diff --git a/cut-highlights/checker.txt b/cut-highlights/checker.txt
new file mode 100644
index 0000000..0efa65f
--- /dev/null
+++ b/cut-highlights/checker.txt
@@ -0,0 +1,11 @@
+[30.0, 0]
+[60.0, 0]
+[90.0, 0]
+[120.0, 0]
+[150.0, 0]
+[180.0, 0]
+[210.0, 0]
+[240.0, 0]
+[270.0, 0]
+[300.0, 0]
+[330.0, 0]
diff --git a/cut-highlights/cut_highlights.py b/cut-highlights/cut_highlights.py
new file mode 100644
index 0000000..ce9d6a5
--- /dev/null
+++ b/cut-highlights/cut_highlights.py
@@ -0,0 +1,186 @@
+#   File Name: cut_highlights.py
+#        Team: standardization
+#  Programmer: ssw03270
+#  Start Date: 07/08/20
+# Last Update: August 2, 2020
+#     Purpose: Almost highlight video has 3 game.
+#              So we have to cut it to compare with our highlights.
+#              This program help to do it.
+
+# 1620, 780 (1920, 1080) : minimap start point in edit video, raw video size
+# If you want to see visual working process, erase #(notes) under the code.
+
+import cv2 as cv
+import numpy as np
+import os
+from matplotlib import pyplot as plt
+
+
+def matching(video_file: str, video_capture: np.ndarray, video_path: str, compare_image: np.ndarray) -> None:
+    """
+        For comparing video's capture and image
+
+        Args:
+            video_capture: One frame of video for compare
+            video_path: Video's path that user input
+            compare_image: Image file for compare
+
+        Returns:
+            N/A
+
+        Raises:
+            N/A
+    """
+    checker = []
+    is_writing = False
+    game_set = ["_GAME1", "_GAME2", "_GAME3", "_GAME4", "_GAME5"]
+    game_num = -1
+
+    width = int(video_capture.get(cv.CAP_PROP_FRAME_WIDTH))
+    height = int(video_capture.get(cv.CAP_PROP_FRAME_HEIGHT))
+    fourcc = cv.VideoWriter_fourcc(*"mp4v")
+    fps = video_capture.get(cv.CAP_PROP_FPS)
+
+    sift_ans = False
+    while True:
+        if not sift_ans:
+            is_writing = False
+
+        else:
+            if not is_writing:
+                is_writing = True
+                game_num += 1
+                out = cv.VideoWriter(video_file + game_set[game_num] + ".mp4", fourcc, fps, (width, height), 1)
+
+            out.write(frame_color)
+            # cv.imshow("EditedFrame", frame_color)
+
+        ret, frame_color = video_capture.read()
+        if not ret:
+            break
+        frame_gray = cv.cvtColor(frame_color, cv.COLOR_BGR2GRAY)
+        width_end, height_end = frame_gray.shape
+
+        width_start = round(780 / 1080 * width_end)
+        height_start = round(1620 / 1920 * height_end)
+
+        frame_resize = frame_gray[width_start: width_end, height_start: height_end]
+
+        # Showing video.
+        # cv.imshow("VideoFrame", frame_gray)
+
+        if video_capture.get(cv.CAP_PROP_POS_FRAMES) % fps == 0:
+            print("start comparing..." + str(video_capture.get(cv.CAP_PROP_POS_FRAMES)))
+            sift_ans = sift_algorithm(frame_resize, compare_image)
+            checker.append([video_capture.get(cv.CAP_PROP_POS_FRAMES), sift_ans])
+
+        # Stopping video.
