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remove adjacent duplicate frames #5

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remove adjacent duplicate frames #5

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18 changes: 15 additions & 3 deletions video2pdfslides.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@
import img2pdf
import glob
import argparse
from skimage.metrics import structural_similarity

############# Define constants

Expand All @@ -18,6 +19,7 @@
DETECT_SHADOWS = False # If true, the algorithm will detect shadows and mark them.
MIN_PERCENT = 0.1 # min % of diff between foreground and background to detect if motion has stopped
MAX_PERCENT = 3 # max % of diff between foreground and background to detect if frame is still in motion
SSIM_THRESHOLD = 0.9 # SSIM threshold of two consecutive frame


def get_frames(video_path):
Expand Down Expand Up @@ -71,6 +73,7 @@ def detect_unique_screenshots(video_path, output_folder_screenshot_path):
(W, H) = (None, None)

screenshoots_count = 0
last_screenshot_file_path = ""
for frame_count, frame_time, frame in get_frames(video_path):
orig = frame.copy() # clone the original frame (so we can save it later),
frame = imutils.resize(frame, width=600) # resize the frame
Expand All @@ -94,9 +97,18 @@ def detect_unique_screenshots(video_path, output_folder_screenshot_path):
filename = f"{screenshoots_count:03}_{round(frame_time/60, 2)}.png"

path = os.path.join(output_folder_screenshot_path, filename)
print("saving {}".format(path))
cv2.imwrite(path, orig)
screenshoots_count += 1

image_ssim = 0.0;
if last_screenshot_file_path != "":
image_last = cv2.imread(last_screenshot_file_path)
image_ssim = structural_similarity(image_last, orig, channel_axis=2, data_range=255)

if image_ssim < SSIM_THRESHOLD:
print("saving {}".format(path))
cv2.imwrite(path, orig)
last_screenshot_file_path = path
screenshoots_count += 1


# otherwise, either the scene is changing or we're still in warmup
# mode so let's wait until the scene has settled or we're finished
Expand Down