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python : improved documentation

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saundersp
2024-04-28 22:35:42 +02:00
parent c71b04f00d
commit 4a42747837
5 changed files with 78 additions and 77 deletions
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48
python/ViolaJones.py
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@ -18,7 +18,7 @@ else:
@njit('uint8[:, :, :, :](uint16, uint16)')
def build_features(width: int, height: int) -> np.ndarray:
"""Initialize the features base on the input shape.
"""Initialize the features based on the input shape.
Args:
shape (Tuple[int, int]): Shape of the image (Width, Height)
@ -90,9 +90,31 @@ def classify_weak_clf(x_feat_i: np.ndarray, threshold: int, polarity: int) -> np
res[polarity * x_feat_i < polarity * threshold] = 1
return res
@njit('uint8[:](float64[:], int32[:, :], int32[:, :])')
def classify_viola_jones(alphas: np.ndarray, classifiers: np.ndarray, X_feat: np.ndarray) -> np.ndarray:
"""Classify the trained classifiers on the given features.
Args:
alphas (np.ndarray): Trained alphas
classifiers (np.ndarray): Trained classifiers
X_feat (np.ndarray): Integrated features
Returns:
np.ndarray: Classification results
"""
total = np.zeros(X_feat.shape[1], dtype = np.float64)
for i, alpha in enumerate(tqdm_iter(alphas, "Classifying ViolaJones")):
(j, threshold, polarity) = classifiers[i]
total += alpha * classify_weak_clf(X_feat[j], threshold, polarity)
y_pred = np.zeros(X_feat.shape[1], dtype = np.uint8)
y_pred[total >= 0.5 * np.sum(alphas)] = 1
return y_pred
@njit('Tuple((int32, float64, float64[:]))(int32[:, :], float64[:], int32[:, :], uint8[:])')
def select_best(classifiers: np.ndarray, weights: np.ndarray, X_feat: np.ndarray, y: np.ndarray) -> Tuple[int, float, np.ndarray]:
"""Select the best classifier given theirs predictions.
"""Select the best classifier given their predictions.
Args:
classifiers (np.ndarray): The weak classifiers
@ -139,28 +161,6 @@ def train_viola_jones(T: int, X_feat: np.ndarray, X_feat_argsort: np.ndarray, y:
return alphas, final_classifier
@njit('uint8[:](float64[:], int32[:, :], int32[:, :])')
def classify_viola_jones(alphas: np.ndarray, classifiers: np.ndarray, X_feat: np.ndarray) -> np.ndarray:
"""Classify the trained classifiers on the given features.
Args:
alphas (np.ndarray): Trained alphas
classifiers (np.ndarray): Trained classifiers
X_feat (np.ndarray): Integrated features
Returns:
np.ndarray: Classification results
"""
total = np.zeros(X_feat.shape[1], dtype = np.float64)
for i, alpha in enumerate(tqdm_iter(alphas, "Classifying ViolaJones")):
(j, threshold, polarity) = classifiers[i]
total += alpha * classify_weak_clf(X_feat[j], threshold, polarity)
y_pred = np.zeros(X_feat.shape[1], dtype = np.uint8)
y_pred[total >= 0.5 * np.sum(alphas)] = 1
return y_pred
#@njit
#def get_best_anova_features(X: np.ndarray, y: np.ndarray) -> np.ndarray:
# #SelectPercentile(f_classif, percentile = 10).fit(X, y).get_support(indices = True)