+        # if cv.waitKey(1) > 0:
+        #     break
+
+    # write_txt(checker)
+    out.release()
+    video_capture.release()
+    cv.destroyAllWindows()
+
+
+def sift_algorithm(frame_resize: np.ndarray, compare_image: np.ndarray) -> bool:
+    """
+        Using sift algorithm to compare video's capture and image
+
+        Args:
+            frame_resize: Resized frame for compare
+            compare_image: Image file for compare
+
+        Returns:
+            If list name of good has 30 or more values, returns 1. (It means this frame is ingame)
+            If not, returns 0. (It means this frame isn't ingame)
+
+        Raises:
+            N/A
+    """
+    sift = cv.xfeatures2d.SIFT_create()
+
+    keypoint_1, descriptor_1 = sift.detectAndCompute(frame_resize, None)
+    keypoint_2, descriptor_2 = sift.detectAndCompute(compare_image, None)
+
+    bf = cv.BFMatcher()
+    matches = bf.knnMatch(descriptor_1, descriptor_2, 2)
+
+    good = []
+    for m, n in matches:
+        if m.distance < 0.75 * n.distance:
+            good.append([m])
+
+    # Showing plt.
+    # plt_image = cv.drawMatchesKnn(frame_resize, keypoint_1, compare_image, keypoint_2, good, None, flags=2)
+    # plt.imshow(plt_image)
+    # plt.show()
+
+    if len(good) > 15:
+        print("this frame is ingame.")
+        return True
+    else:
+        print("this frame isn't ingame.")
+        return False
+
+
+def write_txt(checker: list) -> None:
+    """
+        Writing the value that the frame has.
+        Now, it wasn't needed - 2020/08/02
+
+        Args:
+            checker: This list has current frame number and that's status.
+
+        Returns:
+            N/A
+
+        Raises:
+            N/A
+    """
+    print("start writeing...")
+    f = open("checker.txt", "w")
+    for data in checker:
+        f.writelines(str(data) + "\n")
+    f.close()
+
+
+def make_resource(path: str, type: int) -> np.ndarray:
+    """
+        Making a resource with path, according to type.
+
+        Args:
+            path: File's local path.
+            type: File's type, 0 is video file, 1 is image file.
+
+        Returns:
+            If type is 0, returns video capture.
+            If type is 1, returns image which convert gray.
+
+        Raises:
+            N/A
+    """
+    if type == 0:
+        return cv.VideoCapture(path)
+    elif type == 1:
+        return cv.imread(path, cv.COLOR_BGR2GRAY)
+
+
+def main() -> None:
+    image_path = "../resources/standardization/sample_image"
+    video_path = "../resources/standardization/sample_video"
+
+    video_list = os.listdir(video_path)
+    for video_file in video_list:
+        new_video_path = video_path + "/" + video_file
+        video_capture = make_resource(new_video_path, 0)
+
+        minimap_file = image_path + "/minimap.png"
+        minimap_image = make_resource(minimap_file, 1)
+
+        matching(video_file[0:len(video_file) - 4], video_capture, video_path, minimap_image)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/minimap/cognition_inGame.py b/minimap/cognition_inGame.py
new file mode 100644
index 0000000..45c156c
--- /dev/null
+++ b/minimap/cognition_inGame.py
@@ -0,0 +1,127 @@
+"""
+#   File Name: cognition_inGame.py
+#        Team: standardization
+#  Programmer: tjswodud
+#  Start Date: 07/07/20
+# Last Update: September 28, 2020
+#     Purpose: Full video of LCK will be given in this program.
+#              And compare frame and minimap image (template) per frame, using sift_algorithm.
+#              (if it success for compare, that frame is ingame, if not, that frame is not ingame.)
+#              Finally, this program will return edited video, except for frame that is not ingame.
