backup / scripts /morph_video.py

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import sys
import os
import dlib
import numpy as np
from skimage import io
import cv2
from imutils import face_utils
import argparse
import shutil
import random

import subprocess


class NoFaceFound(Exception):
   """Raised when there is no face found"""
   pass

def calculate_margin_help(img1,img2):
    size1 = img1.shape
    size2 = img2.shape
    diff0 = abs(size1[0]-size2[0])//2
    diff1 = abs(size1[1]-size2[1])//2
    avg0 = (size1[0]+size2[0])//2
    avg1 = (size1[1]+size2[1])//2

    return [size1,size2,diff0,diff1,avg0,avg1]

def crop_image(img1,img2):
    [size1,size2,diff0,diff1,avg0,avg1] = calculate_margin_help(img1,img2)

    if(size1[0] == size2[0] and size1[1] == size2[1]):
        return [img1,img2]

    elif(size1[0] <= size2[0] and size1[1] <= size2[1]):
        scale0 = size1[0]/size2[0]
        scale1 = size1[1]/size2[1]
        if(scale0 > scale1):
            res = cv2.resize(img2,None,fx=scale0,fy=scale0,interpolation=cv2.INTER_AREA)
        else:
            res = cv2.resize(img2,None,fx=scale1,fy=scale1,interpolation=cv2.INTER_AREA)
        return crop_image_help(img1,res)

    elif(size1[0] >= size2[0] and size1[1] >= size2[1]):
        scale0 = size2[0]/size1[0]
        scale1 = size2[1]/size1[1]
        if(scale0 > scale1):
            res = cv2.resize(img1,None,fx=scale0,fy=scale0,interpolation=cv2.INTER_AREA)
        else:
            res = cv2.resize(img1,None,fx=scale1,fy=scale1,interpolation=cv2.INTER_AREA)
        return crop_image_help(res,img2)

    elif(size1[0] >= size2[0] and size1[1] <= size2[1]):
        return [img1[diff0:avg0,:],img2[:,-diff1:avg1]]

    else:
        return [img1[:,diff1:avg1],img2[-diff0:avg0,:]]

def crop_image_help(img1,img2):
    [size1,size2,diff0,diff1,avg0,avg1] = calculate_margin_help(img1,img2)

    if(size1[0] == size2[0] and size1[1] == size2[1]):
        return [img1,img2]

    elif(size1[0] <= size2[0] and size1[1] <= size2[1]):
        return [img1,img2[-diff0:avg0,-diff1:avg1]]

    elif(size1[0] >= size2[0] and size1[1] >= size2[1]):
        return [img1[diff0:avg0,diff1:avg1],img2]

    elif(size1[0] >= size2[0] and size1[1] <= size2[1]):
        return [img1[diff0:avg0,:],img2[:,-diff1:avg1]]

    else:
        return [img1[:,diff1:avg1],img2[diff0:avg0,:]]

def generate_face_correspondences(theImage1, theImage2):
    # Detect the points of face.
    detector = dlib.get_frontal_face_detector()
    predictor = dlib.shape_predictor('./models/shape_predictor_68_face_landmarks.dat')
    corresp = np.zeros((68,2))

    imgList = crop_image(theImage1,theImage2)
    list1 = []
    list2 = []
    j = 1

    for img in imgList:

        size = (img.shape[0],img.shape[1])
        if(j == 1):
            currList = list1
        else:
            currList = list2

        # Ask the detector to find the bounding boxes of each face. The 1 in the
        # second argument indicates that we should upsample the image 1 time. This
        # will make everything bigger and allow us to detect more faces.

        dets = detector(img, 1)

        try:
            if len(dets) == 0:
                raise NoFaceFound
        except NoFaceFound:
            print("Sorry, but I couldn't find a face in the image.")

        j=j+1

        for k, rect in enumerate(dets):

            # Get the landmarks/parts for the face in rect.
            shape = predictor(img, rect)
            # corresp = face_utils.shape_to_np(shape)

            for i in range(0,68):
                x = shape.part(i).x
                y = shape.part(i).y
                currList.append((x, y))
                corresp[i][0] += x
                corresp[i][1] += y
                # cv2.circle(img, (x, y), 2, (0, 255, 0), 2)

