From 326db345db13b1ab3a76406644654cb78b4d1b8d Mon Sep 17 00:00:00 2001
From: "jules@lens" <julescarbon@gmail.com>
Date: Sun, 8 Dec 2019 11:59:27 +0100
Subject: biggan search test

---
 cli/app/commands/biggan/test.py | 116 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 116 insertions(+)
 create mode 100644 cli/app/commands/biggan/test.py

(limited to 'cli/app/commands/biggan/test.py')

diff --git a/cli/app/commands/biggan/test.py b/cli/app/commands/biggan/test.py
new file mode 100644
index 0000000..593d557
--- /dev/null
+++ b/cli/app/commands/biggan/test.py
@@ -0,0 +1,116 @@
+import click
+
+from app.utils import click_utils
+from app.settings import app_cfg
+
+import os
+from os.path import join
+import time
+import numpy as np
+import random
+from scipy.stats import truncnorm
+from subprocess import call
+import cv2 as cv
+
+from PIL import Image
+
+def image_to_uint8(x):
+  """Converts [-1, 1] float array to [0, 255] uint8."""
+  x = np.asarray(x)
+  x = (256. / 2.) * (x + 1.)
+  x = np.clip(x, 0, 255)
+  x = x.astype(np.uint8)
+  return x
+
+def truncated_z_sample(batch_size, z_dim, truncation):
+  values = truncnorm.rvs(-2, 2, size=(batch_size, z_dim))
+  return truncation * values
+
+def truncated_z_single(z_dim, truncation):
+  values = truncnorm.rvs(-2, 2, size=(1, z_dim))
+  return truncation * values
+
+def create_labels(batch_size, vocab_size, num_classes):
+  label = np.zeros((batch_size, vocab_size))
+  for i in range(batch_size):
+    for _ in range(random.randint(1, num_classes)):
+      j = random.randint(0, vocab_size-1)
+      label[i, j] = random.random()
+    label[i] /= label[i].sum()
+  return label
+
+def imconvert_uint8(im):
+  im = np.clip(((im + 1) / 2.0) * 256, 0, 255)
+  im = np.uint8(im)
+  return im
+
+def imconvert_float32(im):
+  im = np.float32(im)
+  im = (im / 256) * 2.0 - 1
+  return im
+
+def imread(filename):
+  img = cv.imread(filename, cv.IMREAD_UNCHANGED)
+  if img is not None:
+    if len(img.shape) > 2:
+      img = img[...,::-1]
+  return img
+  
+def imwrite(filename, img):
+  if img is not None:
+    if len(img.shape) > 2:
+      img = img[...,::-1]
+  return cv.imwrite(filename, img)
+
+def imgrid(imarray, cols=5, pad=1):
+  if imarray.dtype != np.uint8:
+    raise ValueError('imgrid input imarray must be uint8')
+  pad = int(pad)
+  assert pad >= 0
+  cols = int(cols)
+  assert cols >= 1
+  N, H, W, C = imarray.shape
+  rows = int(np.ceil(N / float(cols)))
+  batch_pad = rows * cols - N
+  assert batch_pad >= 0
+  post_pad = [batch_pad, pad, pad, 0]
+  pad_arg = [[0, p] for p in post_pad]
+  imarray = np.pad(imarray, pad_arg, 'constant', constant_values=255)
+  H += pad
+  W += pad
+  grid = (imarray
+          .reshape(rows, cols, H, W, C)
+          .transpose(0, 2, 1, 3, 4)
+          .reshape(rows*H, cols*W, C))
+  if pad:
+    grid = grid[:-pad, :-pad]
+  return grid
+
+@click.command('')
+@click.option('-i', '--input', 'opt_fp_in', required=True, 
+  help='Path to input image')
+@click.option('-s', '--dims', 'opt_dims', default=128, type=int,
+  help='Dimensions of BigGAN network (128, 256, 512)')
+# @click.option('-r', '--recursive', 'opt_recursive', is_flag=True)
+@click.pass_context
+def cli(ctx, opt_fp_in, opt_dims):
+  """
+  Search for an image in BigGAN using gradient descent
+  """
+  if opt_fp_in:
+    target_im = imread(opt_fp_in)
+    w = target_im.shape[1]
+    h = target_im.shape[0]
+    if w <= h:
+      scale = opt_dims / w
+    else:
+      scale = opt_dims / h
+    print("{} {}".format(w, h))
+    target_im = cv.resize(target_im,(0,0), fx=scale, fy=scale)
+    phi_target = target_im[:opt_dims,:opt_dims]
+    print(phi_target.shape)
+    print(phi_target[64,64])
+    if phi_target.shape[2] == 4:
+      phi_target_a = phi_target[:,:,1:4]
+      imwrite('crop.png', phi_target_a)
+
-- 
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