multiple.py

import cv2
import numpy as np

# Load the video file
cap = cv2.VideoCapture('../test.webm')

# Initialize the face detector
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

# Define blockiness check function
def blockiness(frame):
    # Split the frame into 8x8 blocks
    blocks = [frame[y:y+8, x:x+8] for y in range(0, frame.shape[0], 8) for x in range(0, frame.shape[1], 8)]
    # Calculate the standard deviation of each block
    block_stddevs = [np.std(block) for block in blocks]
    # Calculate the mean standard deviation of all blocks
    mean_stddev = np.mean(block_stddevs)
    # Normalize the mean standard deviation to a scale of 1 to 10
    blockiness_rating = int(np.interp(mean_stddev, [0, 30], [10, 1]))
    return blockiness_rating

# Initialize variables for metrics
frame_count = 0
multiple_faces_count = 0
flatness_score = 10
blurriness_score = 10
blockiness_score = 10

# Process each frame of the video
while cap.isOpened():
    # Read a frame
    ret, frame = cap.read()
    if not ret:
        break
    
    # Increment frame count
    frame_count += 1
    
    # Convert the frame to grayscale
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    
    # Detect faces in the frame
    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
    
    # Update multiple faces count
    if len(faces) > 1:
        multiple_faces_count += 1
    
    # Calculate flatness score
    flatness_score = min(flatness_score, np.std(gray))
    
    # Calculate blurriness score
    blurriness_score = min(blurriness_score, cv2.Laplacian(gray, cv2.CV_64F).var())
    
    # Calculate blockiness score
    blockiness_score = min(blockiness_score, blockiness(frame))
    
# Calculate percentages for multiple faces
multiple_faces_percentage = multiple_faces_count / frame_count * 100

# Normalize scores to a scale of 1 to 10
flatness_rating = int(np.interp(flatness_score, [0, 255], [1, 10]))
blurriness_rating = int(np.interp(blurriness_score, [0, 1000], [10, 1]))
blockiness_rating = blockiness_score

# Print the ratings for all four metrics
print(f"Multiple Faces Percentage: {multiple_faces_percentage:.2f}%")
print(f"Flatness Rating: {flatness_rating}")
print(f"Blurriness Rating: {blurriness_rating}")
print(f"Blockiness Rating: {blockiness_rating}")