def _draw_batch_preds(harn, batch, outputs, lim=16):
"""
Example:
>>> # xdoctest: +REQUIRES(--slow)
>>> kw = {'workers': 0, 'xpu': 'cpu', 'batch_size': 8}
>>> harn = setup_harn(cmdline=False, **kw).initialize()
>>> batch = harn._demo_batch(tag='train')
>>> outputs, loss_parts = harn.run_batch(batch)
>>> toshow = harn._draw_batch_preds(batch, outputs)
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> kwplot.imshow(toshow)
"""
import cv2
im = batch['im'].data.cpu().numpy()
class_true = batch['class_idxs'].data.cpu().numpy()
class_pred = outputs['class_probs'].data.cpu().numpy().argmax(axis=1)
batch_imgs = []
for bx in range(min(len(class_true), lim)):
orig_img = im[bx].transpose(1, 2, 0)
out_size = class_pred[bx].shape[::-1]
orig_img = cv2.resize(orig_img, tuple(map(int, out_size)))
orig_img = kwimage.ensure_alpha_channel(orig_img)
pred_heatmap = kwimage.Heatmap(
class_idx=class_pred[bx],
classes=harn.classes
)
true_heatmap = kwimage.Heatmap(
class_idx=class_true[bx],
classes=harn.classes
)
# TODO: scale up to original image size
pred_img = pred_heatmap.draw_on(orig_img, channel='idx', with_alpha=.5)
true_img = true_heatmap.draw_on(orig_img, channel='idx', with_alpha=.5)
true_img = kwimage.ensure_uint255(true_img)
pred_img = kwimage.ensure_uint255(pred_img)
true_img = kwimage.draw_text_on_image(
true_img, 'true', org=(0, 0), valign='top', color='blue')
pred_img = kwimage.draw_text_on_image(
pred_img, 'pred', org=(0, 0), valign='top', color='blue')
item_img = kwimage.stack_images([pred_img, true_img], axis=1)
batch_imgs.append(item_img)
toshow = kwimage.stack_images_grid(batch_imgs, chunksize=2, overlap=-32)
return toshow
def _draw_batch(harn, batch, decoded, limit=32):
"""
Example:
>>> from ggr_matching import *
>>> harn = setup_harn().initialize()
>>> batch = harn._demo_batch(0, tag='vali')
>>> outputs, loss = harn.run_batch(batch)
>>> decoded = harn._decode(outputs)
>>> stacked = harn._draw_batch(batch, decoded, limit=42)
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> kwplot.imshow(stacked, colorspace='rgb', doclf=True)
>>> kwplot.show_if_requested()
"""
tostack = []
fontkw = {'fontScale': 1.0, 'thickness': 2}
n = min(limit, len(decoded['triple_imgs'][0]))
dsize = (300, 300)
for i in range(n):
ims = [g[i].transpose(1, 2, 0) for g in decoded['triple_imgs']]
ims = [cv2.resize(g, dsize) for g in ims]
ims = [kwimage.atleast_3channels(g) for g in ims]
triple_nxs = [n[i] for n in decoded['triple_nxs']]
text = 'distAP={:.3g} -- distAN={:.3g} -- {}'.format(
decoded['distAP'][i],
decoded['distAN'][i],
str(triple_nxs),
)
color = ('dodgerblue'
if decoded['distAP'][i] < decoded['distAN'][i] else
'orangered')
img = kwimage.stack_images(ims,
overlap=-2,
axis=1,
bg_value=(10 / 255, 40 / 255, 30 / 255))
img = (img * 255).astype(np.uint8)
img = kwimage.draw_text_on_image(img,
text,
org=(2, img.shape[0] - 2),
color=color,
**fontkw)
tostack.append(img)
stacked = kwimage.stack_images_grid(tostack,
overlap=-10,
bg_value=(30, 10, 40),
axis=1,
chunksize=3)
return stacked
import kwplot
import numpy as np
import kwimage
kwplot.autompl()
f = 8
blank_key = np.zeros((64 * f, 54 * f, 3))
blank_key[:, :, :] = np.array(kwimage.Color('darkgray').as255())[None, None, :]
blank_key[0:f * 2, :] = (3, 3, 3)
blank_key[-f * 2:, :] = (3, 3, 3)
blank_key[:, 0:f * 2] = (3, 3, 3)
blank_key[:, -f * 2:] = (3, 3, 3)
key = kwimage.draw_text_on_image(blank_key.copy(), text='!\n1', halign='center', valign='center', color='white')
kwplot.imshow(key)
tab_symbol = '->'
left_rows = []
alt_text0 = [None, None, None, None, None, None, None, None]
row_text0 = ['esc', 'F1', 'F2', 'F3', 'F4', 'F5', 'F6', 'caps']
left_rows += [(alt_text0, row_text0)]
alt_text1 = [None, '~', '!', '@', '#', '$', '%', None]
row_text1 = ['tab', '`', '1', '2', '3', '4', '5', 'win']
left_rows += [(alt_text1, row_text1)]
alt_text2 = ['|', '?', None, None, None, None, None, None]