def worker_thread(result_fn_queue, eval_dir, label_dir, pad, offset, channel,
steps, relax, all_positive, all_prec_tp, all_true,
all_recall_tp):
while True:
i, result_fn = result_fn_queue.get()
if result_fn is None:
break
img_id = basename(result_fn).split('pred_')[-1]
img_id, _ = os.path.splitext(img_id)
if '.' in img_id:
img_id = img_id.split('.')[0]
if len(re.findall('_', img_id)) > 1:
img_id = '_'.join(img_id.split('_')[1:])
out_dir = '{}{}'.format(eval_dir, img_id)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
print(img_id)
label = cv.imread('{}{}.tif'.format(label_dir, img_id),
cv.IMREAD_GRAYSCALE)
pred = np.load(result_fn)
label = label[pad + offset - 1:pad + offset - 1 + pred.shape[0],
pad + offset - 1:pad + offset - 1 + pred.shape[1]]
cv.imwrite('{}/label_{}.png'.format(out_dir, img_id), label * 125)
print('pred_shape: {}'.format(pred.shape))
for c in six.moves.range(channel):
for t in six.moves.range(0, steps):
threshold = 1.0 / steps * t
pred_vals = np.array(pred[:, :, c] >= threshold,
dtype=np.int32)
label_vals = np.array(label, dtype=np.int32)
if channel > 1:
label_vals = np.array(label == c, dtype=np.int32)
all_positive[i, c, t] = np.sum(pred_vals)
all_prec_tp[i, c, t] = relax_precision(pred_vals, label_vals,
relax)
all_true[i, c, t] = np.sum(label_vals)
all_recall_tp[i, c, t] = relax_recall(pred_vals, label_vals,
relax)
pre_rec, breakeven_pt = get_pre_rec(all_positive[i, c],
all_prec_tp[i, c], all_true[i,
c],
all_recall_tp[i, c], steps)
draw_pre_rec_curve(pre_rec, breakeven_pt)
plt.savefig('{}/pr_curve_{}.png'.format(out_dir, c))
np.save('{}/pre_rec_{}'.format(out_dir, c), pre_rec)
cv.imwrite('{}/pred_{}.png'.format(out_dir, c),
pred[:, :, c] * 255)
print(img_id, c, breakeven_pt)
print('thread finished')
def worker_thread(result_fn_queue):
while True:
i, result_fn = result_fn_queue.get()
if result_fn is None:
break
img_id = basename(result_fn).split('pred_')[-1]
img_id, _ = os.path.splitext(img_id)
if '.' in img_id:
img_id = img_id.split('.')[0]
if len(re.findall('_', img_id)) > 1:
img_id = '_'.join(img_id.split('_')[1:])
out_dir = '%s/%s' % (eval_dir, img_id)
makedirs(out_dir)
print(img_id)
label = cv.imread('%s/%s.tif' %
(label_dir, img_id), cv.IMREAD_GRAYSCALE)
pred = np.load(result_fn)
pred = pred[args.offset:1500 - (60 - args.offset),
args.offset:1500 - (60 - args.offset)]
label = label[args.pad + args.offset:
args.pad + args.offset + pred.shape[0],
args.pad + args.offset:
args.pad + args.offset + pred.shape[1]]
cv.imwrite('%s/label_%s.png' % (out_dir, img_id), label * 125)
print('label_shape:', label.shape)
print('pred_shape:', pred.shape)
for c in six.moves.range(args.channel):
for t in six.moves.range(0, args.steps):
threshold = 1.0 / args.steps * t
pred_vals = np.array(
pred[:, :, c] >= threshold, dtype=np.int32)
label_vals = np.array(label, dtype=np.int32)
if args.channel > 1:
label_vals = np.array(label == c, dtype=np.int32)
all_positive[i, c, t] = np.sum(pred_vals)
all_prec_tp[i, c, t] = relax_precision(
pred_vals, label_vals, args.relax)
all_true[i, c, t] = np.sum(label_vals)
all_recall_tp[i, c, t] = relax_recall(
pred_vals, label_vals, args.relax)
pre_rec, breakeven_pt = get_pre_rec(
all_positive[i, c], all_prec_tp[i, c],
all_true[i, c], all_recall_tp[i, c], args.steps)
draw_pre_rec_curve(pre_rec, breakeven_pt)
plt.savefig('%s/pr_curve_%d.png' % (out_dir, c))