+"""
+
+import cognition_pause as pause
+import cv2 as cv
+import numpy as np
+import searching_upperleft_banner as upperleft
+
+def match_template(video_capture: np.ndarray, template: np.ndarray, pause_image: np.ndarray, compare_images: list, video_file: str, video_path: str) -> None:
+    """
+        Compare captured video and template image (minimap image) with sift_algorithm
+        , and then write video frame that is in_game
+        Args:
+            video_capture: captured video using VideoCapture in __main__
+            template: a template image (minimap image) to compare with video_capture
+            video_file: name of video file in string type
+            video_path: path of output_video (output_video will be stored this path)
+        Returns:
+            None
+        Raises:
+            N/A
+    """
+    is_writing = False
+
+    file_name = video_file.replace('.mp4', '')
+
+    width = int(video_capture.get(cv.CAP_PROP_FRAME_WIDTH))
+    height = int(video_capture.get(cv.CAP_PROP_FRAME_HEIGHT))
+    fourcc = cv.VideoWriter_fourcc(*"mp4v")
+    fps = video_capture.get(cv.CAP_PROP_FPS)
+    output = cv.VideoWriter((video_path + '/' + file_name + '_output' + '.mp4'), fourcc, fps, (width, height), 1)
+
+    # sift_ans = False
+    while True:
+        ret, frame = video_capture.read()
+        if not ret:
+            break
+
+        frame_gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
+        width_end, height_end = frame_gray.shape # 1080, 1920
+        # width_end_2, height_end_2 = 638, 1138
+
+        width_start = round(780 / 1080 * width_end) # 780
+        height_start = round(1620 / 1920 * height_end) # 1620
+        # width_start_2 = round(442 / 1080 * width_end) # 442
+        # height_start_2 = round(782 / 1920 * height_end) # 782
+
+        frame_resize = frame_gray[width_start: width_end, height_start: height_end]
+        frame_resize_center = frame_gray[442: 638, 782: 1138]
+
+        total_frames = int(video_capture.get(cv.CAP_PROP_FRAME_COUNT))
+
+        if video_capture.get(cv.CAP_PROP_POS_FRAMES) % int(fps) == 0:
+            current_frame = int(video_capture.get(cv.CAP_PROP_POS_FRAMES))
+            percentage = (current_frame / total_frames) * 100
+            # print('{}/{} - {}%'.format(current_frame, total_frames, percentage))
+            print(end='\r')
+            print('Processing... {}%\r'.format(round(percentage, 2)), end = '')
+
+            sift_ans = sift_algorithm(frame_resize, template)
+            pause_ans = pause.sift_algorithm(frame_resize_center, pause_image)
+            replay_ans = upperleft.check_algorithm(upperleft.frame_resize(frame_gray, 0), compare_images[0])
+            highlight_ans = upperleft.check_algorithm(upperleft.frame_resize(frame_gray, 1), compare_images[1])
+            proview_ans = upperleft.check_algorithm(upperleft.frame_resize(frame_gray, 2), compare_images[2])
+
+            if (sift_ans and pause_ans and replay_ans and highlight_ans and proview_ans):
+                is_writing = True
+            else:
+                is_writing = False
+
+        if is_writing:
+            output.write(frame)
+        else:
+            continue
+
+    output.release()
+    video_capture.release()
+    cv.destroyAllWindows()
+
+def sift_algorithm(frame_resize: np.ndarray, template: np.ndarray) -> bool:
+    """
+        Compare video's frame and template image, using sift_algorithm
+        Args:
+            frame_resize: each of video's frame that is resized to template image
+            template: a template image (minimap image) to compare with video_capture
+        Returns:
+            [bool type]
+            if frame_resize and template match more than 15 points, return True (this frame is ingame.)
+            if not, return False (this frame is not ingame.)
+        Raises:
+            N/A
+    """
+    sift = cv.xfeatures2d.SIFT_create()
+
+    keypoint_1, descriptor_1 = sift.detectAndCompute(frame_resize, None)
+    keypoint_2, descriptor_2 = sift.detectAndCompute(template, None)
+
+    bf_matcher = cv.BFMatcher()
+    match = bf_matcher.knnMatch(descriptor_1, descriptor_2, 2)
+
+    success_match = []
+    for m, n in match:
+        if m.distance < n.distance * 0.75:
+            success_match.append([m])
+
+    # plot_image = cv.drawMatchesKnn(frame_resize, keypoint_1, template, keypoint_2, success_match, None, flags=2)
+    # plot.imshow(plot_image)
+    # plot.show()
+
+    if len(success_match) > 15:
+        # print('this frame is ingame.')
