def load_data(split='train'):
data = dict()
for shape in shapes_type:
path = shapes_dataset_path + '/' + str(split) + '/' + str(shape) + '/'
imgs = find_files(path)
x = load_images(imgs, path)
y = [shapes_label[shape]] * len(x)
data[shape] = (x, y)
return data
def run(self, data_path, video=False):
data = find_files(data_path, sorted_by_idx=False)
if video:
media = load_videos(data, data_path)
else:
media = load_images(data, data_path)
if video:
print(len(media), len(media[0]))
size = self.model_specs['size']
omega = self.model_specs['omega']
cropper = CropImage(size)
for instance in media:
instance = rgb2grayscale(instance)
plt.imshow(instance, cmap='gray')
plt.title('Input')
plt.show()
original_crops = cropper.crop(instance)
lg_filter = laguerre_gauss_filter(size, omega)
ft_lg_filter = np.fft.fft2(lg_filter)
x_profile, y_profile = ft_pipeline(ft_lg_filter, original_crops)
ft_crops = np.abs(np.concatenate((x_profile, y_profile), axis=1))
if not self.is_joblib:
predictions = self.model.predict_on_batch(ft_crops)
else:
predictions = self.model.predict(ft_crops)
print(predictions)
pos_preds = []
for i in range(len(predictions)):
if not self.is_joblib:
pred = np.argmax(predictions[i])
else:
pred = predictions[i]
if pred == 0:
continue
# img = original_crops[i]
# plt.imshow(img, cmap='gray')
# plt.title('Prediction: ' + str(pred))
# plt.show()
# plt.pause()
# plt.close()
pos_preds.append(original_crops[i])
print(len(pos_preds))
n_row, n_col = 2, 3
_, axs = plt.subplots(n_row, n_col, figsize=(12, 12))
axs = axs.flatten()
plt.suptitle('Positive Predictions kNN')
for img, ax in zip(pos_preds, axs):
ax.imshow(img, cmap='gray')
plt.show()
def __init__(self, path):
self.path = path
images_path = find_files(self.path)
self.images_path = shuffle(images_path)
for img in images:
resized.append(resize(img, dim))
return resized
if __name__ == '__main__':
cropper = CropImage(64)
path = '/home/alejandro/Documents/Universidad/Semestre 6/PI1/Datasets/Mineria/samples/'
save_path = '/home/alejandro/Documents/Universidad/Semestre 6/PI1/Datasets/Mineria/crop_64x64_samples/'
cropper = CropImage(64)
imgs = find_files(path)
print('found_images')
images = load_images(imgs, path)
print('loaded_images')
count_0 = 0
count_1 = 0
for i in range(len(imgs)):
image = rgb2grayscale(images[i])
crops = cropper.crop(image)
if i % 100 == 0:
print(i)
for c in crops:
img_class = imgs[i][0]
if img_class == 0:
class_count = count_0