def LoadDataset(filename, imagepath): file_list = np.genfromtxt(filename, dtype=np.dtype([('Filename','S21'), ('Width', int), ('Height', int), ('roi_x1', int), ('roi_y1', int), ('roi_x2', int), ('roi_y2', int), ('classId', int)])) ImageProcProxy.createCLAHE(clipLimit=1.4, tileGridSize=(2,2)) input_images = ImageProcProxy.emptyDataset(file_list.shape[0], params['input']['width'], params['input']['height']) for sample in xrange(file_list.shape[0]): image_file = ImageProcProxy.readImageColor(imagepath + file_list['Filename'][sample]) image_roi = ImageProcProxy.cropImage(image_file, file_list['roi_x1'][sample], file_list['roi_y1'][sample], file_list['roi_x2'][sample], file_list['roi_y2'][sample]) image_clahe = ImageProcProxy.emptyLike(image_roi) image_clahe[:,:,0] = ImageProcProxy.applyCLAHE(image_roi[:,:,0]) image_clahe[:,:,1] = ImageProcProxy.applyCLAHE(image_roi[:,:,1]) image_clahe[:,:,2] = ImageProcProxy.applyCLAHE(image_roi[:,:,2]) image_resized = ImageProcProxy.resizeInterLinear(image_clahe, params['input']['width'], params['input']['height']) image_gaussian = ImageProcProxy.applyGaussian(image_resized, params['filter']['gaussian_radius'], params['filter']['gaussian_sigma']) image_int = ImageProcProxy.convertBGR2INT(image_gaussian) image_vector = ImageProcProxy.flattenImage(image_int) input_images[sample]= image_vector return input_images, file_list['classId']
def LoadDataset(filename, imagepath): file_list = np.genfromtxt(filename, dtype=np.dtype([('Filename', 'S21'), ('Width', int), ('Height', int), ('roi_x1', int), ('roi_y1', int), ('roi_x2', int), ('roi_y2', int), ('classId', int)])) ImageProcProxy.createCLAHE(clipLimit=1.4, tileGridSize=(2, 2)) input_images = ImageProcProxy.emptyDataset(file_list.shape[0], params['input']['width'], params['input']['height']) for sample in xrange(file_list.shape[0]): image_file = ImageProcProxy.readImageColor( imagepath + file_list['Filename'][sample]) image_roi = ImageProcProxy.cropImage(image_file, file_list['roi_x1'][sample], file_list['roi_y1'][sample], file_list['roi_x2'][sample], file_list['roi_y2'][sample]) image_clahe = ImageProcProxy.emptyLike(image_roi) image_clahe[:, :, 0] = ImageProcProxy.applyCLAHE(image_roi[:, :, 0]) image_clahe[:, :, 1] = ImageProcProxy.applyCLAHE(image_roi[:, :, 1]) image_clahe[:, :, 2] = ImageProcProxy.applyCLAHE(image_roi[:, :, 2]) image_resized = ImageProcProxy.resizeInterLinear( image_clahe, params['input']['width'], params['input']['height']) image_gaussian = ImageProcProxy.applyGaussian( image_resized, params['filter']['gaussian_radius'], params['filter']['gaussian_sigma']) image_int = ImageProcProxy.convertBGR2INT(image_gaussian) image_vector = ImageProcProxy.flattenImage(image_int) input_images[sample] = image_vector return input_images, file_list['classId']
def LoadDataset(imagefile, labelfile, finalsize): loaded = np.fromfile(file=open(imagefile),dtype=np.uint8) images = loaded[16:].reshape((-1,28,28)) loaded = np.fromfile(file=open(labelfile),dtype=np.uint8) labels = loaded[8:].reshape((images.shape[0])) input_images = ImageProcProxy.emptyDataset(images.shape[0], params['input']['width'], params['input']['height']) for sample in xrange(images.shape[0]): image_resized = ImageProcProxy.resizeInterLinear(images[sample], params['input']['width'], params['input']['height']) image_gaussian = ImageProcProxy.applyGaussian(image_resized, params['filter']['gaussian_radius'], params['filter']['gaussian_sigma']) image_vector = ImageProcProxy.flattenImage(image_gaussian) input_images[sample]= image_vector return input_images[:finalsize], labels[:finalsize]
def LoadDataset(imagefile, labelfile, finalsize): loaded = np.fromfile(file=open(imagefile), dtype=np.uint8) images = loaded[16:].reshape((-1, 28, 28)) loaded = np.fromfile(file=open(labelfile), dtype=np.uint8) labels = loaded[8:].reshape((images.shape[0])) input_images = ImageProcProxy.emptyDataset(images.shape[0], params['input']['width'], params['input']['height']) for sample in xrange(images.shape[0]): image_resized = ImageProcProxy.resizeInterLinear( images[sample], params['input']['width'], params['input']['height']) image_gaussian = ImageProcProxy.applyGaussian( image_resized, params['filter']['gaussian_radius'], params['filter']['gaussian_sigma']) image_vector = ImageProcProxy.flattenImage(image_gaussian) input_images[sample] = image_vector return input_images[:finalsize], labels[:finalsize]