+        return True
+    else:
+        # print('this frame is not ingame.')
+        return False
+
+def create_capture(path: str):
+    return cv.VideoCapture(path)
diff --git a/minimap/cognition_pause.py b/minimap/cognition_pause.py
new file mode 100644
index 0000000..05109af
--- /dev/null
+++ b/minimap/cognition_pause.py
@@ -0,0 +1,52 @@
+"""
+#   File Name: cognition_pause.py
+#        Team: standardization
+#  Programmer: tjswodud
+#  Start Date: 07/07/20
+# Last Update: September 28, 2020
+#     Purpose: This file will be used module of cognition_inGame.py.
+#              compare every frame and pause_image, and then cut the frame that has a pause_image.
+"""
+
+import cv2 as cv
+# import matplotlib.pyplot as plot
+import numpy as np
+
+
+def sift_algorithm(frame_resize: np.ndarray, pause_image: np.ndarray) -> bool:
+    """
+        Compare video's frame and pause image, using sift_algorithm
+
+        Args:
+            frame_resize: each of video's frame that is resized to template image
+            pause_image: a image (pause_image) to compare with video_capture
+
+        Returns:
+            [bool type]
+            if frame_resize and pause_image match more than 15 points, return False (this frame isn't ingame.)
+            if not, return True (this frame is ingame. -> match with template_image)
+
+        Raises:
+            N/A
+    """
+    sift = cv.xfeatures2d.SIFT_create()
+
+    keypoint_1, descriptor_1 = sift.detectAndCompute(frame_resize, None)
+    keypoint_2, descriptor_2 = sift.detectAndCompute(pause_image, None)
+
+    bf_matcher = cv.BFMatcher()
+    match = bf_matcher.knnMatch(descriptor_1, descriptor_2, 2)
+
+    success_match = []
+    for m, n in match:
+        if m.distance < n.distance * 0.75:
+            success_match.append([m])
+
+    # plot_image = cv.drawMatchesKnn(frame_resize, keypoint_1, pause_image, keypoint_2, success_match, None, flags=2)
+    # plot.imshow(plot_image)
+    # plot.show()
+
+    if len(success_match) > 15:
+        return False
+    else:
+        return True
\ No newline at end of file
diff --git a/minimap/cut_highlights.py b/minimap/cut_highlights.py
new file mode 100644
index 0000000..7dda315
--- /dev/null
+++ b/minimap/cut_highlights.py
@@ -0,0 +1,174 @@
+#   File Name: cut_highlights.py
+#        Team: standardization
+#  Programmer: ssw03270
+#  Start Date: 07/08/20
+# Last Update: August 2, 2020
+#     Purpose: Almost highlight video has 3 game.
+#              So we have to cut it to compare with our highlights.
+#              This program help to do it.
+
+# 1620, 780 (1920, 1080) : minimap start point in edit video, raw video size
+# If you want to see visual working process, erase #(notes) under the code.
+
+import cv2 as cv
+import numpy as np
+import os
+from matplotlib import pyplot as plt
+
+
+def matching(video_file: str, video_capture: np.ndarray, video_path: str, compare_image: np.ndarray) -> None:
+    """
+        For comparing video's capture and image
+        Args:
+            video_capture: One frame of video for compare
+            video_path: Video's path that user input
+            compare_image: Image file for compare
+        Returns:
+            N/A
+        Raises:
+            N/A
+    """
+    checker = []
+    is_writing = False
+    game_set = ["_GAME1", "_GAME2", "_GAME3", "_GAME4", "_GAME5"]
+    game_num = -1
+
+    width = int(video_capture.get(cv.CAP_PROP_FRAME_WIDTH))
+    height = int(video_capture.get(cv.CAP_PROP_FRAME_HEIGHT))
+    fourcc = cv.VideoWriter_fourcc(*"mp4v")
+    fps = video_capture.get(cv.CAP_PROP_FPS)
+
+    sift_ans = False
+    while True:
+        if not sift_ans:
+            is_writing = False
+
+        else:
+            if not is_writing:
+                is_writing = True
+                game_num += 1
+                out = cv.VideoWriter(video_file + game_set[game_num] + ".mp4", fourcc, fps, (width, height), 1)
+
+            out.write(frame_color)
+            # cv.imshow("EditedFrame", frame_color)
+
+        ret, frame_color = video_capture.read()
+        if not ret:
+            break
+        frame_gray = cv.cvtColor(frame_color, cv.COLOR_BGR2GRAY)
+        width_end, height_end = frame_gray.shape
+
+        width_start = round(780 / 1080 * width_end)
+        height_start = round(1620 / 1920 * height_end)
+
+        frame_resize = frame_gray[width_start: width_end, height_start: height_end]
+
+        # Showing video.
