test_image_operations.py

import cv2
import numpy as np
import pytesseract
import os
import imutils
import time
import sys
import argparse
from subprocess import PIPE, Popen
from imutils import contours
from scipy.linalg import norm
from scipy import sum, average
from PIL import Image
from termcolor import colored
from time import sleep
# VARIABLES
TEXT_TOP = 425
TEXT_BOTTOM = 600
TEXT_LEFT = 5
TEXT_RIGHT = 700
SAMPLING_FRAME_COUNT = 60
SCENE_CHANGE_PERCENTAGE = 50
width, height = 450, 82
count = 0
count_sub_start = 0
max_diff = 0
found = False
progress_bar = ""
(x, y, w, h) = (0, 0, 0, 0)
ar_old = 1
crWidth_old = 1
count_contours = 0


# Functions
def create_blank(width, height):
    """Create new image(numpy array) filled with certain color in RGB"""
    # Create black blank image
    image = np.zeros((height, width), np.uint8)

    # Since OpenCV uses BGR, convert the color first
    # color = tuple(reversed(rgb_color))
    # Fill image with color
    image[:] = 0

    return image


def update_progress(progress, total):
    percents = 100 * (progress / float(total))
    filled_length = int(round(100 * progress / float(total)))
    sys.stdout.write(
        '\r[\033[1;34mINFO\033[0;0m] [\033[0;32m{0}\033[0;0m] Buffering:{1}%'.format('#' * (filled_length / 5),
                                                                                     filled_length))
    if progress == total:
        sys.stdout.write('\n')
    sys.stdout.flush()


def cmdline(command):
    process = Popen(
        args=command,
        stdout=PIPE,
        shell=True
    )
    return process.communicate()[0]


# Start Of Script

cap = cv2.VideoCapture('test.mp4')
ret, current_frame = cap.read()
previous_frame = current_frame
height_frame, width_frame = current_frame.shape[:2]
window_pos_x = width_frame
window_pos_y = height_frame

# Init ROI_OLD Variable
roi_old = create_blank(width, height)
dst = create_blank(width, height)

# Play Video File
while (cap.isOpened()):
    if not ret:
        break
    # As subtitles are mostly fixed in position to reduce processing of Images we crop out the area where Subtitles should be   
    cropped_current = current_frame[TEXT_TOP:TEXT_BOTTOM, TEXT_LEFT:TEXT_RIGHT]
    cropper_previous = previous_frame[TEXT_TOP:TEXT_BOTTOM, TEXT_LEFT:TEXT_RIGHT]
    current_frame_gray = cv2.cvtColor(cropped_current, cv2.COLOR_BGR2GRAY)
    previous_frame_gray = cv2.cvtColor(cropper_previous, cv2.COLOR_BGR2GRAY)

    # Extract Subtitle Area from Cropped Image
    roi = cropped_current

    current_height, current_width = current_frame_gray.shape[:2]

    image_data = np.asarray(current_frame_gray)
    old_image_data = np.asarray(previous_frame_gray)

    image_data2 = cv2.inRange(image_data, (150), (255))
    old_image_data2 = cv2.inRange(image_data, (150), (255))

    dst = cv2.addWeighted(image_data2, 0.3, old_image_data2, 0.7, 0)

    mse_diff = np.concatenate((image_data2, dst, old_image_data2), axis=0)
    cv2.imshow("Pixels", mse_diff)

    # dst = create_blank(current_width, current_height)

    # Resize ROI old to proper dimensions as konvolutions eat up pixels
    # roi_old = cv2.resize(roi_old,(current_width, current_height), interpolation = cv2.INTER_LINEAR)

    # Save current frame to old so it can be used in next iteration to detect changes
    # roi_old = dst

    # Show non edited Video feed
    cv2.putText(current_frame, "Frame:" + str(count), (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, 255)
    cv2.imshow('Movie', current_frame)
    # cv2.moveWindow('Movie', 0, 0)

    # Increase Frame counter
    count = count + 1
    sleep(40/1000)

    # Wait q key to quit
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

    if count % 5 == 0:
        previous_frame = current_frame.copy()
    ret, current_frame = cap.read()

cap.release()
sys.exit(0)