Skip to content

cleanup, use vectorisation #2

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
TBourton wants to merge 1 commit into
base: main
Choose a base branch
from
Open

cleanup, use vectorisation #2

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
172 changes: 49 additions & 123 deletions src/vid_to_deepframes_rawframes.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,148 +1,74 @@

import numpy as np
import os
import cv2



import cv2
import numpy as np

image_path = 'D:\\Pattern_Letters_HR_PAD\\BBDD\\3DMAD\\session03\\'
image_name_video = []
# Load the cascade
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

for f in [f for f in os.listdir(image_path)]:
if not("_C.avi" in f): #OULU

if not("_C.avi" in f): # OULU
continue
carpeta= os.path.join(image_path, f)

carpeta = os.path.join(image_path, f)
cap = cv2.VideoCapture(carpeta)
frame_rate = cap.get(cv2.CAP_PROP_FPS)
nFrames = cap.get(7)
max_frames = int(nFrames)
ruta_parcial = os.path.join('D:\\Pattern_Letters_HR_PAD\\BBDD\\3DMAD\\DeepFrames',f)
if not(os.path.exists(ruta_parcial)) :
os.mkdir(ruta_parcial);
ruta_parcial2 = os.path.join('D:\\Pattern_Letters_HR_PAD\\BBDD\\3DMAD\\RawFrames',f)
if not(os.path.exists(ruta_parcial2)) :
os.mkdir(ruta_parcial2);
ruta_parcial = os.path.join('D:\\Pattern_Letters_HR_PAD\\BBDD\\3DMAD\\DeepFrames',f)
if not(os.path.exists(ruta_parcial)):
os.mkdir(ruta_parcial)
ruta_parcial2 = os.path.join('D:\\Pattern_Letters_HR_PAD\\BBDD\\3DMAD\\RawFrames',f)
if not(os.path.exists(ruta_parcial2)):
os.mkdir(ruta_parcial2)

L = 36
C_R=np.empty((L,L,max_frames))
C_G=np.empty((L,L,max_frames))
C_B=np.empty((L,L,max_frames))

D_R=np.empty((L,L,max_frames))
D_G=np.empty((L,L,max_frames))
D_B=np.empty((L,L,max_frames))

D_R2=np.empty((L,L,max_frames))
D_G2=np.empty((L,L,max_frames))
D_B2=np.empty((L,L,max_frames))

medias_R = np.empty((L,L))
medias_G = np.empty((L,L))
medias_B = np.empty((L,L))

desviaciones_R = np.empty((L,L))
desviaciones_G = np.empty((L,L))
desviaciones_B = np.empty((L,L))

imagen = np.empty((L,L,3))

medias_CR = np.empty((L,L))
medias_CG = np.empty((L,L))
medias_CB = np.empty((L,L))

desviaciones_CR = np.empty((L,L))
desviaciones_CG = np.empty((L,L))
desviaciones_CB = np.empty((L,L))
ka = 1


while(cap.isOpened() and ka< max_frames):
C = []
ka = 1

while (cap.isOpened() and ka < max_frames):
ret, frame = cap.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# Detect faces
faces = face_cascade.detectMultiScale(gray, 1.1, 4)
#rectangle around the faces
# rectangle around the faces
for (x, y, w, h) in faces:
# face = cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
face = frame[y:y + h, x:x + w]


face = cv2.resize(face, (L,L), interpolation = cv2.INTER_AREA)
# cv2.imshow('img', face)
# cv2.waitKey()
C_R[:,:,ka] = face[:,:,0]
C_G[:,:,ka] = face[:,:,1]
C_B[:,:,ka] = face[:,:,2]


