python : Updated code with better display, documentation and format_time
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saundersp
@ -18,10 +18,10 @@ def set_integral_image(X: np.ndarray) -> np.ndarray:
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"""Transform the input images in integrated images (CPU version).
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Args:
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X (np.ndarray): Dataset of images.
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X (np.ndarray): Dataset of images
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Returns:
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np.ndarray: Dataset of integrated images.
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np.ndarray: Dataset of integrated images
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"""
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X_ii = np.empty_like(X, dtype = np.uint32)
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for i, Xi in enumerate(tqdm_iter(X, "Applying integral image")):
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@ -34,59 +34,18 @@ def set_integral_image(X: np.ndarray) -> np.ndarray:
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X_ii[i] = ii
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return X_ii
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@njit('uint32(uint32[:, :], int16, int16, int16, int16)')
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def __compute_feature__(ii: np.ndarray, x: int, y: int, w: int, h: int) -> int:
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"""Compute a feature on an integrated image at a specific coordinate (CPU version).
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Args:
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ii (np.ndarray): Integrated image.
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x (int): X coordinate.
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y (int): Y coordinate.
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w (int): width of the feature.
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h (int): height of the feature.
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Returns:
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int: Computed feature.
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"""
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return ii[y + h, x + w] + ii[y, x] - ii[y + h, x] - ii[y, x + w]
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@njit('int32[:, :](uint8[:, :, :, :], uint32[:, :, :])')
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def apply_features(feats: np.ndarray, X_ii: np.ndarray) -> np.ndarray:
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"""Apply the features on a integrated image dataset (CPU version).
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Args:
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feats (np.ndarray): Features to apply.
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X_ii (np.ndarray): Integrated image dataset.
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Returns:
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np.ndarray: Applied features.
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"""
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X_feat = np.empty((feats.shape[0], X_ii.shape[0]), dtype = np.int32)
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for i, (p, n) in enumerate(tqdm_iter(feats, "Applying features")):
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for j, x_i in enumerate(X_ii):
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p_x, p_y, p_w, p_h = p[0]
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p1_x, p1_y, p1_w, p1_h = p[1]
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n_x, n_y, n_w, n_h = n[0]
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n1_x, n1_y, n1_w, n1_h = n[1]
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p1 = __compute_feature__(x_i, p_x, p_y, p_w, p_h) + __compute_feature__(x_i, p1_x, p1_y, p1_w, p1_h)
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n1 = __compute_feature__(x_i, n_x, n_y, n_w, n_h) + __compute_feature__(x_i, n1_x, n1_y, n1_w, n1_h)
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X_feat[i, j] = int32(p1) - int32(n1)
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return X_feat
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@njit('int32[:, :](int32[:, :], uint16[:, :], uint8[:], float64[:])')
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def train_weak_clf(X_feat: np.ndarray, X_feat_argsort: np.ndarray, y: np.ndarray, weights: np.ndarray) -> np.ndarray:
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"""Train the weak classifiers on a given dataset (CPU version).
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Args:
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X_feat (np.ndarray): Feature images dataset.
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X_feat_argsort (np.ndarray): Sorted indexes of the integrated features.
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y (np.ndarray): Labels of the features.
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weights (np.ndarray): Weights of the features.
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X_feat (np.ndarray): Feature images dataset
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X_feat_argsort (np.ndarray): Sorted indexes of the integrated features
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y (np.ndarray): Labels of the features
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weights (np.ndarray): Weights of the features
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Returns:
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np.ndarray: Trained weak classifiers.
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np.ndarray: Trained weak classifiers
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"""
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total_pos, total_neg = weights[y == 1].sum(), weights[y == 0].sum()
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@ -112,29 +71,85 @@ def train_weak_clf(X_feat: np.ndarray, X_feat_argsort: np.ndarray, y: np.ndarray
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classifiers[i] = (best_threshold, best_polarity)
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return classifiers
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@njit('uint32(uint32[:, :], int16, int16, int16, int16)')
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def __compute_feature__(ii: np.ndarray, x: int, y: int, w: int, h: int) -> int:
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"""Compute a feature on an integrated image at a specific coordinate (CPU version).
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Args:
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ii (np.ndarray): Integrated image
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x (int): X coordinate
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y (int): Y coordinate
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w (int): width of the feature
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h (int): height of the feature
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Returns:
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int: Computed feature
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"""
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return ii[y + h, x + w] + ii[y, x] - ii[y + h, x] - ii[y, x + w]
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@njit('int32[:, :](uint8[:, :, :, :], uint32[:, :, :])')
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def apply_features(feats: np.ndarray, X_ii: np.ndarray) -> np.ndarray:
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"""Apply the features on a integrated image dataset (CPU version).