            # Add back the background
            currList.append((1,1))
            currList.append((size[1]-1,1))
            currList.append(((size[1]-1)//2,1))
            currList.append((1,size[0]-1))
            currList.append((1,(size[0]-1)//2))
            currList.append(((size[1]-1)//2,size[0]-1))
            currList.append((size[1]-1,size[0]-1))
            currList.append(((size[1]-1),(size[0]-1)//2))

    # Add back the background
    narray = corresp/2
    narray = np.append(narray,[[1,1]],axis=0)
    narray = np.append(narray,[[size[1]-1,1]],axis=0)
    narray = np.append(narray,[[(size[1]-1)//2,1]],axis=0)
    narray = np.append(narray,[[1,size[0]-1]],axis=0)
    narray = np.append(narray,[[1,(size[0]-1)//2]],axis=0)
    narray = np.append(narray,[[(size[1]-1)//2,size[0]-1]],axis=0)
    narray = np.append(narray,[[size[1]-1,size[0]-1]],axis=0)
    narray = np.append(narray,[[(size[1]-1),(size[0]-1)//2]],axis=0)

    return [size,imgList[0],imgList[1],list1,list2,narray]




# Check if a point is inside a rectangle
def rect_contains(rect, point):

    if point[0] < rect[0]:
        return False
    elif point[1] < rect[1]:
        return False
    elif point[0] > rect[2]:
        return False
    elif point[1] > rect[3]:
        return False
    return True

# Write the delaunay triangles into a file
def draw_delaunay(f_w, f_h, subdiv, dictionary1):

    list4 = []

    triangleList = subdiv.getTriangleList()
    r = (0, 0, f_w, f_h)

    for t in triangleList :
        pt1 = (int(t[0]), int(t[1]))
        pt2 = (int(t[2]), int(t[3]))
        pt3 = (int(t[4]), int(t[5]))

        if rect_contains(r, pt1) and rect_contains(r, pt2) and rect_contains(r, pt3) :
            list4.append((dictionary1[pt1],dictionary1[pt2],dictionary1[pt3]))

    dictionary1 = {}
    return list4

def make_delaunay(f_w, f_h, theList, img1, img2):

    # Make a rectangle.
    rect = (0, 0, f_w, f_h)

    # Create an instance of Subdiv2D.
    subdiv = cv2.Subdiv2D(rect)

    # Make a points list and a searchable dictionary.
    theList = theList.tolist()
    points = [(int(x[0]),int(x[1])) for x in theList]
    dictionary = {x[0]:x[1] for x in list(zip(points, range(76)))}

    # Insert points into subdiv
    for p in points :
        subdiv.insert(p)

    # Make a delaunay triangulation list.
    list4 = draw_delaunay(f_w, f_h, subdiv, dictionary)

    # Return the list.
    return list4



import numpy as np
import cv2
import sys
import os
import math
from subprocess import Popen, PIPE
from PIL import Image

# Apply affine transform calculated using srcTri and dstTri to src and
# output an image of size.
def apply_affine_transform(src, srcTri, dstTri, size) :

    # Given a pair of triangles, find the affine transform.
    warpMat = cv2.getAffineTransform(np.float32(srcTri), np.float32(dstTri))

    # Apply the Affine Transform just found to the src image
    dst = cv2.warpAffine(src, warpMat, (size[0], size[1]), None, flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT_101)

    return dst


# Warps and alpha blends triangular regions from img1 and img2 to img
def morph_triangle(img1, img2, img, t1, t2, t, alpha) :

    # Find bounding rectangle for each triangle
    r1 = cv2.boundingRect(np.float32([t1]))
    r2 = cv2.boundingRect(np.float32([t2]))
    r = cv2.boundingRect(np.float32([t]))

    # Offset points by left top corner of the respective rectangles
    t1Rect = []
    t2Rect = []
    tRect = []

    for i in range(0, 3):
        tRect.append(((t[i][0] - r[0]),(t[i][1] - r[1])))
        t1Rect.append(((t1[i][0] - r1[0]),(t1[i][1] - r1[1])))
        t2Rect.append(((t2[i][0] - r2[0]),(t2[i][1] - r2[1])))

    # Get mask by filling triangle
    mask = np.zeros((r[3], r[2], 3), dtype = np.float32)
    cv2.fillConvexPoly(mask, np.int32(tRect), (1.0, 1.0, 1.0), 16, 0)

