diff --git a/__pycache__/imageProcess.cpython-38.pyc b/__pycache__/imageProcess.cpython-38.pyc
index 421798d2bd14fb0373f5881855f092994202292c..781d95caa203cb8b26f3f98f4c0cff872bc74a59 100644
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diff --git a/__pycache__/loadAffwild.cpython-38.pyc b/__pycache__/loadAffwild.cpython-38.pyc
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index 0000000000000000000000000000000000000000..11042780dc77eb84fc05ad784113bfa993167f37
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diff --git a/__pycache__/loadExpWDS.cpython-38.pyc b/__pycache__/loadExpWDS.cpython-38.pyc
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diff --git a/__pycache__/loadFer2013ds.cpython-38.pyc b/__pycache__/loadFer2013ds.cpython-38.pyc
index 08fa3949ccfa1ea775d20f33b432ec244d74ec5e..c01c5f18e516716bf499d2202d506dd2c2a25905 100644
Binary files a/__pycache__/loadFer2013ds.cpython-38.pyc and b/__pycache__/loadFer2013ds.cpython-38.pyc differ
diff --git a/__pycache__/loadRavdessDs.cpython-38.pyc b/__pycache__/loadRavdessDs.cpython-38.pyc
index e58c4aa7cbfe2ee2e1fbb4fa4c064806e15caa57..dee7ea69bad7a07207953feec090113a673aaaa8 100644
Binary files a/__pycache__/loadRavdessDs.cpython-38.pyc and b/__pycache__/loadRavdessDs.cpython-38.pyc differ
diff --git a/imageProcess.py b/imageProcess.py
index cba699ee0404a2f84d91f848d05e27a089659ea1..6719ce170e96bffe4b3909d81fcfda046ec69b7b 100644
--- a/imageProcess.py
+++ b/imageProcess.py
@@ -29,11 +29,6 @@ def imageProcess(image, writeEmotion=True):
face_color = image[y:y+h, x:x+w]
facesList.append(face_color)
- #Detect eyes on the face, create green rectangle
- eyes = eye_cascade.detectMultiScale(face_gray)
- for (ex,ey,ew,eh) in eyes:
- cv2.rectangle(face_color,(ex,ey),(ex+ew,ey+eh),(0,255,0),1)
-
#Write emotion on the image
if writeEmotion:
emotion = fa.detectEmotion(face_color)
diff --git a/loadAffwild.py b/loadAffwild.py
new file mode 100644
index 0000000000000000000000000000000000000000..c50f06902252db18cc8ad998794b83b3d0dd5053
--- /dev/null
+++ b/loadAffwild.py
@@ -0,0 +1,125 @@
+import os
+import cv2
+from utils import *
+import imageProcess as ip
+from config import input_shape
+
+
+def extractDataFromVideo_(filename, videoName, facesList, labelsList, maxNbrImages):
+ # Extract every faces in a specified video and add it to a list of faces as well as labels corresponding.
+ frameRate = 1/15
+ emotions = ["Neutral", "Angry", "Disgust", "Fear", "Happy", "Sad", "Suprise"]
+
+ # Start capture of a video and reading of a label file
+ cap = cv2.VideoCapture(filename)
+ if (cap.isOpened() == False):
+ print("Error opening video")
+
+ file = open("data/affwild/labels/"+videoName[:-4]+'.txt', 'r')
+ file.readline()
+
+ # Read until video is completed
+ k = 0
+ while (cap.isOpened()):
+ # Capture frame-by-frame
+ ret, frame = cap.read()
+ line = file.readline()
+
+ if ret == True:
+ k += 1
+
+ if k*frameRate >= 1: # Read a frame each N frames where N=1/frameRate
+ k = 0
+
+ # Load image and labels
+
+ #Detect faces on the image
+ newFaces = ip.imageProcess(frame, writeEmotion=False)
+
+ #If 2 faces were detected, it means an error was made since there is only single-person videos here.
+ if len(newFaces) == 1:
+ facesList += newFaces
+
+ emotionNbr = emotionToNumber(emotions[int(line[0])])
+ labelsList.append(emotionNbr)
+ print("Donnée ajoutée, donnée :", len(facesList))
+ elif True: print("Erreur pour la donnée : Aucun ou plusieurs visages détectés")
+
+ # If we overreach the maximum number of images desired, stop
+ if len(facesList) > maxNbrImages:
+ break
+
+ # Press Q on keyboard to exit
+ if cv2.waitKey(1) & 0xFF == ord('q'):
+ break
+
+ # Display the resulting frame
+ if False:
+ cv2.imshow('AffWild data extraction...', frame)
+
+ # Break the loop
+ else:
+ break
+
+ # When everything done, release the video capture object
+ cap.release()
+
+ # Closes all the frames
+ cv2.destroyAllWindows()
+
+ #Close file
+ file.close()
+
+ # Return face and label lists with new datas
+ return facesList, labelsList
+
+
+
+
+# LOAD DATA
+
+def loadAffwildData(maxNbrImages=10000000000):
+ print(f"\nCHARGEMENT DE {maxNbrImages} DONNEES DEPUIS AFFWILD...")
