- ax = fig.add_axes([0, 0, 1, 1])
- ax.imshow(image, interpolation='none')
- circle = Circle(center[::-1], radius=radius, **kwargs)
- ax.add_patch(circle)
- ax.axis("off")
- fig.canvas.draw()
- trans = ax.figure.dpi_scale_trans.inverted()
- bbox = ax.bbox.transformed(trans)
- buff = io.BytesIO()
- plt.savefig(buff, format="png", dpi=ax.figure.dpi, bbox_inches=bbox)
- buff.seek(0)
- image = plt.imread(buff)[..., :3]
- plt.close(fig)
- return image
-
-
-def save_image_sequence(prefix_fname, images, overlaid_images=None, centers=None,
- radius=5, alpha=0.8, time_start_ind=0):
+def save_image_sequence(prefix_fname, images, time_start_ind=0):
import cv2
head, tail = os.path.split(prefix_fname)
if head and not os.path.exists(head):
os.makedirs(head)
- if images.shape[-1] == 1:
- images = as_heatmap(images)
- if overlaid_images is not None:
- assert images.shape[-1] == 3
- assert overlaid_images.shape[-1] == 1
- gray_images = rgb2gray(images)
- overlaid_images = as_heatmap(overlaid_images)
- images = (1 - alpha) * gray_images[..., None] + alpha * overlaid_images
for t, image in enumerate(images):
image_fname = '%s_%02d.png' % (prefix_fname, time_start_ind + t)
- if centers is not None:
- scale = np.max(np.array([256, 256]) / np.array(image.shape[:2]))
- image = resize_and_draw_circle(image, np.array(image.shape[:2]) * scale, centers[t], radius,
- edgecolor='r', fill=False, linestyle='--', linewidth=2)
image = (image * 255.0).astype(np.uint8)
- image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+ if image.shape[-1] == 1:
+ image = np.tile(image, (1, 1, 3))
+ else:
+ image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
cv2.imwrite(image_fname, image)
@@ -139,7 +84,10 @@ def merge_hparams(hparams0, hparams1):
def save_prediction_eval_results(task_dir, results, model_hparams, sample_start_ind=0, only_metrics=False, subtasks=None):
+ sequence_length = model_hparams.sequence_length
context_frames = model_hparams.context_frames
+ future_length = sequence_length - context_frames
+
context_images = results['images'][:, :context_frames]
if 'eval_diversity' in results:
@@ -159,6 +107,8 @@ def save_prediction_eval_results(task_dir, results, model_hparams, sample_start_
for metric_name in metric_names:
subtask_dir = task_dir + '_%s_%s' % (metric_name, subtask)
gen_images = results.get('eval_gen_images_%s/%s' % (metric_name, subtask), results.get('eval_gen_images'))
+ # only keep the future frames
+ gen_images = gen_images[:, -future_length:]
metric = results['eval_%s/%s' % (metric_name, subtask)]
save_metrics(os.path.join(subtask_dir, 'metrics', metric_name),
metric, sample_start_ind=sample_start_ind)
diff --git a/scripts/evaluate_svg.sh b/scripts/evaluate_svg.sh
new file mode 100644
index 0000000000000000000000000000000000000000..212c4ba239ecdbd15c70c05b9336c32175dc8c5c
--- /dev/null
+++ b/scripts/evaluate_svg.sh
@@ -0,0 +1 @@
+#!/usr/bin/env bash
\ No newline at end of file
diff --git a/scripts/generate.py b/scripts/generate.py
index 55eab9d221553d65190f7ba25d344b86171c6fa2..534eb5fa902341e3ca65a1f733f2c5f841c84210 100644
--- a/scripts/generate.py
+++ b/scripts/generate.py
@@ -115,6 +115,10 @@ def main():
hparams_dict=hparams_dict,
hparams=args.model_hparams)
+ sequence_length = model.hparams.sequence_length
+ context_frames = model.hparams.context_frames
+ future_length = sequence_length - context_frames
+
if args.num_samples:
if args.num_samples > dataset.num_examples_per_epoch():
raise ValueError('num_samples cannot be larger than the dataset')
@@ -159,6 +163,8 @@ def main():
feed_dict = {input_ph: input_results[name] for name, input_ph in input_phs.items()}
for stochastic_sample_ind in range(args.num_stochastic_samples):
gen_images = sess.run(model.outputs['gen_images'], feed_dict=feed_dict)
+ # only keep the future frames
+ gen_images = gen_images[:, -future_length:]
for i, gen_images_ in enumerate(gen_images):
gen_images_ = (gen_images_ * 255.0).astype(np.uint8)