| import numpy as np |
| import cv2 |
| import torch |
| import albumentations as albu |
| from iglovikov_helper_functions.utils.image_utils import load_rgb, pad, unpad |
| from iglovikov_helper_functions.dl.pytorch.utils import tensor_from_rgb_image |
|
|
| from cloths_segmentation.pre_trained_models import create_model |
| model = create_model("Unet_2020-10-30") |
| model.eval() |
|
|
| image = cv2.imread(str(r"test.jpg")) |
| image_2_extract = image |
| image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) |
| transform = albu.Compose([albu.Normalize(p=1)], p=1) |
| padded_image, pads = pad(image, factor=32, border=cv2.BORDER_CONSTANT) |
| x = transform(image=padded_image)["image"] |
| x = torch.unsqueeze(tensor_from_rgb_image(x), 0) |
|
|
| with torch.no_grad(): |
| prediction = model(x)[0][0] |
| mask = (prediction > 0).cpu().numpy().astype(np.uint8) |
| mask = unpad(mask, pads) |
| rmask = (cv2.cvtColor(mask, cv2.COLOR_BGR2RGB) * 255).astype(np.uint8) |
| mask2 = np.where((rmask < 255), 0, 1).astype('uint8') |
| image_2_extract = image_2_extract * mask2[:, :, 1, np.newaxis] |
|
|
| tmp = cv2.cvtColor(image_2_extract, cv2.COLOR_BGR2GRAY) |
| _, alpha = cv2.threshold(tmp, 0, 255, cv2.THRESH_BINARY) |
| b, g, r = cv2.split(image_2_extract) |
| rgba = [b, g, r, alpha] |
| dst = cv2.merge(rgba, 4) |
| cv2.imwrite("test.png", dst) |
| |
|
|
