data loaders debugged

.vscode/settings.json

+{
+    "python.pythonPath": "C:\\Users\\timot\\AppData\\Local\\Microsoft\\WindowsApps\\python.exe"
+}
\ No newline at end of file

imageProcess.py

@@ -4,7 +4,7 @@ import numpy as np
 import faceAnalysis as fa
 import timeit as ti
 
-def imageProcess(image, writeEmotion=True):
+def imageProcess(image, writeEmotion=True, writeRectangle=True):
     #Objectives : detect faces, identify emotion associated on it, modify the image by framing faces and writing their emotions associated
     
     facesList = []
@@ -22,6 +22,7 @@ def imageProcess(image, writeEmotion=True):
         x,y,w,h = face
         
         #Create blue rectangle around face of thickness 2
+        if writeRectangle:
             cv2.rectangle(image, (x,y), (x+w,y+h), (255,0,0), 2 )
         
         #Select face image

loadAffwildDS.py

@@ -79,6 +79,9 @@ def extractDataFromVideo_(filename, videoName, facesList, labelsList, maxNbrImag
     return facesList, labelsList
 
 
+
+
+
 # LOAD DATA
 
 def loadAffwildData(maxNbrImages=10000000000, frameRate=1/20):

loadExpWDS.py

@@ -5,7 +5,7 @@ import imageProcess as ip
 import numpy as np
 
 
-def loadExpWData(nbrMaxImages=float('inf'), onlyDetected=False):
+def loadExpWData(nbrMaxImages=float('inf'), onlyDetected=False, detectedFace=False):
 	print(f"\nCHARGEMENT DE {nbrMaxImages} DONNEES DEPUIS EXPW...")
 	folderImages = 'data/expW/images/'
 	fileLabels = 'data/expW/labels.lst'
@@ -24,7 +24,7 @@ def loadExpWData(nbrMaxImages=float('inf'), onlyDetected=False):
 		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)
+		facesDetected = ip.imageProcess(faceAccordingToDS, writeEmotion=False, writeRectangle=False)
 		
 		#Suivi visuel (facultatif, fait un peu peur sans attendre 1000ms entre deux images...)
 		if False:
@@ -36,7 +36,7 @@ def loadExpWData(nbrMaxImages=float('inf'), onlyDetected=False):
 		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:
+			if len(facesDetected) != 0 and detectedFace:
 				face = facesDetected[0]
 			else:
 				face = faceAccordingToDS
@@ -48,13 +48,12 @@ def loadExpWData(nbrMaxImages=float('inf'), onlyDetected=False):
 			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")
+			print(f"{nbrImages} données chargées depuis expW (sur {k} données traités).", end='\r')
 
 	X = np.array(X)
 	Y = np.array(Y)
-
+	print('\n')
 	return X, Y
 
 

loadFer2013ds.py

@@ -36,7 +36,7 @@ def loadFer2013Data(maxNbrImages=35887):
     print(f"\nCHARGEMENT DE {maxNbrImages} DONNEES DEPUIS FER2013 ...")
 
     maxNbrImages = min(maxNbrImages, 35887)
-    filename = "data/fer2013.csv"
+    filename = "data/fer2013/fer2013.csv"
     emotions = ["Angry", "Disgust", "Fear",
                 "Happy", "Sad", "Suprise", "Neutral"]
 

loadRavdessDs.py

@@ -5,15 +5,17 @@ import imageProcess as ip
 from config import input_shape
 
 
-def extractDataFromVideo(filename, videoName, facesList, labelsList, maxNbrImages):
+def extractDataFromVideo(filename, videoName, facesList, labelsList, maxNbrImages, frameRate):
     # 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
-    frameRate = 1
     cap = cv2.VideoCapture(filename)
     if (cap.isOpened() == False):
         print("Error opening video stream or file")
 
+    emotions_ravdess = ["_", "Neutral", "Calm", "Happy",
+                "Sad", "Angry", "Fear", "Disgust", "Suprise"]
+
     # Read until video is completed
     k = 0
     while (cap.isOpened()):
@@ -21,9 +23,7 @@ def extractDataFromVideo(filename, videoName, facesList, labelsList, maxNbrImage
         ret, frame = cap.read()
         if ret == True:
             k += 1
-            # If we overreach the maximum number of images desired, stop
-            if len(facesList) > maxNbrImages:
-                break
+            
 
             if k*frameRate >= 1:  # Read a frame each N frames where N=1/frameRate
                 k = 0
@@ -32,7 +32,7 @@ def extractDataFromVideo(filename, videoName, facesList, labelsList, maxNbrImage
 
                 # Ravdess emotions list is not in the same order as fer2013 (reference)
                 emotionNbr = int(videoName[7])
-                emotion = emotions[emotionNbr]
+                emotion = emotions_ravdess[emotionNbr]
                 emotionNbr = emotionToNumber(emotion)
 
                 # Detect faces on the image
@@ -45,8 +45,8 @@ def extractDataFromVideo(filename, videoName, facesList, labelsList, maxNbrImage
                 elif True:
                     print("Erreur pour la donnée : Aucun ou plusieurs visages détectés")
 