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Args:
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feats (np.ndarray): Features to apply
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X_ii (np.ndarray): Integrated image dataset
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Returns:
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np.ndarray: Applied features
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"""
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X_feat = np.empty((feats.shape[0], X_ii.shape[0]), dtype = np.int32)
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for i, (p, n) in enumerate(tqdm_iter(feats, "Applying features")):
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for j, x_i in enumerate(X_ii):
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p_x, p_y, p_w, p_h = p[0]
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p1_x, p1_y, p1_w, p1_h = p[1]
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n_x, n_y, n_w, n_h = n[0]
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n1_x, n1_y, n1_w, n1_h = n[1]
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p1 = __compute_feature__(x_i, p_x, p_y, p_w, p_h) + __compute_feature__(x_i, p1_x, p1_y, p1_w, p1_h)
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n1 = __compute_feature__(x_i, n_x, n_y, n_w, n_h) + __compute_feature__(x_i, n1_x, n1_y, n1_w, n1_h)
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X_feat[i, j] = int32(p1) - int32(n1)
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return X_feat
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@njit('int32(int32[:], uint16[:], int32, int32)')
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def as_partition(a: np.ndarray, indices: np.ndarray, l: int, h: int) -> int:
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i = l - 1
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j = l
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for j in range(l, h + 1):
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if a[indices[j]] < a[indices[h]]:
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def _as_partition_(d_a: np.ndarray, d_indices: np.ndarray, low: int, high: int) -> int:
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"""Partition of the argsort algorithm.
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Args:
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d_a (np.ndarray): Array on device to sort
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d_indices (np.ndarray): Array of indices on device to write to
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low (int): lower bound to sort
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high (int): higher bound to sort
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Returns:
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int: Last index sorted
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"""
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i, j = low - 1, low
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for j in range(low, high + 1):
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if d_a[d_indices[j]] < d_a[d_indices[high]]:
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i += 1
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indices[i], indices[j] = indices[j], indices[i]
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d_indices[i], d_indices[j] = d_indices[j], d_indices[i]
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i += 1
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indices[i], indices[j] = indices[j], indices[i]
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d_indices[i], d_indices[j] = d_indices[j], d_indices[i]
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return i
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@njit('void(int32[:], uint16[:], int32, int32)')
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def argsort_bounded(a: np.ndarray, indices: np.ndarray, l: int, h: int):
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total = h - l + 1;
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stack = np.empty((total,), dtype = np.int32)
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stack[0] = l
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stack[1] = h
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top = 1;
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def argsort_bounded(d_a: np.ndarray, d_indices: np.ndarray, low: int, high: int) -> None:
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"""Perform an indirect sort of a given array within a given bound.
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low = l
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high = h
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Args:
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d_a (np.ndarray): Array to sort
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d_indices (np.ndarray): Array of indices to write to
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low (int): lower bound to sort
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high (int): higher bound to sort
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"""
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total = high - low + 1
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stack = np.empty((total,), dtype = np.int32)
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stack[0] = low
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stack[1] = high
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top = 1
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while top >= 0:
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high = stack[top]
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@ -143,24 +158,32 @@ def argsort_bounded(a: np.ndarray, indices: np.ndarray, l: int, h: int):
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top -= 1
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if low >= high:
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break;
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break
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p = as_partition(a, indices, low, high);
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p = _as_partition_(d_a, d_indices, low, high)
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if p - 1 > low:
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top += 1
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stack[top] = low;
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stack[top] = low
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top += 1
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stack[top] = p - 1;
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stack[top] = p - 1
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if p + 1 < high:
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top += 1
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stack[top] = p + 1;
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stack[top] = p + 1
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top += 1
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stack[top] = high;
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stack[top] = high
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@njit('uint16[:, :](int32[:, :])')
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def argsort(X_feat: np.ndarray) -> np.ndarray:
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"""Perform an indirect sort of a given array.
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Args:
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X_feat (np.ndarray): Array to sort
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Returns:
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np.ndarray: Array of indices that sort the array
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"""
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indices = np.empty_like(X_feat, dtype = np.uint16)
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indices[:, :] = np.arange(indices.shape[1])
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for i in tqdm_iter(range(X_feat.shape[0]), "argsort"):
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