+        # cv.imshow("VideoFrame", frame_gray)
+
+        if video_capture.get(cv.CAP_PROP_POS_FRAMES) % fps == 0:
+            print("start comparing..." + str(video_capture.get(cv.CAP_PROP_POS_FRAMES)))
+            sift_ans = sift_algorithm(frame_resize, compare_image)
+            checker.append([video_capture.get(cv.CAP_PROP_POS_FRAMES), sift_ans])
+
+        # Stopping video.
+        # if cv.waitKey(1) > 0:
+        #     break
+
+    # write_txt(checker)
+    out.release()
+    video_capture.release()
+    cv.destroyAllWindows()
+
+
+def sift_algorithm(frame_resize: np.ndarray, compare_image: np.ndarray) -> bool:
+    """
+        Using sift algorithm to compare video's capture and image
+        Args:
+            frame_resize: Resized frame for compare
+            compare_image: Image file for compare
+        Returns:
+            If list name of good has 30 or more values, returns 1. (It means this frame is ingame)
+            If not, returns 0. (It means this frame isn't ingame)
+        Raises:
+            N/A
+    """
+    sift = cv.xfeatures2d.SIFT_create()
+
+    keypoint_1, descriptor_1 = sift.detectAndCompute(frame_resize, None)
+    keypoint_2, descriptor_2 = sift.detectAndCompute(compare_image, None)
+
+    bf = cv.BFMatcher()
+    matches = bf.knnMatch(descriptor_1, descriptor_2, 2)
+
+    good = []
+    for m, n in matches:
+        if m.distance < 0.75 * n.distance:
+            good.append([m])
+
+    # Showing plt.
+    # plt_image = cv.drawMatchesKnn(frame_resize, keypoint_1, compare_image, keypoint_2, good, None, flags=2)
+    # plt.imshow(plt_image)
+    # plt.show()
+
+    if len(good) > 15:
+        print("this frame is ingame.")
+        return True
+    else:
+        print("this frame isn't ingame.")
+        return False
+
+
+def write_txt(checker: list) -> None:
+    """
+        Writing the value that the frame has.
+        Now, it wasn't needed - 2020/08/02
+        Args:
+            checker: This list has current frame number and that's status.
+        Returns:
+            N/A
+        Raises:
+            N/A
+    """
+    print("start writeing...")
+    f = open("checker.txt", "w")
+    for data in checker:
+        f.writelines(str(data) + "\n")
+    f.close()
+
+
+def make_resource(path: str, type: int) -> np.ndarray:
+    """
+        Making a resource with path, according to type.
+        Args:
+            path: File's local path.
+            type: File's type, 0 is video file, 1 is image file.
+        Returns:
+            If type is 0, returns video capture.
+            If type is 1, returns image which convert gray.
