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import numpy as np
import cv2
from PIL import Image
from typing import List
from skimage.metrics import structural_similarity as ssim
from skimage import io, color
ROUND_DIGIT=3
DYN_SAMPLE_STEP=4
NUM_ASPECT=5
MSE_POINT_HIGH=3000
MSE_POINT_MID=1000
MSE_POINT_LOW=100
class MetricMSE_dyn():
def __init__(self) -> None:
"""
Initialize a class MetricMSE_dyn for testing dynamic degree of a given video.
"""
None
def evaluate(self, frame_list:List[Image.Image]):
"""
Calculate the MSE (Mean Squared Error) between frames sampled at regular intervals of a given video to test dynamic_degree,
then quantize the orginal output based on some predefined thresholds.
Args:
frame_list:List[Image.Image], frames of the video used in calculation.
Returns:
mse_avg: float, the computed MSE between frames sampled at regular intervals and then averaged among all the pairs.
quantized_ans: int, the quantized value of the above avg MSE scores based on pre-defined thresholds.
"""
mse_list=[]
sampled_list = frame_list[::DYN_SAMPLE_STEP]
for f_idx in range(len(sampled_list)-1):
imageA = cv2.cvtColor(np.array(sampled_list[f_idx]), cv2.COLOR_RGB2BGR)
imageB = cv2.cvtColor(np.array(sampled_list[f_idx+1]), cv2.COLOR_RGB2BGR)
err = np.sum((imageA.astype("float") - imageB.astype("float")) ** 2)
err /= float(imageA.shape[0] * imageA.shape[1])
mse_value = err
mse_list.append(mse_value)
mse_avg=np.mean(mse_list)
quantized_ans=0
if mse_avg >= MSE_POINT_HIGH:
quantized_ans=4
elif mse_avg < MSE_POINT_HIGH and mse_avg >= MSE_POINT_MID:
quantized_ans=3
elif mse_avg < MSE_POINT_MID and mse_avg >= MSE_POINT_LOW:
quantized_ans=2
else:
quantized_ans=1
return mse_avg, quantized_ans
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