def fill(self) -> None:
"""
Populate StackedWidget with thumbnails
from THUMBS_DIR
"""
for image in list_images(THUMBS_DIR):
self.add(image)
def run():
for fname in hlp.list_images('cropped'):
im = cv2.imread(fname)
if im == None: continue
faces = detect(im)
for (x, y, w, h) in faces:
cv2.rectangle(im, (x, y),
(x + w, y + h), (255, 0, 0), 3)
imshow(im)
import cv2
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-d", "--dataset", required=True,
help="Path to the directory that contains the images to be indexed")
ap.add_argument("-i", "--index", required=True,
help="Path to where the features index will be stored")
args = vars(ap.parse_args())
# initialize the color descriptor and open the output index file for writing
desc = ResNetDescriptor()
output = open(args["index"], "w")
# grab the list of image paths and initialize the progress bar
imagePaths = list(helpers.list_images(args["dataset"]))
widgets = ["Indexing: ", progressbar.Percentage(), " ", progressbar.Bar(), " ", progressbar.ETA()]
pbar = progressbar.ProgressBar(maxval=len(imagePaths), widgets=widgets)
pbar.start()
# loop over the image paths in the dataset directory
for (i, imagePath) in enumerate(sorted(imagePaths)):
# extract the image filename (i.e. the unique image ID) from the image
# path, then load the image itself
filename = imagePath[imagePath.rfind("/") + 1:]
image = helpers.image_preprocessor(imagePath)
# describe the image
features = desc.describe(image)
# write the features to our index file
def load_data(path):
images = (normalize_image(cv2.imread(fname)) for fname in list_images(path))
X = np.vstack([im.flatten() for im in images])
return X
