trying to improve whole project

1 files changed, 0 insertions, 108 deletions

diff --git a/utils.py b/utils.py
deleted file mode 100644
index 9935d16..0000000
--- a/utils.py
+++ /dev/null
@@ -1,108 +0,0 @@
-import numpy as np
-import scipy.linalg
-import cv2
-
-
-def fid(reals, fakes):
-    """FID score calculation.
-
-    Args:
-        reals (numpy.array): Real images.
-        fakes (numpy.array): Fake images.
-    """
-    reals = reals.reshape(reals.shape[0], -1)
-    fakes = fakes.reshape(fakes.shape[0], -1)
-
-    mu_real = np.mean(reals, axis=0)
-    mu_fake = np.mean(fakes, axis=0)
-    sigma_real = np.cov(reals, rowvar=False)
-    sigma_fake = np.cov(fakes, rowvar=False)
-
-    diff = mu_real - mu_fake
-    covmean = np.dot(sigma_real, sigma_fake.T)
-    covmean, _ = scipy.linalg.sqrtm(sigma_real.dot(sigma_fake), disp=False)
-
-    if not np.isfinite(covmean).all():
-        eps=1e-6
-        offset = np.eye(sigma_real.shape[0]) * eps
-        ncovmean = scipy.linalg.sqrtm((sigma_real + offset).dot(sigma_fake + offset))
-        covmean = ncovmean
-
-    if np.iscomplexobj(covmean):
-        covmean = covmean.real
-
-    return diff @ diff + np.trace(sigma_real) + np.trace(sigma_fake) - 2 * np.trace(covmean)
-
-
-def kl(reals, fakes):
-    """KL divergence calculation.
-
-    Args:
-        reals (numpy.array): Real images.
-        fakes (numpy.array): Fake images.
-    """
-    reals = reals.transpose(1, 0, 2, 3).reshape(reals.shape[1], -1)
-    fakes = fakes.transpose(1, 0, 2, 3).reshape(fakes.shape[1], -1)
-
-    hist_real = np.apply_along_axis(lambda a: np.histogram(a, bins=40, range=(-1, 1))[0], 1, reals)
-    hist_fake = np.apply_along_axis(lambda a: np.histogram(a, bins=40, range=(-1, 1))[0], 1, fakes)
-
-    hist_real = hist_real + 1
-    hist_fake = hist_fake + 1
-
-    hist_real = hist_real / np.sum(hist_real)
-    hist_fake = hist_fake / np.sum(hist_fake)
-
-    return np.mean(np.log(hist_real / hist_fake))
-
-
-def jsd(reals, fakes):
-    """Jensen-Shannon divergence calculation.
-
-    Args:
-        reals (numpy.array): Real images.
-        fakes (numpy.array): Fake images.
-    """
-    reals = reals.transpose(1, 0, 2, 3).reshape(reals.shape[1], -1)
-    fakes = fakes.transpose(1, 0, 2, 3).reshape(fakes.shape[1], -1)
-
-    hist_real = np.apply_along_axis(lambda a: np.histogram(a, bins=40, range=(-1, 1))[0], 1, reals)
-    hist_fake = np.apply_along_axis(lambda a: np.histogram(a, bins=40, range=(-1, 1))[0], 1, fakes)
-
-    hist_real = hist_real + 1
-    hist_fake = hist_fake + 1
-
-    hist_real = hist_real / np.sum(hist_real)
-    hist_fake = hist_fake / np.sum(hist_fake)
-
-    hist_avg = (hist_real + hist_fake) / 2
-
-    return 0.5 * (np.mean(np.log(hist_real / hist_avg)) + np.mean(np.log(hist_fake / hist_avg)))
-
-
-def haar(image):
-    # Load the Haar cascade for face detection
-    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
-
-    # Convert the image to grayscale
-    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-
-    # Perform face detection
-    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=4)
-
-    # Draw rectangles around the detected faces
-    for (x, y, w, h) in faces:
-        cv2.rectangle(image, (x, y), (x+w, y+h), (255, 0, 0), 2)
-
-    # Display the result
-    cv2.imshow('Face Detection', image)
-    cv2.waitKey(0)
-    cv2.destroyAllWindows()
-
-
-if __name__ == '__main__':
-    # Load the image
-    image = cv2.imread('data/edface/500/03120500_000.png')
-
-    # Perform face detection
-    haar(image)
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