# 前50, 100, 200 看一下是否搞错了
if (step in [50, 500]) or (step > 0 and step % args.test_display == 0):
test_predict = total_image_predict(
ori_image_path=args.test_image_path,
input_placeholder=image,
logits_prob_node=logits_prob,
is_training_placeholder=model._is_training,
sess=sess,
multi_scale=args.multi_scale)
test_label = cv2.imread(args.test_label_path, cv2.IMREAD_GRAYSCALE)
# 保存图片
cv2.imwrite(filename='%spredict_color_%d.png' % (log_path, step),
img=color_predicts(img=test_predict))
result = iou(y_pre=np.reshape(test_predict, -1),
y_true=np.reshape(test_label, -1))
print("======================%d======================" % step)
for key in result.keys():
offset = 40 - key.__len__()
print(key + ' ' * offset + '%.4f' % result[key])
test_summary = tf.Summary(value=[
tf.Summary.Value(tag=key, simple_value=result[key])
for key in result.keys()
])
# 记录summary
sess=sess,
multi=False,
size=512)
g_truth = cv2.resize(np.array(Image.open(line[2])), (512, 512))
result = iou(y_pre=np.reshape(test_predict, -1),
y_true=np.reshape(g_truth, -1))
label_valid = np.unique(g_truth)
for key in sorted(result.keys()):
if key in key_map and not key_map[key] in label_valid:
continue
if np.isnan(result[key]) or result[key] != result[key]:
continue
if not key in all_tests_result:
all_tests_result[key] = [result[key]]
else:
all_tests_result[key].append(result[key])
test_label = color_predicts(img=g_truth)
test_predict = color_predicts(img=test_predict)
full_image = np.concatenate((img_rgb, test_label, test_predict),
axis=1)
out_name = line[0].split('/')[-1].split('.')[0] + '_predict.jpg'
out_path = result_dir + '/' + out_name
cv2.imwrite(out_path, full_image)
print('Saved ' + out_path)
avg_tests_result = {}
for key in sorted(all_tests_result.keys()):
avg_tests_result[key] = sum(all_tests_result[key]) / len(
all_tests_result[key])
offset = 40 - key.__len__()
print(key + ' ' * offset + '%.4f' % avg_tests_result[key])