+
+ foldername = "data/affwild/videos/"
+ facesList = []
+ labelsList = []
+ k = 1
+ nbrOfVideos = len(os.listdir(foldername))
+
+ # For each video...
+ for videoName in os.listdir(foldername):
+
+ # If we overreach the maximum number of images desired, stop
+ if len(facesList) >= maxNbrImages:
+ break
+
+ elif videoName+'_left' in os.listdir("data/affwild/labels") or videoName+'_right' in os.listdir("data/affwild/labels"):
+ print("Vidéo à deux visages, non pris en compte")
+
+ else:
+ k+=1
+ print(f"Traitement de {videoName}, video {k}/{nbrOfVideos}")
+ filename = foldername+videoName
+
+ # Press Q on keyboard to exit ONE video
+ if cv2.waitKey(1) & 0xFF == ord('q'):
+ break
+
+ #Add datas extracted from the specified video to features and labels
+ facesList, labelsList = extractDataFromVideo_(
+ filename, videoName, facesList, labelsList, maxNbrImages)
+
+ # List of colored images N*M*3 faces to array of gray images 48*48*1
+ N = len(facesList)
+ print(f"TRAITEMENT AFFWILD: traitement des {N} visages détectés sur les vidéos de AffWild...")
+
+ for k in range(N):
+ visage = facesList[k]
+ facesList[k] = normAndResize(visage, input_shape)
+ X = np.array(facesList)
+
+ Y = np.array(labelsList)
+
+ print(N, "données chargées depuis AffWild.")
+ return X, Y
diff --git a/loadExpWDS.py b/loadExpWDS.py
new file mode 100644
index 0000000000000000000000000000000000000000..f146b528de4268c6c472e49fac9b3f63dbf9dfa1
--- /dev/null
+++ b/loadExpWDS.py
@@ -0,0 +1,65 @@
+import cv2
+from utils import *
+from config import input_shape
+import imageProcess as ip
+import numpy as np
+
+
+def loadExpWData(nbrMaxImages=float('inf'), onlyDetected=False):
+ print(f"\nCHARGEMENT DE {nbrMaxImages} DONNEES DEPUIS EXPW...")
+ folderImages = 'data/expW/images/'
+ fileLabels = 'data/expW/labels.lst'
+
+ file = open(fileLabels, 'r')
+ nbrImages = 0
+ k = 0
+ X = []
+ Y = []
+
+ for line in file:
+ if nbrImages>=nbrMaxImages: break
+ k+= 1
+
+ #Face extraction, according to the dataset annotations AND the face detector (cascade)
+ imageName, Id, top, left, right, bottom, cofidence, label = line.strip().split(' ')
+ image = cv2.imread(folderImages+imageName)
+ faceAccordingToDS = image[int(top):int(bottom), int(left):int(right)]
+ facesDetected = ip.imageProcess(faceAccordingToDS, writeEmotion=False)
+
+ #Suivi visuel (facultatif, fait un peu peur sans attendre 1000ms entre deux images...)
+ if False:
+ cv2.imshow("ExpW importation...", faceAccordingToDS)
+ if cv2.waitKey(1000) & 0xFF == ord('q'):
+ break
+
+ #Add extracted data to our dataset
+ if len(facesDetected) == 1 or not onlyDetected: #Otherwise no face were detected or a no-face was detected as face
+
+ #Select in priority image detected by detector
+ if len(facesDetected) != 0:
+ face = facesDetected[0]
+ else:
+ face = faceAccordingToDS
+
+ #Colored N*M*3 face to gray 48*48*1 image.
+ gray = normAndResize(face, input_shape)
+
+ X.append(gray)
+ Y.append(label) #Emotion order is the same as fer2013.
+
+ nbrImages += 1
+ else: print("Erreur pour la donnée : Aucun ou plusieurs visages détectés")
+
+ print(f"{nbrImages} données chargées depuis expW (sur {k} données traités).\n")
+
+ X = np.array(X)
+ Y = np.array(Y)
+
+ return X, Y
+
+
+
+
+
+
+
diff --git a/loadFer2013ds.py b/loadFer2013ds.py
index b10e6b8574f70fdeff4247dbf085b7700f22e966..434ebc910b26a6f537f844399b3edd7e08ecaa39 100644
--- a/loadFer2013ds.py
+++ b/loadFer2013ds.py
@@ -31,7 +31,8 @@ def strToArray(string): # Fer2013 provides images as string so it needs to be t
# LOAD DATA AS ARRAY
def loadFer2013Data(maxNbrImages=35887):
- c = 0
+ print(f"\nCHARGEMENT DE {maxNbrImages} DONNEES DEPUIS FER2013 ...")