if ka > 1:
D_R[:,:,ka-1] = ( C_R[:,:,ka] - C_R[:,:,ka-1] ) / ( C_R[:,:,ka] + C_R[:,:,ka-1] );
D_G[:,:,ka-1] = ( C_G[:,:,ka] - C_G[:,:,ka-1] ) / ( C_G[:,:,ka] + C_G[:,:,ka-1] );
D_B[:,:,ka-1] = ( C_B[:,:,ka] - C_B[:,:,ka-1] ) / ( C_B[:,:,ka] + C_B[:,:,ka-1] );
ka = ka+1



for i in range(0,L):
for j in range(0,L):
medias_R[i,j]=np.mean(D_R[i,j,:])
medias_G[i,j]=np.mean(D_G[i,j,:])
medias_B[i,j]=np.mean(D_B[i,j,:])
desviaciones_R[i,j]=np.std(D_R[i,j,:])
desviaciones_G[i,j]=np.std(D_G[i,j,:])
desviaciones_B[i,j]=np.std(D_B[i,j,:])

for i in range(0,L):
for j in range(0,L):
medias_CR[i,j]=np.mean(C_R[i,j,:])
medias_CG[i,j]=np.mean(C_G[i,j,:])
medias_CB[i,j]=np.mean(C_B[i,j,:])
desviaciones_CR[i,j]=np.std(C_R[i,j,:])
desviaciones_CG[i,j]=np.std(C_G[i,j,:])
desviaciones_CB[i,j]=np.std(C_B[i,j,:])

for k in range(0,max_frames):
D_R2[:,:,k] = (C_R[:,:,k] - medias_CR)/(desviaciones_CR+000.1)
D_G2[:,:,k] = (C_G[:,:,k] - medias_CG)/(desviaciones_CG+000.1)
D_B2[:,:,k] = (C_B[:,:,k] - medias_CB)/(desviaciones_CB+000.1)



for k in range(0,max_frames):

imagen[:,:,0] = D_R2[:,:,k]
imagen[:,:,1] = D_G2[:,:,k]
imagen[:,:,2] = D_B2[:,:,k]

imagen= np.uint8(imagen)

nombre_salvar= os.path.join(ruta_parcial2,str(k)+'.png')
cv2.imwrite(nombre_salvar, imagen)


for k in range(0,max_frames):

D_R[:,:,k] = (D_R[:,:,k] - medias_R)/(desviaciones_R+000.1)
D_G[:,:,k] = (D_G[:,:,k] - medias_G)/(desviaciones_G+000.1)
D_B[:,:,k] = (D_B[:,:,k] - medias_B)/(desviaciones_B+000.1)

for k in range(0,max_frames):

imagen[:,:,0] = D_R[:,:,k]
imagen[:,:,1] = D_G[:,:,k]
imagen[:,:,2] = D_B[:,:,k]

imagen= np.uint8(imagen)

nombre_salvar= os.path.join(ruta_parcial,str(k)+'.png')
cv2.imwrite(nombre_salvar, imagen)



face = cv2.resize(face, (L, L), interpolation=cv2.INTER_AREA)
C.append(face)
ka += 1

cap.release()
cv2.destroyAllWindows()

C = np.array(C, dtype=np.float64)

epsilon = 0.1

D = np.diff(C, axis=0) / (C[:-1] + C[1:])
medias_D = np.mean(D, axis=0)
desviaciones_D = np.std(D, axis=0)
D = (D - medias_D) / (desviaciones_D + epsilon)
D = np.uint8(D)

for k, imagen in enumerate(D):
nombre_salvar = os.path.join(ruta_parcial, str(k + 1) + '.png')
cv2.imwrite(nombre_salvar, imagen)

medias_C = np.mean(C, axis=0)
desviaciones_C = np.std(C, axis=0)
C = (C - medias_C) / (desviaciones_C + epsilon)
C = C[:-1]
C = np.uint8(C)

for k, imagen in enumerate(C):
nombre_salvar = os.path.join(ruta_parcial2, str(k + 1) + '.png')
cv2.imwrite(nombre_salvar, imagen)


print("Exiting...")