+        Raises:
+            N/A
+    """
+    if type == 0:
+        return cv.VideoCapture(path)
+    elif type == 1:
+        return cv.imread(path, cv.COLOR_BGR2GRAY)
+
+
+def main() -> None:
+    image_path = "../resources/standardization/sample_image"
+    video_path = "../resources/standardization/sample_video"
+
+    video_list = os.listdir(video_path)
+    for video_file in video_list:
+        new_video_path = video_path + "/" + video_file
+        video_capture = make_resource(new_video_path, 0)
+
+        minimap_file = image_path + "/minimap.png"
+        minimap_image = make_resource(minimap_file, 1)
+
+        matching(video_file[0:len(video_file) - 4], video_capture, video_path, minimap_image)
+
+
+if __name__ == '__main__':
+    main()
\ No newline at end of file
diff --git a/minimap/cut_not_inGame.py b/minimap/cut_not_inGame.py
new file mode 100644
index 0000000..be3a6b8
--- /dev/null
+++ b/minimap/cut_not_inGame.py
@@ -0,0 +1,55 @@
+"""
+#   File Name: cut_not_inGame.py
+#        Team: standardization
+#  Programmer: tjswodud
+#  Start Date: 07/07/20
+# Last Update: September 28, 2020
+#     Purpose: Full video of LCK will be given in this program.
+#              And compare frame and compare_image (minimap, pause, upperleft banner) per frame, using sift_algorithm.
+#              (if it success for compare, that frame is ingame, if not, that frame is not ingame.)
+#              Finally, this program will return edited video, except for frame that is not ingame.
+"""
+
+import cognition_inGame as ingame
+import cv2 as cv
+import os
+import searching_upperleft_banner as upperleft
+import time
+
+start_time = time.time()
+
+# in Windows OS
+resource_path = "E:/video/resources"
+output_path = "E:/video/outputs"
+
+# in Ubuntu OS
+# resource_path = "/media/cogongnam/f8447e77-84e5-43a2-a0f0-e1b1977f1322/video/resources"
+# output_path = "/media/cogongnam/f8447e77-84e5-43a2-a0f0-e1b1977f1322/video/outputs"
+
+replay_banner_path = "../resources/replay_banner.png"
+replay_banner = upperleft.resource(replay_banner_path)
+highlight_banner_path = "../resources/highlight.png"
+highlight_banner = upperleft.resource(highlight_banner_path)
+pro_view_banner_path = "../resources/pro view.png"
+pro_view_banner = upperleft.resource(pro_view_banner_path)
+
+video_list = os.listdir(resource_path)
+template_image = cv.imread("../resources/minimap_templ.png", cv.COLOR_BGR2GRAY)
+pause_image = cv.imread("../resources/pause_image.png", cv.COLOR_BGR2GRAY)
+
+compare_images = []
+compare_images.append(replay_banner)
+compare_images.append(highlight_banner)
+compare_images.append(pro_view_banner)
+
+video_num = 1
+for video_file in video_list:
+    new_video_path = resource_path + '/' + video_file
+    video_capture = ingame.create_capture(new_video_path)
+    print('[No.{} video is editing...]'.format(video_num))
+    ingame.match_template(video_capture, template_image, pause_image, compare_images, video_file, output_path)
+    video_num += 1
+
+end_time = time.time()
+exe_time = round((end_time - start_time), 1)
+print('time : {} s.'.format(exe_time))
\ No newline at end of file
diff --git a/resources/highlight.png b/resources/highlight.png
new file mode 100644
index 0000000..16a1f69
Binary files /dev/null and b/resources/highlight.png differ
diff --git a/resources/minimap_templ.png b/resources/minimap_templ.png
new file mode 100644
index 0000000..93580a9
Binary files /dev/null and b/resources/minimap_templ.png differ
diff --git a/resources/pause_image.png b/resources/pause_image.png
new file mode 100644
index 0000000..2f0210b
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diff --git a/resources/standardization/sample_image/replay_game.png b/resources/standardization/sample_image/replay_game.png
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diff --git a/searching_upperleft_banner/checker.txt b/searching_upperleft_banner/checker.txt
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diff --git a/searching_upperleft_banner/searching_upperleft_banner.py b/searching_upperleft_banner/searching_upperleft_banner.py
new file mode 100644
index 0000000..8b9637c
--- /dev/null
+++ b/searching_upperleft_banner/searching_upperleft_banner.py
@@ -0,0 +1,222 @@
+"""
+# File Name: searching_left_banner
+#      Team: standardization
+# Programmer: wpdudH
+# Start Date: 07/15/20
+#Last Update: September 28, 2020
+#    Purpose: Raw video consists of in-game videos and replay videos.
