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Make library usable with Python 3. #2

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0af41f4
Require Python 3.9, add dependency install command
intrigus Nov 23, 2021
53d9eeb
Update printing for python 3.
intrigus Nov 23, 2021
3b8abf7
Remove unnecessary import & update imports
intrigus Nov 23, 2021
369c5af
Use modern API for current time.
intrigus Nov 23, 2021
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29 changes: 14 additions & 15 deletions ci2v.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,14 +8,13 @@
import cv2

from sys import stdout
from itertools import izip

from skimage import color
from skimage.measure import structural_similarity as ssim
from skimage.metrics import structural_similarity as ssim


#record start time
start = time.clock()
start = time.process_time()

#ignore non-contiguous skimage warning
warnings.filterwarnings("ignore", module="skimage")
Expand Down Expand Up @@ -45,7 +44,7 @@ def parse_video(image, video, n_matches, break_point=False, verbose=False):
frame_count = 0

#get current time
fps_time = time.clock()
fps_time = time.process_time()

cap = cv2.VideoCapture(video)
while(cap.isOpened()):
Expand Down Expand Up @@ -81,7 +80,7 @@ def parse_video(image, video, n_matches, break_point=False, verbose=False):
similarities = similarities[:n_matches]

#calculate fps
fps = frame_count / (time.clock() - fps_time)
fps = frame_count / (time.process_time() - fps_time)

#feedback to cli
stdout.write('\r@ %d [%sfps] | best: %d (%s) \r'
Expand All @@ -99,13 +98,13 @@ def parse_video(image, video, n_matches, break_point=False, verbose=False):

def sort_results(results, output=False):
#sort results
print '\n'
print('\n')
sorted_results = sorted(results, key=operator.itemgetter('similarity'), reverse=True)
n = 0
print '\n--results:'
print('\n--results:')
for res in sorted_results:
n += 1
print '#%s\t%s\t%s\t: %s' % (n, res['filename'], res['frame'], res['similarity'])
print('#%s\t%s\t%s\t: %s' % (n, res['filename'], res['frame'], res['similarity']))

#save matched frames
if output:
Expand All @@ -126,7 +125,7 @@ def walk(source_image, directory, number=1, break_point=False):
for ext in extentions:
if file.endswith(ext):
video_fn = (os.path.join(root, file))
print video_fn
print(video_fn)
similarities = parse_video(source_image,
video_fn,
n_matches=number,
Expand Down Expand Up @@ -181,32 +180,32 @@ def main():
#either walk directory or hande single file
if args.directory:
#scan directory and process each video file
print '\n--reading videos:'
print('\n--reading videos:')
results = walk(source_image, args.directory, args.number, args.break_point)
s_results = sort_results(results, args.output)

else:
#process single video file
print '\n--reading video:'
print('\n--reading video:')
similarities = parse_video(source_image,
args.video,
n_matches=args.number,
break_point=args.break_point)

print '\n\n--results:'
print('\n\n--results:')
#results to cli
n = 0
for d in similarities:
n += 1
print '#%s\t%s\t: %s' % (n, d['frame'], d['similarity'])
print('#%s\t%s\t: %s' % (n, d['frame'], d['similarity']))

#save matched frames
if args.output:
save_frame(args.output, n, d['image'])

seconds_taken = time.clock() - start
seconds_taken = time.process_time() - start
time_taken = str(datetime.timedelta(seconds=seconds_taken))
print '\n--time taken: \n%s\n' % time_taken
print('\n--time taken: \n%s\n' % time_taken)


if __name__ == '__main__':
Expand Down
8 changes: 6 additions & 2 deletions readme.md
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Original file line number Diff line number Diff line change
Expand Up @@ -5,11 +5,15 @@ ci2v searches videos for frames most similar to an image.


## Requirements
* python
* python 3.9 (might work with lower versions, but is untested!)
* numpy
* opencv
* scikit-image

```bash
pip install scikit-image opencv-python numpy
```


## How to use it

Expand Down Expand Up @@ -83,4 +87,4 @@ Search recursively through the specified directory for video files and process e

ci2v is built around **OpenCV** and **scikit-image**, and uses the latter's [*Structural similarity index*](http://scikit-image.org/docs/dev/auto_examples/plot_ssim.html) function to compare an input image to a video frame.

Because `ssim` can be slow with large images, ci2v rescales images to 10x10px greyscale arrays. This means ci2v can search a video at around 200fps and still find acceptable matches.
Because `ssim` can be slow with large images, ci2v rescales images to 10x10px greyscale arrays. This means ci2v can search a video at around 200fps and still find acceptable matches.