Spaces:
Running
on
Zero
Running
on
Zero
File size: 2,969 Bytes
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# coding: utf-8
import os
from glob import glob
import os.path as osp
import imageio
import numpy as np
import cv2; cv2.setNumThreads(0); cv2.ocl.setUseOpenCL(False)
def load_image_rgb(image_path: str):
if not osp.exists(image_path):
raise FileNotFoundError(f"Image not found: {image_path}")
img = cv2.imread(image_path, cv2.IMREAD_COLOR)
return cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
def load_driving_info(driving_info):
driving_video_ori = []
def load_images_from_directory(directory):
image_paths = sorted(glob(osp.join(directory, '*.png')) + glob(osp.join(directory, '*.jpg')))
return [load_image_rgb(im_path) for im_path in image_paths]
def load_images_from_video(file_path):
reader = imageio.get_reader(file_path)
return [image for idx, image in enumerate(reader)]
if osp.isdir(driving_info):
driving_video_ori = load_images_from_directory(driving_info)
elif osp.isfile(driving_info):
driving_video_ori = load_images_from_video(driving_info)
return driving_video_ori
def contiguous(obj):
if not obj.flags.c_contiguous:
obj = obj.copy(order="C")
return obj
def resize_to_limit(img: np.ndarray, max_dim=1920, n=2):
"""
ajust the size of the image so that the maximum dimension does not exceed max_dim, and the width and the height of the image are multiples of n.
:param img: the image to be processed.
:param max_dim: the maximum dimension constraint.
:param n: the number that needs to be multiples of.
:return: the adjusted image.
"""
h, w = img.shape[:2]
# ajust the size of the image according to the maximum dimension
if max_dim > 0 and max(h, w) > max_dim:
if h > w:
new_h = max_dim
new_w = int(w * (max_dim / h))
else:
new_w = max_dim
new_h = int(h * (max_dim / w))
img = cv2.resize(img, (new_w, new_h))
# ensure that the image dimensions are multiples of n
n = max(n, 1)
new_h = img.shape[0] - (img.shape[0] % n)
new_w = img.shape[1] - (img.shape[1] % n)
if new_h == 0 or new_w == 0:
# when the width or height is less than n, no need to process
return img
if new_h != img.shape[0] or new_w != img.shape[1]:
img = img[:new_h, :new_w]
return img
def load_img_online(obj, mode="bgr", **kwargs):
max_dim = kwargs.get("max_dim", 1920)
n = kwargs.get("n", 2)
if isinstance(obj, str):
if mode.lower() == "gray":
img = cv2.imread(obj, cv2.IMREAD_GRAYSCALE)
else:
img = cv2.imread(obj, cv2.IMREAD_COLOR)
else:
img = obj
# Resize image to satisfy constraints
img = resize_to_limit(img, max_dim=max_dim, n=n)
if mode.lower() == "bgr":
return contiguous(img)
elif mode.lower() == "rgb":
return contiguous(img[..., ::-1])
else:
raise Exception(f"Unknown mode {mode}")
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