    # Apply warpImage to small rectangular patches
    img1Rect = img1[r1[1]:r1[1] + r1[3], r1[0]:r1[0] + r1[2]]
    img2Rect = img2[r2[1]:r2[1] + r2[3], r2[0]:r2[0] + r2[2]]

    size = (r[2], r[3])
    warpImage1 = apply_affine_transform(img1Rect, t1Rect, tRect, size)
    warpImage2 = apply_affine_transform(img2Rect, t2Rect, tRect, size)

    # Alpha blend rectangular patches
    imgRect = (1.0 - alpha) * warpImage1 + alpha * warpImage2

    # Copy triangular region of the rectangular patch to the output image
    img[r[1]:r[1]+r[3], r[0]:r[0]+r[2]] = img[r[1]:r[1]+r[3], r[0]:r[0]+r[2]] * ( 1 - mask ) + imgRect * mask


def generate_morph_sequence(duration, frame_rate, img1, img2, points1, points2, tri_list, size, output):
    num_images = int(duration * frame_rate)
    p = subprocess.Popen(['ffmpeg', '-y', '-f', 'image2pipe', '-r', str(frame_rate), '-s', str(size[1])+'x'+str(size[0]), '-i', '-', '-c:v', 'libx264', '-crf', '25', '-vf', 'scale=trunc(iw/2)*2:trunc(ih/2)*2', '-pix_fmt', 'yuv420p', output], stdin=subprocess.PIPE)

    for _ in range(10):#(int(frame_rate/3)):
        res = Image.fromarray(cv2.cvtColor(np.uint8(img1), cv2.COLOR_BGR2RGB))
        res.save(p.stdin, 'JPEG')

    for j in range(0, num_images):
        img1 = np.float32(img1)
        img2 = np.float32(img2)
        points = []
        alpha = j / (num_images - 1)

        for i in range(0, len(points1)):
            x = (1 - alpha) * points1[i][0] + alpha * points2[i][0]
            y = (1 - alpha) * points1[i][1] + alpha * points2[i][1]
            points.append((x, y))

        morphed_frame = np.zeros(img1.shape, dtype=img1.dtype)

        for i in range(len(tri_list)):
            x = int(tri_list[i][0])
            y = int(tri_list[i][1])
            z = int(tri_list[i][2])

            t1 = [points1[x], points1[y], points1[z]]
            t2 = [points2[x], points2[y], points2[z]]
            t = [points[x], points[y], points[z]]

            morph_triangle(img1, img2, morphed_frame, t1, t2, t, alpha)

        res = Image.fromarray(cv2.cvtColor(np.uint8(morphed_frame), cv2.COLOR_BGR2RGB))
        res.save(p.stdin, 'JPEG')

    p.stdin.close()
    p.wait()



# def doMorphing(image_paths, duration, frame_rate, output):
#     output_files = []
#     to_delete = []
#     for i in range(len(image_paths) - 1):
#         img1 = cv2.imread(image_paths[i])
#         img2 = cv2.imread(image_paths[i + 1])
#         size, img1, img2, points1, points2, list3 = generate_face_correspondences(img1, img2)
#         tri = make_delaunay(size[1], size[0], list3, img1, img2)
#         output_file = f"{output}_{i}.mp4"
#         to_delete.append(output_file)
#         generate_morph_sequence(duration, frame_rate, img1, img2, points1, points2, tri, size, output_file)
#         output_files.append(output_file)

#     # Concatenate videos into one
#     ffmpeg_command = ['ffmpeg', '-y', '-f', 'concat', '-safe', '0', '-i', 'files.txt', '-c', 'copy', f"{output}_combined.mp4"]
#     with open(f'files.txt', 'w') as f:
#         for file in output_files:
#             f.write(f"file '{file}'\n")
#     subprocess.run(ffmpeg_command)
#     os.remove(f'files.txt')

#     # Convert the final combined video to a GIF
#     gif_command = [
#         'ffmpeg', '-y', '-i', f"{output}_combined.mp4", '-vf', 'fps=10,scale=320:-1:flags=lanczos,palettegen', f'{output}_palette.png'
#     ]
#     subprocess.run(gif_command)

