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import numpy as np
import torch
from tqdm import tqdm
import cv2
def ssim(img1, img2):
C1 = 0.01 ** 2
C2 = 0.03 ** 2
img1 = img1.astype(np.float64)
img2 = img2.astype(np.float64)
kernel = cv2.getGaussianKernel(11, 1.5)
window = np.outer(kernel, kernel.transpose())
mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5] # valid
mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
mu1_sq = mu1 ** 2
mu2_sq = mu2 ** 2
mu1_mu2 = mu1 * mu2
sigma1_sq = cv2.filter2D(img1 ** 2, -1, window)[5:-5, 5:-5] - mu1_sq
sigma2_sq = cv2.filter2D(img2 ** 2, -1, window)[5:-5, 5:-5] - mu2_sq
sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *
(sigma1_sq + sigma2_sq + C2))
return ssim_map.mean()
def calculate_ssim_function(img1, img2):
# [0,1]
# ssim is the only metric extremely sensitive to gray being compared to b/w
if not img1.shape == img2.shape:
raise ValueError('Input images must have the same dimensions.')
if img1.ndim == 2:
return ssim(img1, img2)
elif img1.ndim == 3:
if img1.shape[0] == 3:
ssims = []
for i in range(3):
ssims.append(ssim(img1[i], img2[i]))
return np.array(ssims).mean()
elif img1.shape[0] == 1:
return ssim(np.squeeze(img1), np.squeeze(img2))
else:
raise ValueError('Wrong input image dimensions.')
def trans(x):
return x
def calculate_ssim(videos1, videos2):
print("calculate_ssim...")
# videos [batch_size, timestamps, channel, h, w]
assert videos1.shape == videos2.shape
videos1 = trans(videos1)
videos2 = trans(videos2)
ssim_results = []
for video_num in tqdm(range(videos1.shape[0])):
# get a video
# video [timestamps, channel, h, w]
video1 = videos1[video_num]
video2 = videos2[video_num]
ssim_results_of_a_video = []
for clip_timestamp in range(len(video1)):
# get a img
# img [timestamps[x], channel, h, w]
# img [channel, h, w] numpy
img1 = video1[clip_timestamp].numpy()
img2 = video2[clip_timestamp].numpy()
# calculate ssim of a video
ssim_results_of_a_video.append(calculate_ssim_function(img1, img2))
ssim_results.append(ssim_results_of_a_video)
ssim_results = np.array(ssim_results)
ssim = {}
ssim_std = {}
for clip_timestamp in range(len(video1)):
ssim[clip_timestamp] = np.mean(ssim_results[:,clip_timestamp])
ssim_std[clip_timestamp] = np.std(ssim_results[:,clip_timestamp])
result = {
"value": ssim,
"value_std": ssim_std,
"video_setting": video1.shape,
"video_setting_name": "time, channel, heigth, width",
}
return result
# test code / using example
def main():
NUMBER_OF_VIDEOS = 8
VIDEO_LENGTH = 50
CHANNEL = 3
SIZE = 64
videos1 = torch.zeros(NUMBER_OF_VIDEOS, VIDEO_LENGTH, CHANNEL, SIZE, SIZE, requires_grad=False)
videos2 = torch.zeros(NUMBER_OF_VIDEOS, VIDEO_LENGTH, CHANNEL, SIZE, SIZE, requires_grad=False)
device = torch.device("cuda")
import json
result = calculate_ssim(videos1, videos2)
print(json.dumps(result, indent=4))
if __name__ == "__main__":
main() |