+#             So we need to distinguish between in-game videos and replay videos.
+#             This program carries out that work.
+"""
+
+import cv2 as cv
+import numpy as np
+# from matplotlib import pyplot as plt
+
+def check_algorithm(frame_resize: np.ndarray, compare_image: np.ndarray) -> bool:
+    """
+        Comparing video's capture to replay banner image to use sift algorithm
+        Args:
+            frame_resize: Resized frame for compare
+            compare_image: Image file for comparison
+        Returns:
+            If list name of good has more than 30 values, return 1 (It means this frame is replay)
+            If not, returns 0 (It means this frame is in-game)
+        Raises:
+            N/A
+    """
+    sift = cv.xfeatures2d.SIFT_create()
+
+    # frame_resize = cv.cvtColor(frame_resize, cv.COLOR_BGR2GRAY)
+
+    keypoint_1, descriptor_1 = sift.detectAndCompute(frame_resize, None)
+    keypoint_2, descriptor_2 = sift.detectAndCompute(compare_image, None)
+
+    bf = cv.BFMatcher()
+    matches = bf.knnMatch(descriptor_1, descriptor_2, k=2)
+
+    good = []
+    for m, n in matches:
+        if m.distance < 0.75 * n.distance:
+            good.append([m])
+
+    # plt_image = cv.drawMatchesKnn(frame_resize, keypoint_1, compare_image, keypoint_2, good, None, flags=2)
+    # plt.imshow(plt_image)
+    # plt.show()
+
+    if len(good) > 30:
+        return False
+    else:
+        return True
+
+'''
+def checklist_writer(compare_result:list) -> None:
+    """
+        Write check file consisting of results comparing video's capture to replay banner image
+        Args:
+            compare_result: list of result of compare
+        Returns:
+            None
+        Raises:
+            N/A
+    """
+    classified = []
+    for i in range(len(compare_result)):
+        classified.append("[{}, {}]".format(i * 30, compare_result[i]))
+    # Store the video's play time and result of check_algorithm in the list named classified
+
+    with open("checker.txt", 'w') as file:
+        for i in range(len(classified)):
+            text = classified[i]
+            file.writelines(text+'\n')
+    # Write csv file consisting of values of list named classified
+'''
+
+def frame_resize(frame: np.ndarray, image: int) -> np.ndarray:
+    """
+        Resize frame to be compared
+        Args:
+            frame: Frame of video
+            image: Determinist of frame resize value
+                   If image value is 0, frame will be resized to the same size as the replay banner
+                   If image value is 1, frame will be resized to the same size as the highlight banner
+                   If image value is 2, frame will be resized to the same size as the pro view banner
+        Returns:
+            frame_resize: Resized frame
+        Raises:
+            N/A
+    """
+    height, width = frame.shape
+
+    if image == 0:
+        start_height = round(45 / 1080 * height)
+        start_width = 0
+        end_width = round(254 / 1920 * width)
+        end_height = round(122 / 1080 * height)
+        # replay_banner start point : (0,45)
+        # replay_banner end point : (254,122)
+    elif image == 1:
+        start_height = round(45 / 1080 * height)
+        start_width = 0
+        end_width = round(339 / 1920 * width)
+        end_height = round(122 / 1080 * height)
+        # highlight_banner start point : (0,45)
+        # highlight_banner end point : (339,122)
+    else:
+        start_height = round(45 / 1080 * height)
+        start_width = round(1592 / 1920 * width)
+        end_width = 1920
+        end_height = round(122 / 1080 * height)
+        # pro_view_banner start point : (1592,45)
+        # pro_view_banner end point : (1920,122)
+
+    frame_resize = frame[start_height:end_height, start_width:end_width]
