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app.py
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app.py
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from flask import Flask, render_template, request
from flask.ext.uploads import UploadSet, configure_uploads, ALL
import cv2
import subprocess as sp
import re
import base64
import os
from glob import glob
import numpy as np
FFMPEG_BIN = "ffmpeg"
app = Flask(__name__)
videos = UploadSet('videos', ALL)
app.config['UPLOADED_VIDEOS_DEST'] = 'static/video/'
configure_uploads(app, videos)
@app.route('/')
def index():
return render_template('index.html')
@app.route('/upload', methods=['POST'])
def upload():
if 'file' in request.files:
filename = videos.save(request.files['file'])
return extract_first_frame(filename)
return render_template('500.html')
@app.route('/view', methods=['POST'])
def mask():
datauri = request.form['data']
name = request.form['name']
filename = request.form['filename']
imgstr = re.search(r'base64,(.*)', datauri).group(1)
# create mask
with open('static/input/{0}/{0}_mask.png'.format(name), 'wb') as f:
f.write(base64.b64decode(imgstr))
# create directory for input frames
command = [ 'mkdir',
'-pv',
'static/frames/{0}'.format(name)]
pipe = sp.Popen(command, stdout = sp.PIPE)
# create directory for output
command = [ 'mkdir',
'-pv',
'static/output/{0}'.format(name)]
pipe = sp.Popen(command, stdout = sp.PIPE)
# extract frames from video
command = [ FFMPEG_BIN,
'-i', 'static/video/' + filename,
'-r', '15',
'-y',
'-vf', 'scale=1280:720',
'-f', 'image2',
'static/frames/{0}/%04d.png'.format(name)]
pipe = sp.Popen(command, stdout = sp.PIPE, bufsize=2**16)
pipe.wait()
# apply blending and output frames
images = readImages("static/frames/{0}/".format(name))
first = images[0]
mask = cv2.imread("static/input/{0}/{0}_mask.png".format(name))
mask[mask != 0] = 1
for i, image in enumerate(images):
print "Applying blending to frame {0} of {1}.".format(i, len(images))
output = blend(first, image, mask)
cv2.imwrite("static/output/{0}/{1}.png".format(name, i), output)
# bring frames back together
command = [ FFMPEG_BIN,
'-i', 'static/output/{0}/%d.png'.format(name),
'static/output/{0}/{0}.gif'.format(name)]
pipe = sp.Popen(command, stdout = sp.PIPE, bufsize=2**16)
pipe.wait()
return render_template('view.html', image='static/output/{0}/{0}.gif'.format(name))
def readImages(image_dir):
""" This function reads in input images from a image directory
Note: This is implemented for you since its not really relevant to
computational photography (+ time constraints).
Args:
image_dir (str): The image directory to get images from.
Returns:
images(list): List of images in image_dir. Each image in the list is of
type numpy.ndarray.
"""
extensions = ['bmp', 'pbm', 'pgm', 'ppm', 'sr', 'ras', 'jpeg',
'jpg', 'jpe', 'jp2', 'tiff', 'tif', 'png']
search_paths = [os.path.join(image_dir, '*.' + ext) for ext in extensions]
image_files = sorted(reduce(list.__add__, map(glob, search_paths)))
images = [cv2.imread(f, cv2.IMREAD_UNCHANGED | cv2.IMREAD_COLOR)
for f in image_files]
return images
def extract_first_frame(filename):
name = filename[0:filename.find('.')]
command = [ 'rm',
'-rf',
'static/input/{0}'.format(name)]
pipe = sp.Popen(command, stdout = sp.PIPE)
command = [ 'mkdir',
'-pv',
'static/input/{0}'.format(name)]
pipe = sp.Popen(command, stdout = sp.PIPE)
command = [ FFMPEG_BIN,
'-i', 'static/video/' + filename,
'-vframes', '1',
'-vf', 'scale=1280:720',
'-f', 'image2',
'static/input/{0}/{0}.png'.format(name)]
pipe = sp.Popen(command, stdout = sp.PIPE, bufsize=2**16)
pipe.wait()
return render_template('process.html', image='static/input/{0}/{0}.png'.format(name), name=name, filename=filename)
def blend(image1, image2, mask):
# generate Gaussian pyramid for image 1
G = image1.astype(np.float32)
gpA = [G]
for i in xrange(6):
G = cv2.pyrDown(G)
gpA.append(G.astype(np.float32))
# generate Gaussian pyramid for image 2
G = image2.astype(np.float32)
gpB = [G]
for i in xrange(6):
G = cv2.pyrDown(G)
gpB.append(G.astype(np.float32))
# generate Gaussian pyramid for mask
G = mask.astype(np.float32)
gpM = [G]
for i in xrange(6):
G = cv2.pyrDown(G)
gpM.append(G.astype(np.float32))
# generate Laplacian Pyramid for image 1
lpA = [gpA[5]]
for i in xrange(5,0,-1):
rows,cols = gpA[i-1].shape[:2]
GE = cv2.pyrUp(gpA[i])[:rows,:cols]
L = cv2.subtract(gpA[i-1],GE)
lpA.append(L)
# generate Laplacian Pyramid for image 2
lpB = [gpB[5]]
for i in xrange(5,0,-1):
rows,cols = gpB[i-1].shape[:2]
GE = cv2.pyrUp(gpB[i])[:rows,:cols]
L = cv2.subtract(gpB[i-1],GE)
lpB.append(L)
# Now add the images with mask
LS = []
length = len(lpA)
for i in range(length):
LS.append(lpB[i]*gpM[length-i-1] + lpA[i]*(1-gpM[length-i-1]))
# now reconstruct
ls_ = LS[0]
for i in xrange(1,6):
rows,cols = LS[i].shape[:2]
ls_ = cv2.pyrUp(ls_)[:rows,:cols]
ls_ = cv2.add(ls_, LS[i])
ls_ = np.clip(ls_, 0, 255)
return ls_.astype(np.uint8)
if __name__ == '__main__':
app.run(debug=True, host='0.0.0.0')