#     gif_command = [
#         'ffmpeg', '-y', '-i', f"{output}_combined.mp4", '-i', f'{output}_palette.png', '-filter_complex',
#         'fps=10,scale=320:-1:flags=lanczos[x];[x][1:v]paletteuse', f'{output}.gif'
#     ]
#     subprocess.run(gif_command)
#     os.remove(f'{output}_palette.png')
#     for file_delete in to_delete:
#         os.remove(file_delete)


def doMorphing(image_paths, duration, frame_rate, output):
    output_files = []
    for i in range(len(image_paths) - 1):
        img1 = cv2.imread(image_paths[i])
        img2 = cv2.imread(image_paths[i + 1])
        size, img1, img2, points1, points2, list3 = generate_face_correspondences(img1, img2)
        tri = make_delaunay(size[1], size[0], list3, img1, img2)
        output_file = f"{output}_{i}.mp4"
        generate_morph_sequence(duration, frame_rate, img1, img2, points1, points2, tri, size, output_file)
        output_files.append(output_file)

    # Concatenate videos into one
    ffmpeg_command = ['ffmpeg', '-y', '-f', 'concat', '-safe', '0', '-i', 'files.txt', '-c', 'copy', f"{output}_combined.mp4"]
    with open(f'files.txt', 'w') as f:
        for file in output_files:
            f.write(f"file '{file}'\n")
    subprocess.run(ffmpeg_command)
    os.remove(f'files.txt')

    # Convert the final combined video to a GIF
    gif_command = [
        'ffmpeg', '-y', '-i', f"{output}_combined.mp4", '-vf', 'fps=10,scale=600:-1:flags=lanczos,palettegen', f'{output}_palette.png'
    ]
    subprocess.run(gif_command)

    gif_command = [
        'ffmpeg', '-y', '-i', f"{output}_combined.mp4", '-i', f'{output}_palette.png', '-filter_complex',
        'fps=10,scale=320:-1:flags=lanczos[x];[x][1:v]paletteuse', f'{output}.gif'
    ]
    subprocess.run(gif_command)
    os.remove(f'{output}_palette.png')

	import sys
	import os
	import dlib
	import numpy as np
	from skimage import io
	import cv2
	from imutils import face_utils
	import argparse
	import shutil
	import random

	import subprocess


	class NoFaceFound(Exception):
	"""Raised when there is no face found"""
	pass

	def calculate_margin_help(img1,img2):
	size1 = img1.shape
	size2 = img2.shape
	diff0 = abs(size1[0]-size2[0])//2
	diff1 = abs(size1[1]-size2[1])//2
	avg0 = (size1[0]+size2[0])//2
	avg1 = (size1[1]+size2[1])//2

	return [size1,size2,diff0,diff1,avg0,avg1]

	def crop_image(img1,img2):
	[size1,size2,diff0,diff1,avg0,avg1] = calculate_margin_help(img1,img2)

	if(size1[0] == size2[0] and size1[1] == size2[1]):
	return [img1,img2]

	elif(size1[0] <= size2[0] and size1[1] <= size2[1]):
	scale0 = size1[0]/size2[0]
	scale1 = size1[1]/size2[1]
	if(scale0 > scale1):
	res = cv2.resize(img2,None,fx=scale0,fy=scale0,interpolation=cv2.INTER_AREA)
	else:
	res = cv2.resize(img2,None,fx=scale1,fy=scale1,interpolation=cv2.INTER_AREA)
	return crop_image_help(img1,res)

	elif(size1[0] >= size2[0] and size1[1] >= size2[1]):
	scale0 = size2[0]/size1[0]
	scale1 = size2[1]/size1[1]
	if(scale0 > scale1):
	res = cv2.resize(img1,None,fx=scale0,fy=scale0,interpolation=cv2.INTER_AREA)
	else:
	res = cv2.resize(img1,None,fx=scale1,fy=scale1,interpolation=cv2.INTER_AREA)
	return crop_image_help(res,img2)

	elif(size1[0] >= size2[0] and size1[1] <= size2[1]):
	return [img1[diff0:avg0,:],img2[:,-diff1:avg1]]

	else:
	return [img1[:,diff1:avg1],img2[-diff0:avg0,:]]

