File size: 2,861 Bytes
87ec96b aa6793a 6849ec3 87ec96b 3582bf4 b5da8a7 87ec96b aa6793a b5da8a7 6849ec3 87ec96b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 |
from PIL import Image, ImageChops
import requests
import os
import torch
import functools
import numpy as np
import cv2
from skimage.metrics import structural_similarity as compare_ssim
from moviepy.editor import VideoFileClip
from modules.utils.paths import *
TEST_IMAGE_URL = "https://github.com/microsoft/onnxjs-demo/raw/master/src/assets/EmotionSampleImages/sad_baby.jpg"
TEST_VIDEO_URL = "https://github.com/jhj0517/sample-medias/raw/master/vids/human-face/expression01_short.mp4"
TEST_IMAGE_PATH = os.path.normpath(os.path.join(PROJECT_ROOT_DIR, "tests", "test.png"))
TEST_VIDEO_PATH = os.path.normpath(os.path.join(PROJECT_ROOT_DIR, "tests", "test_expression.mp4"))
TEST_EXPRESSION_OUTPUT_PATH = os.path.normpath(os.path.join(PROJECT_ROOT_DIR, "tests", "edited_expression.png"))
TEST_EXPRESSION_AAA = 100
def download_image(url, path):
if os.path.exists(path):
return
response = requests.get(url, stream=True)
if response.status_code == 200:
with open(path, 'wb') as file:
for chunk in response.iter_content(1024):
file.write(chunk)
print(f"Image successfully downloaded to {path}")
else:
raise Exception(f"Failed to download image. Status code: {response.status_code}")
def are_images_different(image1_path: str, image2_path: str):
image1 = Image.open(image1_path)
image2 = Image.open(image2_path)
diff = ImageChops.difference(image1, image2)
if diff.getbbox() is None:
return False
else:
return True
def are_videos_different(video1_path: str, video2_path: str):
cap1 = cv2.VideoCapture(video1_path)
cap2 = cv2.VideoCapture(video2_path)
while True:
ret1, frame1 = cap1.read()
ret2, frame2 = cap2.read()
if not ret1 or not ret2:
if ret1 != ret2:
return True
break
if frame1.shape != frame2.shape:
frame1 = cv2.resize(frame1, (frame2.shape[1], frame2.shape[0]))
score, _ = compare_ssim(frame1, frame2, full=True, multichannel=True)
if score < 0.99:
return True
cap1.release()
cap2.release()
return False
def validate_video(video_path):
cap = cv2.VideoCapture(video_path)
if not cap.isOpened():
print("Could not open video file.")
return False
frame_count = 0
while True:
ret, frame = cap.read()
if not ret:
break
frame_count += 1
cap.release()
if frame_count == 0:
print("No frames found in video file.")
return False
return True
def has_sound(video_path: str):
try:
video = VideoFileClip(video_path)
return video.audio is not None
except Exception as e:
return False
@functools.lru_cache
def is_cuda_available():
return torch.cuda.is_available()
|