def seg_predict_image_task1(image_path, use_tta=False):
'''只针对一张图片'''
num_folds = 4
# use_tta = False # 如果为false,表示不采用取平均数方式
y_pred = np.zeros(shape=(1, 224, 224))
resize_image_np, W, H = get_resize_image_np(image_path, 224)
if use_tta:
# 采用取平均数的模式
for k_fold in range(num_folds + 1):
model_name = 'task1_vgg16'
run_name = 'task1_vgg16_k%d_v0' % k_fold
model = backbone('vgg16').segmentation_model(load_from=run_name)
# model.load_weights("/home/zhangfan/workData/LinuxCode/pythonProject/ISIC2018/model_data/task1_vgg16_k0_v0/task1_vgg16_k0_v0.hdf5")
y_pred += inv_sigmoid(
model.predict_on_batch(resize_image_np))[:, :, :, 0]
y_pred /= (num_folds + 1)
else:
# 只是用最后一组数据的权重
run_name = 'task1_vgg16_k%d_v0' % num_folds
model = backbone('vgg16').segmentation_model(load_from=run_name)
y_pred += inv_sigmoid(model.predict_on_batch(resize_image_np))[:, :, :,
0]
y_pred = sigmoid(y_pred)
current_pred = y_pred[0] * 255
current_pred[current_pred > 128] = 255
current_pred[current_pred <= 128] = 0
# 将resized_pred传给Nodejs
print({"image_np": current_pred, "width": W, "height": H})
def cls_predict_image(image_path,use_tta=False):
'''识别一张图片'''
num_folds =4
use_tta = False
y_pred = np.zeros(shape=(1,7))
resize_image_np,W,H = get_resize_image_np(image_path,224)
if use_tta:
for k_fold in range(num_folds+1):
run_name = "task3_inception_v3_k"+str(k_fold)+"_v0"
model = backbone("inception_v3").classification_model(load_from=run_name)
# 这里可以考虑使用多线程
if use_tta:
y_pred += inv_sigmoid(task3_tta_predict(model=model,img_arr=resize_image_np))
else:
y_pred += inv_sigmoid(model.predict_on_batch(resize_image_np))
else:
run_name = "task3_inception_v3_k" + str(num_folds) + "_v0"
model = backbone("inception_v3").classification_model(load_from=run_name)
y_pred += inv_sigmoid(model.predict_on_batch(resize_image_np))
y_pred = y_pred / num_folds
y_prob = softmax(y_pred)
print({
"result":y_prob
})
def cls_predict_images(image_paths):
'''识别一组图片'''
num_folds = 5
use_tta = False
run_name = "task3_inception_v3_k" + str(num_folds) + "_v0"
model = backbone("inception_v3").classification_model(load_from=run_name)
for image_path in image_paths:
y_pred = np.zeros(shape=(1,7))
resize_image_np, W, H = get_resize_image_np(image_path,224)
y_pred += inv_sigmoid(model.predict_on_batch(resize_image_np))
y_prod = softmax(y_pred)
print({
"result": y_prod
})
def seg_predict_images_task1(image_paths, use_tta=False):
'''输入图片地址，预测一组图片'''
num_folds = 4
use_tta = False
image_num = len(image_paths) # 得到图片的数量
if use_tta:
pass
else:
y_pred = np.zeros(shape=(1, 224, 224))
run_name = 'task1_vgg16_k%d_v0' % num_folds
model = backbone('vgg16').segmentation_model(load_from=run_name)
for image_path in image_paths:
resize_image_np, W, H = get_resize_image_np(image_path, 224)
y_pred += inv_sigmoid(
model.predict_on_batch(resize_image_np))[:, :, :, 0]
y_pred = sigmoid(y_pred)
current_pred = y_pred[0] * 255
current_pred[current_pred > 128] = 255
current_pred[current_pred <= 128] = 0
print({"image_np": current_pred, "width": W, "height": H})
def seg_predict_images_task1(self,image_paths):
results = []
resize_images_np,images_size = get_resize_images_np(image_paths)
images_num = len(images_size)
y_pred = np.zeros(shape=(images_num,224,224))
with self.graph.as_default():
y_pred += inv_sigmoid(self.task1_model.predict_on_batch(resize_images_np))[:, :, :, 0]
y_pred = sigmoid(y_pred)
y_pred = task1_post_process(y_prediction=y_pred,threshold=0.5,gauss_sigma=2.)
for index,image_path in enumerate(image_paths):
image_dir,image_name,image_ext = get_fileDirectoryPath_fileName_fileExt(image_path)
current_pred = y_pred[index]
current_pred = current_pred * 255
resized_pred = transform.resize(current_pred,
output_shape=images_size[index],
preserve_range=True,
mode='reflect')
resized_pred[resized_pred > 128] = 255
resized_pred[resized_pred <= 128] = 0
img = Image.fromarray(resized_pred.astype(np.uint8))
image_path = os.path.join(task1_result_dir,image_name+"_result"+image_ext)
img.save(image_path)
results.append(image_path)
return results
image_names = image_names[:max_num_images]
image_sizes = image_sizes[:max_num_images]
y_pred = np.zeros(shape=(max_num_images, 224, 224))
num_folds = 5
print('Starting prediction for set %s with TTA set to %r with num_folds %d' % (pred_set, use_tta, num_folds))
for k_fold in range(num_folds):
print('Processing fold ', k_fold)
model_name = 'task%d_%s' % (task_idx, backbone_name)
run_name = 'task%d_%s_k%d_v%s' % (task_idx, backbone_name, k_fold, version)
model = backbone(backbone_name).segmentation_model(load_from=run_name)
if use_tta:
y_pred += inv_sigmoid(task1_tta_predict(model=model, img_arr=images))[:, :, :, 0]
else:
y_pred += inv_sigmoid(model.predict(images))[:, :, :, 0]
print('Done predicting -- now doing post-processing')
y_pred = y_pred / num_folds
y_pred = sigmoid(y_pred)
y_pred = task1_post_process(y_prediction=y_pred, threshold=0.5, gauss_sigma=2.)
output_dir = submission_dir + '/task1_' + pred_set
mkdir_if_not_exist([output_dir])
for i_image, i_name in enumerate(image_names):