+
nbrImagesFer2013 = 35887
filename = "data/fer2013.csv"
emotions = ["Angry", "Disgust", "Fear",
@@ -56,8 +57,9 @@ def loadFer2013Data(maxNbrImages=35887):
X.append(strToArray(stringImage))
Y.append(emotionNbr)
- print(f"Image {i} sur {maxNbrImages} chargée")
+ print(f"Donnée {i} sur {maxNbrImages} chargée", end='\r')
X = np.array(X)
Y = np.array(Y)
+ print(f"{maxNbrImages} données chargées depuis fer2013.")
return X, Y
diff --git a/loadRavdessDs.py b/loadRavdessDs.py
index cb1db154152f931f64266f6d85545d8bed7e417d..f170ce25147b70e2d4d117e0693abf07699ae551 100644
--- a/loadRavdessDs.py
+++ b/loadRavdessDs.py
@@ -5,11 +5,10 @@ import imageProcess as ip
from config import input_shape
-def extractDataFromVideo(filename, videoName, facesList, labelsList):
+def extractDataFromVideo(filename, videoName, facesList, labelsList, maxNbrImages):
# Extract every faces in a specified video and add it to a list of faces as well as labels corresponding.
# Start capture of a video
- print("Lecture vidéo de", videoName)
frameRate = 1
cap = cv2.VideoCapture(filename)
if (cap.isOpened() == False):
@@ -40,12 +39,14 @@ def extractDataFromVideo(filename, videoName, facesList, labelsList):
if len(newFaces) == 1:
facesList += newFaces
labelsList.append(emotionNbr)
- print("Donnée ajoutée, Images:", len(
- facesList), "Labels:", len(labelsList))
- else: print("Erreur pour la donnée : Aucun ou plusieurs visages détectés")
+ elif True: print("Erreur pour la donnée : Aucun ou plusieurs visages détectés")
+
+ # If we overreach the maximum number of images desired, stop
+ if len(facesList) > maxNbrImages:
+ break
# Press Q on keyboard to exit
- if cv2.waitKey(25) & 0xFF == ord('q'):
+ if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Display the resulting frame
@@ -68,7 +69,8 @@ def extractDataFromVideo(filename, videoName, facesList, labelsList):
# LOAD DATA
-def loadRavdessData(maxNbrImages=float('inf')):
+def loadRavdessData(maxNbrImages=10000000000):
+ print(f"\nCHARGEMENT DE {maxNbrImages} DONNEES DEPUIS RAVDESS...")
foldername = "data/ravdessTest/videos/"
emotions = ["_", "Neutral", "Calm", "Happy",
@@ -79,35 +81,36 @@ def loadRavdessData(maxNbrImages=float('inf')):
# For each actor...
for actorName in os.listdir(foldername):
- # If we overreach the maximum number of images desired, stop
- if len(facesList) > maxNbrImages:
- break
-
- print(f"\nTRAITEMENT ACTEUR N°{actorName[-2:]}\n")
+ print(f"TRAITEMENT ACTEUR N°{actorName[-2:]}")
videoNames = os.listdir(foldername+actorName)
nbrOfVideos = len(videoNames)
k = 0
# For each video...
for videoName in videoNames:
+ # If we overreach the maximum number of images desired, stop
+ if len(facesList) >= maxNbrImages:
+ break
+
k += 1
print(f"Traitement de {videoName}, video {k}/{nbrOfVideos}")
filename = foldername+actorName+'/'+videoName
# Press Q on keyboard to exit ONE video
- if cv2.waitKey(25) & 0xFF == ord('q'):
+ if cv2.waitKey(1) & 0xFF == ord('q'):
break
if videoName[7] == '2':
# Doesnt take Calm emotion into account
print("Emotion 'Calme', non prise en compte")
else:
+ #Add datas extracted from the specified video to features and labels
facesList, labelsList = extractDataFromVideo(
- filename, videoName, facesList, labelsList)
+ filename, videoName, facesList, labelsList, maxNbrImages)
# List of colored images N*M*3 faces to array of gray images 48*48*1
N = len(facesList)
- print(f"Traitement des {N} visages détectés sur les vidéos de Ravdess")
+ print(f"TRAITEMENT RAVDESS: traitement des {N} visages détectés sur les vidéos de Ravdess...")
for k in range(N):
visage = facesList[k]
@@ -116,5 +119,5 @@ def loadRavdessData(maxNbrImages=float('inf')):
Y = np.array(labelsList)
- print(N, " données chargées depuis Ravdess.")
+ print(N, "données chargées depuis Ravdess.")
return X, Y
diff --git a/test.py b/test.py
index d376c9b1c99854b72af2eef61111cd6f17f309ed..afd0b49d75e49b3ad6f39385bb30c776fcbf8475 100644
--- a/test.py
+++ b/test.py
@@ -15,9 +15,13 @@ import random as rd
import cv2
import csv
-from loadFer2013ds import *
-from loadRavdessDs import *
+from loadFer2013DS import *
+from loadRavdessDS import *
+from loadExpWDS import *
+from loadAffwild import *
from utils import *
-X, Y = loadFer2013Data(100)
-W, Z = loadRavdessData(100)
\ No newline at end of file
+# X, Y = loadFer2013Data(10)
+# W, Z = loadRavdessData(10)
+# A, B = loadExpWData(10)
+C, D = loadAffwildData(1000)
\ No newline at end of file