+    # resizing frame to reduce the computation
+
+    return frame_resize
+
+'''
+def store_video(video_name:str, video_path:str, compare_images: list ) -> None:
+    """
+        Storing in-game video
+        Args:
+             video_path:Video's path to be stored
+             compare_image: Image comparing with frame
+             video_name: File name of video
+        Returns:
+            None
+        Raises:
+            N/A
+    """
+    # compare_result = []
+    video_capture = cv.VideoCapture(video_path)
+
+    total_frames = video_capture.get(cv.CAP_PROP_FRAME_COUNT)
+    fps = video_capture.get(cv.CAP_PROP_FPS)
+    width = video_capture.get(cv.CAP_PROP_FRAME_WIDTH)
+    height = video_capture.get(cv.CAP_PROP_FRAME_HEIGHT)
+
+    fourcc = cv.VideoWriter_fourcc(*"mp4v")
+    output_video = cv.VideoWriter(video_name+".mp4", fourcc, fps, (int(width), int(height)))
+
+    check = False
+    # If check is True, start storing frame
+    # If check is False, stop storing frame
+
+    print("here")
+
+    while True:
+        ret, frame = video_capture.read()
+
+        if not ret:
+            print("There is no frame.Check the video file")
+            break
+
+
+        if video_capture.get(cv.CAP_PROP_POS_FRAMES) % int(fps) == 0:
+            if check_algorithm(frame_resize(frame, 0), compare_images[0]) == 1 or check_algorithm(frame_resize(frame, 2), compare_images[2]) == 1:
+                # compare frame with replay_banner and pro view banner
+                # compare_result.append('1')
+                check = False
+                print("replay")
+            else:
+                if video_capture.get(cv.CAP_PROP_POS_FRAMES) > total_frames - (240 * fps):
+                    if check_algorithm(frame_resize(frame, 1), compare_images[1]) == 1 or check_algorithm(frame_resize(frame, 2), compare_images[2]) == 1:
+                        # compare frame with highlight_banner and pro view banner
+                        break
+                # compare_result.append('0')
+                check = True
+                print("ingame")
+
+        if check == True:
+            output_video.write(frame)
+            # cv.imshow("output", frame)
+
+            # if cv.waitKey(1) > 0: break
+
+        if video_capture.get(cv.CAP_PROP_POS_FRAMES) == video_capture.get(cv.CAP_PROP_FRAME_COUNT):
+            break
+
+    video_capture.release()
+    cv.destroyAllWindows()
+
+    # checklist_writer(compare_result)
+    '''
+
+def resource(path:str) -> np.ndarray:
+    """
+        Changing the type of picture file with path
+        Args:
+            path: File's local path
+        Returns:
+            black-and-white image
+        Raises:
+            N/A
+    """
+    return cv.imread(path, cv.IMREAD_GRAYSCALE)
+
+"""
+def main() -> None:
+    replay_banner_path = "C:/Users/82102/PycharmProjects/LoL-eSports-Highlight-Extractor/resources/replay_banner.png"
+    replay_banner = resource(replay_banner_path)
+    highlight_banner_path = "C:/Users/82102/PycharmProjects/LoL-eSports-Highlight-Extractor/resources/highlight.png"
+    highlight_banner = resource(highlight_banner_path)
+    pro_view_banner_path = "C:/Users/82102/PycharmProjects/LoL-eSports-Highlight-Extractor/resources/pro view.png"
+    pro_view_banner = resource(pro_view_banner_path)
+
+
+    compare_images = []
+    compare_images.append(replay_banner)
+    compare_images.append(highlight_banner)
+    compare_images.append(pro_view_banner)
+
+    video_path = "./resources/standardization/sample_video"
+    video_list = os.listdir(video_path)
+
+    for video_file in video_list:
+        new_video_path = video_path + "/" + video_file
+        store_video(video_file[0:len(video_file) - 4], new_video_path, compare_images)
+
+if __name__ == '__main__':
+    main()
+    """
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