	def crop_image_help(img1,img2):
	[size1,size2,diff0,diff1,avg0,avg1] = calculate_margin_help(img1,img2)

	if(size1[0] == size2[0] and size1[1] == size2[1]):
	return [img1,img2]

	elif(size1[0] <= size2[0] and size1[1] <= size2[1]):
	return [img1,img2[-diff0:avg0,-diff1:avg1]]

	elif(size1[0] >= size2[0] and size1[1] >= size2[1]):
	return [img1[diff0:avg0,diff1:avg1],img2]

	elif(size1[0] >= size2[0] and size1[1] <= size2[1]):
	return [img1[diff0:avg0,:],img2[:,-diff1:avg1]]

	else:
	return [img1[:,diff1:avg1],img2[diff0:avg0,:]]

	def generate_face_correspondences(theImage1, theImage2):
	# Detect the points of face.
	detector = dlib.get_frontal_face_detector()
	predictor = dlib.shape_predictor('./models/shape_predictor_68_face_landmarks.dat')
	corresp = np.zeros((68,2))

	imgList = crop_image(theImage1,theImage2)
	list1 = []
	list2 = []
	j = 1

	for img in imgList:

	size = (img.shape[0],img.shape[1])
	if(j == 1):
	currList = list1
	else:
	currList = list2

	# Ask the detector to find the bounding boxes of each face. The 1 in the
	# second argument indicates that we should upsample the image 1 time. This
	# will make everything bigger and allow us to detect more faces.

	dets = detector(img, 1)

	try:
	if len(dets) == 0:
	raise NoFaceFound
	except NoFaceFound:
	print("Sorry, but I couldn't find a face in the image.")

	j=j+1

	for k, rect in enumerate(dets):

	# Get the landmarks/parts for the face in rect.
	shape = predictor(img, rect)
	# corresp = face_utils.shape_to_np(shape)

	for i in range(0,68):
	x = shape.part(i).x
	y = shape.part(i).y
	currList.append((x, y))
	corresp[i][0] += x
	corresp[i][1] += y
	# cv2.circle(img, (x, y), 2, (0, 255, 0), 2)

	# Add back the background
	currList.append((1,1))
	currList.append((size[1]-1,1))
	currList.append(((size[1]-1)//2,1))
	currList.append((1,size[0]-1))
	currList.append((1,(size[0]-1)//2))
	currList.append(((size[1]-1)//2,size[0]-1))
	currList.append((size[1]-1,size[0]-1))
	currList.append(((size[1]-1),(size[0]-1)//2))

	# Add back the background
	narray = corresp/2
	narray = np.append(narray,[[1,1]],axis=0)
	narray = np.append(narray,[[size[1]-1,1]],axis=0)
	narray = np.append(narray,[[(size[1]-1)//2,1]],axis=0)
	narray = np.append(narray,[[1,size[0]-1]],axis=0)
	narray = np.append(narray,[[1,(size[0]-1)//2]],axis=0)
	narray = np.append(narray,[[(size[1]-1)//2,size[0]-1]],axis=0)
	narray = np.append(narray,[[size[1]-1,size[0]-1]],axis=0)
	narray = np.append(narray,[[(size[1]-1),(size[0]-1)//2]],axis=0)

	return [size,imgList[0],imgList[1],list1,list2,narray]




	# Check if a point is inside a rectangle
	def rect_contains(rect, point):

	if point[0] < rect[0]:
	return False
	elif point[1] < rect[1]:
	return False
	elif point[0] > rect[2]:
	return False
	elif point[1] > rect[3]:
	return False
	return True

	# Write the delaunay triangles into a file
	def draw_delaunay(f_w, f_h, subdiv, dictionary1):

	list4 = []

	triangleList = subdiv.getTriangleList()
	r = (0, 0, f_w, f_h)

	for t in triangleList :
	pt1 = (int(t[0]), int(t[1]))
	pt2 = (int(t[2]), int(t[3]))
	pt3 = (int(t[4]), int(t[5]))

	if rect_contains(r, pt1) and rect_contains(r, pt2) and rect_contains(r, pt3) :
	list4.append((dictionary1[pt1],dictionary1[pt2],dictionary1[pt3]))

	dictionary1 = {}
	return list4

	def make_delaunay(f_w, f_h, theList, img1, img2):

