turn a video into a brightness graph

main-graph-maker.py

import cv2
import numpy as np
import matplotlib.pyplot as plt

# turn a video into a brightness graph

video_path = 'data/yaoxz7sqop8c1.mp4'
video_capture = cv2.VideoCapture(video_path)

def apply_gamma_correction(image, gamma=0.5):
    """ Apply gamma correction to an image. """
    inv_gamma = 1.0 / gamma
    table = np.array([((i / 255.0) ** inv_gamma) * 255 for i in np.arange(0, 256)]).astype("uint8")
    return cv2.LUT(image, table)

# Check if the video was opened successfully
if not video_capture.isOpened():
    raise Exception("Error opening video file")

# Get the frame rate of the video
frame_rate = video_capture.get(cv2.CAP_PROP_FPS)

# Initialize a list to store original brightness values
brightness_values = []

# Process the video frame by frame to calculate brightness
while True:
    ret, frame = video_capture.read()
    if not ret:
        break

    # Apply gamma correction
    gamma_corrected_frame = apply_gamma_correction(frame, gamma=0.5)
    frame = gamma_corrected_frame

    # Convert the frame to grayscale
    gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # Calculate the average brightness
    average_brightness = np.mean(gray_frame)
    brightness_values.append(average_brightness)

# Release the video capture object
video_capture.release()

# Set the figure width to accommodate one pixel per bar
num_frames = len(brightness_values)
fig_width = num_frames / plt.rcParams['figure.dpi']

# Create the bar graph
plt.figure(figsize=(fig_width, 4), tight_layout=True)
plt.bar(range(num_frames), brightness_values, width=1.0, color='blue', align='edge')
plt.title('Brightness Levels Over Time')
#plt.xlabel('Frame Number')
plt.ylabel('Brightness')
plt.xlim(0, num_frames)  # setting x-axis limits
plt.ylim(min(brightness_values), max(brightness_values))  # setting y-axis limits

# Determine label interval based on the total duration
total_duration = num_frames / frame_rate
label_interval_seconds = 5 # Adjust this value based on the total duration
label_step = int(frame_rate * label_interval_seconds)

# Labeling the x-axis with adjusted intervals
frame_numbers = np.arange(0, num_frames, step=label_step)
time_seconds = frame_numbers / frame_rate
plt.xticks(frame_numbers, [f"{time:.0f}s [{frame}f]" for frame, time in zip(frame_numbers, time_seconds)])

# Save the bar graph as a PNG file
output_bar_graph_path = 'data/brightness_bar_graph5.png'
plt.savefig(output_bar_graph_path, dpi=plt.rcParams['figure.dpi'])  # saving with specific dpi to maintain pixel width

# Show the path of the saved file
output_bar_graph_path