def predict_poly(model, threashold1, threashold2, result, first_class):
predicted_mask = extra_functions.make_prediction_cropped(
model,
image,
initial_size=(112, 112),
final_size=(112 - 32, 112 - 32),
num_masks=num_mask_channels,
num_channels=num_channels)
image_v = np.flipud(image)
predicted_mask_v = extra_functions.make_prediction_cropped(
model,
image_v,
initial_size=(112, 112),
final_size=(112 - 32, 112 - 32),
num_masks=2,
num_channels=num_channels)
image_h = np.fliplr(image)
predicted_mask_h = extra_functions.make_prediction_cropped(
model,
image_h,
initial_size=(112, 112),
final_size=(112 - 32, 112 - 32),
num_masks=2,
num_channels=num_channels)
image_s = np.rot90(image)
predicted_mask_s = extra_functions.make_prediction_cropped(
model,
image_s,
initial_size=(112, 112),
final_size=(112 - 32, 112 - 32),
num_masks=2,
num_channels=num_channels)
new_mask = np.power(
predicted_mask * np.flipud(predicted_mask_v) *
np.fliplr(predicted_mask_h) * np.rot90(predicted_mask_s, 3), 0.25)
x_scaler, y_scaler = extra_functions.get_scalers(H, W, x_max, y_min)
mask_channel = first_class
result += [(image_id, mask_channel + 1,
mask2poly(new_mask[:, :, 0], threashold1, x_scaler, y_scaler))]
mask_channel = first_class + 1
result += [(image_id, mask_channel + 1,
mask2poly(new_mask[:, :, 1], threashold2, x_scaler, y_scaler))]
return result
for image_id in tqdm(test_ids):
image = extra_functions.read_image_16(image_id)
file_name = '{}.tif'.format(image_id)
image_3 = tiff.imread(os.path.join(three_band_path, file_name))
image_3 = np.transpose(image_3, (1, 2, 0))
image_3 = image_3 / 2047 * 255
image_3 = np.array(image_3, dtype=np.uint8)
H = image.shape[1]
W = image.shape[2]
x_max, y_min = extra_functions._get_xmax_ymin(image_id)
predicted_mask = extra_functions.make_prediction_cropped(
model,
image,
initial_size=(128, 128),
final_size=(128 - 32, 128 - 32),
num_masks=num_mask_channels,
num_channels=num_channels)
mask_to_draw = np.zeros((H, W, 3), np.uint8)
for i in range(num_mask_channels):
mask_to_draw[predicted_mask[i] >= threshold] = color[i + 1]
#mask_to_draw[predicted_mask[i]<threshold] = (255,188,64)
image_mask = cv2.addWeighted(image_3, 0.6, mask_to_draw, 0.4, 0)
imwrite('../test_mask/{}_mask.png'.format(image_id), mask_to_draw)
imwrite('../test_mask/{}image.png'.format(image_id), image_3)
imwrite('../test_mask/{}_mask_image.png'.format(image_id), image_mask)
#vivian added
test_ids = test_ids[200:220]
#vivian added
for image_id in tqdm(test_ids):
image = extra_functions.read_image_16(image_id)
H = image.shape[1]
W = image.shape[2]
x_max, y_min = extra_functions._get_xmax_ymin(image_id)
predicted_mask = extra_functions.make_prediction_cropped(
model,
image,
initial_size=(112, 112),
final_size=(112 - 32, 112 - 32),
num_masks=num_mask_channels,
num_channels=num_channels)
image_v = flip_axis(image, 1)
predicted_mask_v = extra_functions.make_prediction_cropped(
model,
image_v,
initial_size=(112, 112),
final_size=(112 - 32, 112 - 32),
num_masks=1,
num_channels=num_channels)
image_h = flip_axis(image, 2)
predicted_mask_h = extra_functions.make_prediction_cropped(
continue
# print(img_3.max())
# print(img_3.min())
# # 读取自定义训练图片
# image = np.transpose(plt.imread("../data/image_tiles{}.tif".format(image_id)), (2, 0, 1)) / 2047.0
# image=image.astype(np.float16)
# image=np.transpose(cv2.imread("../data/image_file_test/{}".format(file_name)), (2, 0, 1)) / 2047.0
# image=image.astype(np.float16)
H = image.shape[1]
W = image.shape[2]
# 预测图片
predicted_mask = extra_functions.make_prediction_cropped(
model,
image,
initial_size=(image_row, image_col),
final_size=(image_row - 32, image_col - 32),
num_masks=num_mask_channels,
num_channels=num_channels)
# 将图片水平翻转然后预测
image_v = flip_axis(image, 1)
predicted_mask_v = extra_functions.make_prediction_cropped(
model,
image_v,
initial_size=(image_row, image_col),
final_size=(image_row - 32, image_col - 32),
num_masks=1,
num_channels=num_channels)
# 将图片竖直翻转然后预测
image_h = flip_axis(image, 2)
predicted_mask_h = extra_functions.make_prediction_cropped(
test_ids.append(i)
print("Number of images: ",len(test_ids))
result = []
def flip_axis(x, axis):
x = np.asarray(x).swapaxes(axis, 0)
x = x[::-1, ...]
x = x.swapaxes(0, axis)
return x
for image_id in test_ids:
print("Predicting: ", image_id)
image = extra_functions.read_image_test(image_id)
predicted_mask = extra_functions.make_prediction_cropped(model, image, batch_size, size=(288, 288))
image_v = flip_axis(image, 0)
predicted_mask_v = extra_functions.make_prediction_cropped(model, image_v,batch_size, size=(288,288))
image_h = flip_axis(image, 1)
predicted_mask_h = extra_functions.make_prediction_cropped(model, image_h,batch_size, size=(288,288))
image_s = image.swapaxes(0, 1)
predicted_mask_s = extra_functions.make_prediction_cropped(model, image_s,batch_size, size=(288,288))
new_mask = np.power(predicted_mask *
flip_axis(predicted_mask_v, 0) *
flip_axis(predicted_mask_h, 1) *
predicted_mask_s.swapaxes(0, 1), 0.25)
new_mask[new_mask >= threshold] = 1;
new_mask[new_mask < threshold] = 0;
print(
"<><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>"
)
print("predict image id: ", image_id)
print(
"<><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>"
)
image = extra_functions.read_image_16(test_path, collect_name,
image_id,
normalization_value[index])
predicted_mask = extra_functions.make_prediction_cropped(
model,
image,
initial_size=(patch_width, patch_height),
final_size=(patch_width - 32, patch_height - 32),
num_masks=1,
num_channels=channels)
image_v = flip_axis(image, 1)
predicted_mask_v = extra_functions.make_prediction_cropped(
model,
image_v,
initial_size=(patch_width, patch_height),
final_size=(patch_width - 32, patch_height - 32),
num_masks=1,
num_channels=channels)
image_h = flip_axis(image, 2)
predicted_mask_h = extra_functions.make_prediction_cropped(