How to use get_patch_path method in dbt-osmosis

Best Python code snippet using dbt-osmosis_python

somo_utils.py

Source:somo_utils.py

1import os2from concepts.poisson import process3from PIL import Image4import numpy as np5def get_patch_path(target_path: str) -> str:6    """Returns path to a `patch` images in TCAV results folder structure base in concept path"""7    base_dir = "/".join(target_path.split('/')[:-2])8    concept_dir, file_name = target_path.split('/')[-2:]9    patch_dir = concept_dir + '_patches'10    return os.path.join(base_dir, patch_dir, file_name)11def get_img_path(target_path: str) -> str:12    """Returns path to a full image in TCAV results folder structure base in concept path"""13    base_dir = "/".join(target_path.split('/')[:-2])14    img_ind = str(int(target_path.split('/')[-1].split('_')[1][:-4]) + 1)15    file_name = (4 - len(img_ind)) * '0' + img_ind + '.png'16    return os.path.join(base_dir, 'images', file_name)17def load_img(path: str) -> np.ndarray:18    """Loads an image as numpy array"""19    return np.array(Image.open(path))20def get_concept_inds(img: np.ndarray) -> np.ndarray:21    """Returns a 2D array with rows of index pairs of concepts pixels (non-background)"""22    background = (117, 117, 117)23    return np.stack(np.where(np.all(img != background, axis=-1)), axis=1)24def diff(target: np.ndarray, source: np.ndarray) -> int:25    """Calculates the symmetric difference between two concepts based on overlapping non-background pixels"""26    target_ind = get_concept_inds(target)27    source_ind = get_concept_inds(source)28    nrows, ncols = target_ind.shape29    dtype = {'names': ['f{}'.format(i) for i in range(ncols)],30             'formats': ncols * [target_ind.dtype]}31    target_ind = target_ind.view(dtype)32    source_ind = source_ind.view(dtype)33    diff = len(np.setxor1d(target_ind, source_ind))34    return diff35def find_best_source(target: np.ndarray, source_label: str, data_dir: str, max_concepts=None) -> str:36    """37    Based on a diff function (above) searches for best match in all concepts of a given source label38    :param target: target concepts to change39    :param source_label: label from ImageNet, searches for candidates in its results40    :param data_dir: path to main results directory41    :param max_concepts: maximum number of concepts to search through, default None searches through all42    :return: path to best concept image found43    """44    source_concepts_dir = os.path.join(data_dir, f"{source_label}_4c_explained", "concepts")45    sources = []46    paths = []47    concepts_searched = 048    for concept_dir in filter(lambda x: not x.endswith('s'), os.listdir(source_concepts_dir)):49        concept_path = os.path.join(source_concepts_dir, concept_dir)50        for img_name in os.listdir(concept_path):51            img_path = os.path.join(concept_path, img_name)52            source = load_img(path=img_path)53            paths.append(img_path)54            sources.append(source)55        concepts_searched += 156        if max_concepts is not None and concepts_searched == max_concepts:57            break58    sources_arr = np.stack(sources)59    vec_diff = np.vectorize(diff, signature='(224,244,3),(224,224,3)->()')60    scores = vec_diff(target, sources_arr)61    source_path = paths[np.argmin(scores)]62    return source_path63# Poisson blending for Semantic Odd an Out64def blend(image: np.ndarray, target_patch: np.ndarray, source_patch: np.ndarray) -> np.ndarray:65    """Blends source patch into image overwriting the target patch"""66    target_inds = get_concept_inds(target_patch)67    source_inds = get_concept_inds(source_patch)68    target_min = target_inds.min(axis=0)69    target_max = target_inds.max(axis=0)70    target_size = target_max - target_min71    box = np.concatenate([source_inds.min(axis=0)[::-1], source_inds.max(axis=0)[::-1]])72    patch = np.array(Image.fromarray(source_patch).resize(size=target_size[::-1], box=box))73    canvas = 117 * np.ones_like(image)74    canvas[target_min[0]:target_max[0], target_min[1]:target_max[1]] = patch75    mask = np.zeros_like(canvas)[:,:,0]76    mask_inds = get_concept_inds(canvas)77    mask[mask_inds[:,0], mask_inds[:,1]] = 178    result = np.stack([process(canvas[:,:,i], image[:,:,i], mask) for i in range(3)], axis=-1)79    return np.clip(result, 0, 255)80def semantic_odd_man_out(target_concept_path: str, source_label: str, data_dir: str) -> np.ndarray:81    """Searches for a concept from source_label class to overwrite using Poisson blending"""82    target = load_img(target_concept_path)83    target_patch = load_img(get_patch_path(target_concept_path))84    image = load_img(get_img_path(target_concept_path))85    source_path = find_best_source(target=target, source_label=source_label, data_dir=data_dir)86    source_patch = load_img(get_patch_path(source_path))87    result = blend(image, target_patch, source_patch)...

app.py

Source:app.py

...32        # Empty T x W x H for each reference tracking point33        X = np.empty((N, 1, 25, 32, 32), np.float32)34        35        for i, point in enumerate(r0):36            im_p, _ = get_patch_path(imt, point, is_scaled=True)37            X[i] = im_p.copy()38        X = X - (X.mean(axis=0) / X.std(axis=0))39        batch_size = 840        N_batches = int(np.ceil(N / batch_size))41        _y1 = []42        with torch.no_grad():43            for i in range(N_batches):44                x = X[i * batch_size:(i + 1) * batch_size]45                x = torch.from_numpy(x).to(device)46                y_pred = model(x)47                _y1.append(y_pred.detach().cpu().numpy())48        y1: np.ndarray = np.vstack(_y1)49        y1 = y1.reshape(-1, 2, 25)50        y1 = y1 + r0[:, :, None]...

predict.py

Source:predict.py

...13    # Empty T x W x H for each reference tracking point14    X = np.empty((N, 1, 25, 32, 32), np.float32)15    16    for i, point in enumerate(r0):17        im_p, _ = get_patch_path(imt, point, is_scaled=True)18        X[i] = im_p.copy()19    X = X - (X.mean(axis=0) / X.std(axis=0))20    batch_size = 821    N_batches = int(np.ceil(N / batch_size))22    device = torch.device('cpu')23    model = load_model(MODEL_PATH, device=device)24    y1 = []25    with torch.no_grad():26        for i in tqdm(range(N_batches)):27            x = X[i * batch_size:(i + 1) * batch_size]28            x = torch.from_numpy(x).to(device)29            y_pred = model(x)30            y1.append(y_pred.detach().cpu().numpy())31    y1 = np.vstack(y1)...

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