semgent_from_folder.py

# import os
# import cv2
# import numpy as np
# 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
# from tqdm import tqdm


# model = create_model("Unet_2020-10-30")
# model.to("cuda")
# model.eval()

# input_dir = "../../image"
# output_dir = "../../seg_masks"

# os.makedirs(output_dir, exist_ok=True)

# for image_filename in tqdm(os.listdir(input_dir), colour="green"):
#     image_path = os.path.join(input_dir, image_filename)

#     image = cv2.imread(image_path)
#     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).to("cuda")

#     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)

#         output_image_path = os.path.join(output_dir, image_filename.replace(".jpg", ".png"))
#         cv2.imwrite(output_image_path, dst)

#     # break



import os
import cv2
import numpy as np
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
from tqdm import tqdm

# Create the model and wrap it with DataParallel
model = create_model("Unet_2020-10-30")
model = torch.nn.DataParallel(model)

# Move the model to CUDA devices
model.to("cuda")
model.eval()

input_dir = "../../image"
output_dir = "../../seg_masks"

os.makedirs(output_dir, exist_ok=True)

for image_filename in tqdm(os.listdir(input_dir), colour="green"):
    image_path = os.path.join(input_dir, image_filename)

    image = cv2.imread(image_path)
    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).to("cuda")

    with torch.no_grad():
        # Use DataParallel to perform inference on all 4 GPUs
        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)

        output_image_path = os.path.join(output_dir, image_filename.replace(".jpg", ".png"))
        cv2.imwrite(output_image_path, dst)

    # break