Spaces:
Runtime error
Runtime error
| import numpy as np | |
| from PIL import Image | |
| import torch | |
| import torch.nn.functional as F | |
| from typing import List | |
| from skimage.metrics import structural_similarity as ssim | |
| from skimage import io, color | |
| ROUND_DIGIT=3 | |
| NUM_ASPECT=5 | |
| TEM_SSIM_POINT_HIGH=0.9 | |
| TEM_SSIM_POINT_MID=0.75 | |
| TEM_SSIM_POINT_LOW=0.6 | |
| class MetricSSIM_sim(): | |
| def __init__(self) -> None: | |
| """ | |
| Initialize a class MetricSSIM_sim for testing temporal consistency of a given video. | |
| """ | |
| None | |
| def evaluate(self, frame_list:List[Image.Image]): | |
| """ | |
| Calculate the SSIM between adjacent frames of a given video to test temporal consistency, | |
| then quantize the orginal output based on some predefined thresholds. | |
| Args: | |
| frame_list:List[Image.Image], frames of the video used in calculation. | |
| Returns: | |
| ssim_avg: float, the computed SSIM between each adjacent pair of frames and then averaged among all the pairs. | |
| quantized_ans: int, the quantized value of the above avg SSIM scores based on pre-defined thresholds. | |
| """ | |
| ssim_list=[] | |
| for f_idx in range(len(frame_list)-1): | |
| frame_1=frame_list[f_idx] | |
| frame_1_gray=color.rgb2gray(frame_1) | |
| frame_2=frame_list[f_idx+1] | |
| frame_2_gray=color.rgb2gray(frame_2) | |
| ssim_value, _ = ssim(frame_1_gray, frame_2_gray, full=True,\ | |
| data_range=frame_2_gray.max() - frame_2_gray.min()) | |
| ssim_list.append(ssim_value) | |
| ssim_avg=np.mean(ssim_list) | |
| quantized_ans=0 | |
| if ssim_avg >= TEM_SSIM_POINT_HIGH: | |
| quantized_ans=4 | |
| elif ssim_avg < TEM_SSIM_POINT_HIGH and ssim_avg >= TEM_SSIM_POINT_MID: | |
| quantized_ans=3 | |
| elif ssim_avg < TEM_SSIM_POINT_MID and ssim_avg >= TEM_SSIM_POINT_LOW: | |
| quantized_ans=2 | |
| else: | |
| quantized_ans=1 | |
| return ssim_avg, quantized_ans | |