	# Make a rectangle.
	rect = (0, 0, f_w, f_h)

	# Create an instance of Subdiv2D.
	subdiv = cv2.Subdiv2D(rect)

	# Make a points list and a searchable dictionary.
	theList = theList.tolist()
	points = [(int(x[0]),int(x[1])) for x in theList]
	dictionary = {x[0]:x[1] for x in list(zip(points, range(76)))}

	# Insert points into subdiv
	for p in points :
	subdiv.insert(p)

	# Make a delaunay triangulation list.
	list4 = draw_delaunay(f_w, f_h, subdiv, dictionary)

	# Return the list.
	return list4



	import numpy as np
	import cv2
	import sys
	import os
	import math
	from subprocess import Popen, PIPE
	from PIL import Image

	# Apply affine transform calculated using srcTri and dstTri to src and
	# output an image of size.
	def apply_affine_transform(src, srcTri, dstTri, size) :

	# Given a pair of triangles, find the affine transform.
	warpMat = cv2.getAffineTransform(np.float32(srcTri), np.float32(dstTri))

	# Apply the Affine Transform just found to the src image
	dst = cv2.warpAffine(src, warpMat, (size[0], size[1]), None, flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT_101)

	return dst


	# Warps and alpha blends triangular regions from img1 and img2 to img
	def morph_triangle(img1, img2, img, t1, t2, t, alpha) :

	# Find bounding rectangle for each triangle
	r1 = cv2.boundingRect(np.float32([t1]))
	r2 = cv2.boundingRect(np.float32([t2]))
	r = cv2.boundingRect(np.float32([t]))

	# Offset points by left top corner of the respective rectangles
	t1Rect = []
	t2Rect = []
	tRect = []

	for i in range(0, 3):
	tRect.append(((t[i][0] - r[0]),(t[i][1] - r[1])))
	t1Rect.append(((t1[i][0] - r1[0]),(t1[i][1] - r1[1])))
	t2Rect.append(((t2[i][0] - r2[0]),(t2[i][1] - r2[1])))

	# Get mask by filling triangle
	mask = np.zeros((r[3], r[2], 3), dtype = np.float32)
	cv2.fillConvexPoly(mask, np.int32(tRect), (1.0, 1.0, 1.0), 16, 0)

	# Apply warpImage to small rectangular patches
	img1Rect = img1[r1[1]:r1[1] + r1[3], r1[0]:r1[0] + r1[2]]
	img2Rect = img2[r2[1]:r2[1] + r2[3], r2[0]:r2[0] + r2[2]]

	size = (r[2], r[3])
	warpImage1 = apply_affine_transform(img1Rect, t1Rect, tRect, size)
	warpImage2 = apply_affine_transform(img2Rect, t2Rect, tRect, size)

	# Alpha blend rectangular patches
	imgRect = (1.0 - alpha) * warpImage1 + alpha * warpImage2

	# Copy triangular region of the rectangular patch to the output image
	img[r[1]:r[1]+r[3], r[0]:r[0]+r[2]] = img[r[1]:r[1]+r[3], r[0]:r[0]+r[2]] * ( 1 - mask ) + imgRect * mask


	def generate_morph_sequence(duration, frame_rate, img1, img2, points1, points2, tri_list, size, output):
	num_images = int(duration * frame_rate)
	p = subprocess.Popen(['ffmpeg', '-y', '-f', 'image2pipe', '-r', str(frame_rate), '-s', str(size[1])+'x'+str(size[0]), '-i', '-', '-c:v', 'libx264', '-crf', '25', '-vf', 'scale=trunc(iw/2)2:trunc(ih/2)2', '-pix_fmt', 'yuv420p', output], stdin=subprocess.PIPE)

	for _ in range(10):#(int(frame_rate/3)):
	res = Image.fromarray(cv2.cvtColor(np.uint8(img1), cv2.COLOR_BGR2RGB))
	res.save(p.stdin, 'JPEG')

	for j in range(0, num_images):
	img1 = np.float32(img1)
	img2 = np.float32(img2)
	points = []
	alpha = j / (num_images - 1)

	for i in range(0, len(points1)):
	x = (1 - alpha) * points1[i][0] + alpha * points2[i][0]
	y = (1 - alpha) * points1[i][1] + alpha * points2[i][1]
	points.append((x, y))