np.save('%s/pre_rec_%d' % (out_dir, c), pre_rec)
cv.imwrite('%s/pred_%d.png' % (out_dir, c), pred[:, :, c] * 255)
print(img_id, c, breakeven_pt)
print('thread finished')
def worker_thread(result_fn_queue):
while True:
i, result_fn = result_fn_queue.get()
if result_fn is None:
break
img_id = basename(result_fn).split('pred_')[-1]
img_id, _ = os.path.splitext(img_id)
if '.' in img_id:
img_id = img_id.split('.')[0]
if len(re.findall('_', img_id)) > 1:
img_id = '_'.join(img_id.split('_')[1:])
out_dir = '%s/%s' % (eval_dir, img_id)
makedirs(out_dir)
print(img_id)
label = cv.imread('%s/%s.tif' %
(label_dir, img_id), cv.IMREAD_GRAYSCALE)
pred = np.load(result_fn)
pred = pred[args.offset:1500 - (60 - args.offset),
args.offset:1500 - (60 - args.offset)]
label = label[args.pad + args.offset:
args.pad + args.offset + pred.shape[0],
args.pad + args.offset:
args.pad + args.offset + pred.shape[1]]
cv.imwrite('%s/label_%s.png' % (out_dir, img_id), label * 125)
print('label_shape:', label.shape)
print('pred_shape:', pred.shape)
for c in six.moves.range(args.channel):
for t in six.moves.range(0, args.steps):
threshold = 1.0 / args.steps * t
pred_vals = np.array(
pred[:, :, c] >= threshold, dtype=np.int32)
label_vals = np.array(label, dtype=np.int32)
if args.channel > 1:
label_vals = np.array(label == c, dtype=np.int32)
all_positive[i, c, t] = np.sum(pred_vals)
all_prec_tp[i, c, t] = relax_precision(
pred_vals, label_vals, args.relax)
all_true[i, c, t] = np.sum(label_vals)
all_recall_tp[i, c, t] = relax_recall(
pred_vals, label_vals, args.relax)
pre_rec, breakeven_pt = get_pre_rec(
all_positive[i, c], all_prec_tp[i, c],
all_true[i, c], all_recall_tp[i, c], args.steps)
draw_pre_rec_curve(pre_rec, breakeven_pt)
plt.savefig('%s/pr_curve_%d.png' % (out_dir, c))
np.save('%s/pre_rec_%d' % (out_dir, c), pre_rec)
cv.imwrite('%s/pred_%d.png' % (out_dir, c), pred[:, :, c] * 255)
print(img_id, c, breakeven_pt)
print('thread finished')
def get_relaxed_pre_rec(p_patch, l_patch):
p_patch = np.array(p_patch, dtype=np.int32)
l_patch = np.array(l_patch, dtype=np.int32)
positive = np.sum(p_patch == 1)
prec_tp = relax_precision(p_patch, l_patch, RELAX)
true = np.sum(l_patch == 1)
recall_tp = relax_recall(p_patch, l_patch, RELAX)
if prec_tp > positive or recall_tp > true:
print(positive, prec_tp, true, recall_tp)
sys.exit('Calculation is wrong.')
return positive, prec_tp, true, recall_tp
def calc_prec_recall(pred, label, t):
deno = 1.0 / 256 * t
pred = np.asarray(pred >= deno, dtype=np.int32)
positive = np.sum(pred == 1)
true = np.sum(label == 1)
prec_tp = relax_precision(pred, label, relax)
recall_tp = relax_recall(pred, label, relax)
if prec_tp > positive or recall_tp > true:
print(positive, prec_tp, true, recall_tp)
sys.exit('Calculation is wrong.')
prec = prec_tp / float(positive) if positive > 0 else 1.0
recall = recall_tp / float(true) if true > 0 else 1.0
return (prec, recall)
def calc_prec_recall(pred, label, t):
deno = 1.0 / 256 * t
pred = np.asarray(pred >= deno, dtype=np.int32)
positive = np.sum(pred == 1)
true = np.sum(label == 1)
prec_tp = relax_precision(pred, label, relax)
recall_tp = relax_recall(pred, label, relax)
if prec_tp > positive or recall_tp > true:
print(positive, prec_tp, true, recall_tp)
sys.exit('Calculation is wrong.')
prec = prec_tp / float(positive) if positive > 0 else 1.0
recall = recall_tp / float(true) if true > 0 else 1.0
return (prec, recall)