initial commit

.gitignore

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+# Ignore contents of video dir (due to size)
+video/*.mp4
+video/*.wav
+
+# PyCharm hidden folders/files
+.idea

README.md

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+# Automated MotD highlights extractor
+This repo contains a small python (3) module for automatically removing the banal and uninsightful commentary from Match of the day, leaving you with just footage of the actual football you wanted to watch in the firrst place.
+
+
+## Example
+
+```python
+import scoreboard_slicer as motd
+
+my_clip = motd.load_video('full_fat_motd.mp4')
+motd.extract_highlights(my_clip, 'highlights_only.mp4')
+```
+
+The module contains two primary functions:
+
+ * `load_video` for loading the video (obviously). This is effectively just a wrapper around `moviepy.editor.VideoFileClip`.
+ * `extract_highlights` for identifying, trimming and joining back together the match highlights.
+
+ And that's more or less all there is to it.
+

scoreboard_slicer.py

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+import moviepy.editor as mpy
+import numpy as np
+import progressbar
+from skimage.color import rgb2grey
+from skimage.feature import corner_harris, corner_peaks
+
+
+# Load video
+def load_video(filename='motd-sample.mp4', in_folder=True):
+    video_loc = './video/' if in_folder else ''
+    clip = mpy.VideoFileClip(video_loc + filename)
+    # change resolution to standardise incoming video & speed up image processing
+    return clip
+
+
+# Get clip's resolution (pixels)
+def get_resolution(clip):
+    sample_frame = clip.get_frame(0)
+    return len(sample_frame[0]), len(sample_frame)
+
+
+# Take a frame of a movie and return number of corners
+def find_corners(image, min_dist=5):
+    bw_image = rgb2grey(image)
+    corners = corner_peaks(corner_harris(bw_image), min_distance=min_dist)
+    return len(corners)
+
+
+def moving_average(values, window):
+    weights = np.repeat(1.0, window) / window
+    sma = np.convolve(values, weights, 'same')
+    return sma
+
+
+def extract_highlights(clip, file_name='output.mp4',
+                       xlim=[0.085, 0.284], ylim=[0.05, 0.1],
+                       sampling_rate=1, minimum_clip=60,
+                       buffer_length=5):
+
+    resolution = get_resolution(clip)
+
+    # Crop to top left corner
+    box_clip = clip.crop(x1=xlim[0] * resolution[0], x2=xlim[1] * resolution[0],
+                         y1=ylim[0] * resolution[1], y2=ylim[1] * resolution[1])
+
+    # Get number of 'corners' for each frame at given sampling rate
+    frame_times = np.arange(0, box_clip.duration, 1 / sampling_rate)
+    bar = progressbar.ProgressBar()
+    n_corners = [find_corners(box_clip.get_frame(t)) for t in bar(frame_times)]
+
+    rolling_corners = moving_average(n_corners, 30 * sampling_rate)
+    is_highlights = np.where([rolling_corners > np.mean(rolling_corners)], 1, 0)[0]
+
+    changes = np.diff(is_highlights)
+    starts = np.where(changes == 1)[0]
+    stops = np.where(changes == -1)[0]
+
+    start_stop = [(starts[i], stops[i]) for i in range(len(starts)) if (stops[i] - starts[i]) >= minimum_clip]
+
+    # get highlights in a list
+    highlights = [clip.subclip(t_start=t[0] - buffer_length, t_end=t[1] + buffer_length) for t in start_stop]
+    # add fade in/out (half buffer length?)
+    highlights = [h.fadein(buffer_length / 2) for h in highlights]
+    highlights = [h.fadeout(buffer_length / 2) for h in highlights]
+
+    # join videos together into one and write to file
+    final_clip = mpy.concatenate_videoclips(highlights, method='compose')
+    final_clip.write_videofile('./output/' + file_name)
+
+

video/readme.md

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+# Video
+Directory for motd video files