	morphed_frame = np.zeros(img1.shape, dtype=img1.dtype)

	for i in range(len(tri_list)):
	x = int(tri_list[i][0])
	y = int(tri_list[i][1])
	z = int(tri_list[i][2])

	t1 = [points1[x], points1[y], points1[z]]
	t2 = [points2[x], points2[y], points2[z]]
	t = [points[x], points[y], points[z]]

	morph_triangle(img1, img2, morphed_frame, t1, t2, t, alpha)

	res = Image.fromarray(cv2.cvtColor(np.uint8(morphed_frame), cv2.COLOR_BGR2RGB))
	res.save(p.stdin, 'JPEG')

	p.stdin.close()
	p.wait()



	# def doMorphing(image_paths, duration, frame_rate, output):
	# output_files = []
	# to_delete = []
	# for i in range(len(image_paths) - 1):
	# img1 = cv2.imread(image_paths[i])
	# img2 = cv2.imread(image_paths[i + 1])
	# size, img1, img2, points1, points2, list3 = generate_face_correspondences(img1, img2)
	# tri = make_delaunay(size[1], size[0], list3, img1, img2)
	# output_file = f"{output}_{i}.mp4"
	# to_delete.append(output_file)
	# generate_morph_sequence(duration, frame_rate, img1, img2, points1, points2, tri, size, output_file)
	# output_files.append(output_file)

	# # Concatenate videos into one
	# ffmpeg_command = ['ffmpeg', '-y', '-f', 'concat', '-safe', '0', '-i', 'files.txt', '-c', 'copy', f"{output}_combined.mp4"]
	# with open(f'files.txt', 'w') as f:
	# for file in output_files:
	# f.write(f"file '{file}'\n")
	# subprocess.run(ffmpeg_command)
	# os.remove(f'files.txt')

	# # Convert the final combined video to a GIF
	# gif_command = [
	# 'ffmpeg', '-y', '-i', f"{output}_combined.mp4", '-vf', 'fps=10,scale=320:-1:flags=lanczos,palettegen', f'{output}_palette.png'
	# ]
	# subprocess.run(gif_command)

	# gif_command = [
	# 'ffmpeg', '-y', '-i', f"{output}_combined.mp4", '-i', f'{output}_palette.png', '-filter_complex',
	# 'fps=10,scale=320:-1:flags=lanczos[x];[x][1:v]paletteuse', f'{output}.gif'
	# ]
	# subprocess.run(gif_command)
	# os.remove(f'{output}_palette.png')
	# for file_delete in to_delete:
	# os.remove(file_delete)


	def doMorphing(image_paths, duration, frame_rate, output):
	output_files = []
	for i in range(len(image_paths) - 1):
	img1 = cv2.imread(image_paths[i])
	img2 = cv2.imread(image_paths[i + 1])
	size, img1, img2, points1, points2, list3 = generate_face_correspondences(img1, img2)
	tri = make_delaunay(size[1], size[0], list3, img1, img2)
	output_file = f"{output}_{i}.mp4"
	generate_morph_sequence(duration, frame_rate, img1, img2, points1, points2, tri, size, output_file)
	output_files.append(output_file)

	# Concatenate videos into one
	ffmpeg_command = ['ffmpeg', '-y', '-f', 'concat', '-safe', '0', '-i', 'files.txt', '-c', 'copy', f"{output}_combined.mp4"]
	with open(f'files.txt', 'w') as f:
	for file in output_files:
	f.write(f"file '{file}'\n")
	subprocess.run(ffmpeg_command)
	os.remove(f'files.txt')

	# Convert the final combined video to a GIF
	gif_command = [
	'ffmpeg', '-y', '-i', f"{output}_combined.mp4", '-vf', 'fps=10,scale=600:-1:flags=lanczos,palettegen', f'{output}_palette.png'
	]
	subprocess.run(gif_command)

	gif_command = [
	'ffmpeg', '-y', '-i', f"{output}_combined.mp4", '-i', f'{output}_palette.png', '-filter_complex',
	'fps=10,scale=320:-1:flags=lanczos[x];[x][1:v]paletteuse', f'{output}.gif'
	]
	subprocess.run(gif_command)
	os.remove(f'{output}_palette.png')