-            # Press Q on keyboard to  exit
-            if cv2.waitKey(1) & 0xFF == ord('q'):
+                # If we overreach the maximum number of images desired, stop
+                if len(facesList) >= maxNbrImages:
                     break
 
             # Display the resulting frame
@@ -69,19 +69,21 @@ def extractDataFromVideo(filename, videoName, facesList, labelsList, maxNbrImage
 
 # LOAD DATA
 
-def loadRavdessData(maxNbrImages=10000000000):
+def loadRavdessData(maxNbrImages=10000000000, frameRate = 1/20):
     print(f"\nCHARGEMENT DE {maxNbrImages} DONNEES DEPUIS RAVDESS...")
 
-    foldername = "data/ravdessTest/videos/"
-    emotions = ["_", "Neutral", "Calm", "Happy",
-                "Sad", "Angry", "Fear", "Disgust", "Suprise"]
+    foldername = "data/ravdess/videos/"
+    
     facesList = []
     labelsList = []
-    maxNbrImages -= 1
 
     # 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"TRAITEMENT ACTEUR N°{actorName[-2:]}")
         videoNames = os.listdir(foldername+actorName)
         nbrOfVideos = len(videoNames)
@@ -89,6 +91,7 @@ def loadRavdessData(maxNbrImages=10000000000):
         k = 0
         # For each video...
         for videoName in videoNames:
+
             # If we overreach the maximum number of images desired, stop
             if len(facesList) >= maxNbrImages:
                 break
@@ -97,17 +100,14 @@ def loadRavdessData(maxNbrImages=10000000000):
             print(f"Traitement de {videoName}, video {k}/{nbrOfVideos}")
             filename = foldername+actorName+'/'+videoName
 
-            # Press Q on keyboard to exit ONE video
-            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:
+
+            elif videoName[7] in [str(n) for n in range(1, 9)]:
                 # Add datas extracted from the specified video to features and labels
                 facesList, labelsList = extractDataFromVideo(
-                    filename, videoName, facesList, labelsList, maxNbrImages)
+                    filename, videoName, facesList, labelsList, maxNbrImages, frameRate)
 
     # List of colored images N*M*3 faces to array of gray images 48*48*1
     N = len(facesList)

test.py

-#Use this file for test
-
-import tensorflow as tf
-from tensorflow import keras
-from tensorflow.keras import datasets, layers, models, losses
-import tensorflow_datasets as tfds
-#from google.colab import files
-
-from matplotlib import image
-import os
 import numpy as np
-import matplotlib.pyplot as plt
-import matplotlib
-import random as rd
-import cv2
-import csv
-
-from loadFer2013DS import *
+A = np.array([ [[1,2],[3,4]] , [[5,6],[7,8]] , [[1,2],[3,4]]])
+B = np.array([ [[1,2],[3,4]] , [[5,6],[7,8]] ])
+A = A.tolist()
+B = B.tolist()
+C = np.stack(A+B, axis = 0)
+print(C.shape)
\ No newline at end of file
-from loadRavdessDS import *
-from loadExpWDS import *
-from loadAffwild import *
-from utils import *
-
-X, Y = loadFer2013Data(10)
-W, Z = loadRavdessData(10)
-A, B = loadExpWData(10)
-C, D = loadAffwildData(10)
\ No newline at end of file

utils.py

@@ -42,8 +42,14 @@ def emotionToNumber(emotion):
                 "Happy", "Sad", "Suprise", "Neutral"]
     return emotions.index(emotion)
 
+def stackImages(listOfArrayImage):
+    liste = []
+    for X in listOfArrayImage:
+        liste += X.tolist()
+    return np.stack(liste, axis=0)
     
-def mergeToDatabase(listOfX, listOfY, validation_repart=[0.025, 0.025, 0.025, 0.025]):
+
+def mergeToDatabase(listOfX, listOfY, validation_repart=[0.1, 0.1, 0.1, 0.1]):
     # This shuffle each X, extract validation data, merge differents X, shuffle again.
     listOfX_train, listOfY_train = [], []
     listOfX_test, listOfY_test = [], []
@@ -55,18 +61,18 @@ def mergeToDatabase(listOfX, listOfY, validation_repart=[0.025, 0.025, 0.025, 0.
         shuffler = np.random.permutation(N)
         X, Y = X[shuffler], Y[shuffler]
         # Extract validation data
-        X_train, Y_train = X[:N*(1-rate)], Y[:N*(1-rate)]
-        X_test, Y_test = X[N*(1-rate):], Y[N*(1-rate):]
+        X_train, Y_train = X[:int(N*(1-rate))], Y[:int(N*(1-rate))]
+        X_test, Y_test = X[int(N*(1-rate)):], Y[int(N*(1-rate)):]
         listOfX_train.append(X_train)
         listOfY_train.append(Y_train)
         listOfX_test.append(X_test)
         listOfY_test.append(Y_test)
     # Merge
-    BigX_train = np.stack(listOfX_train)
-    BigY_train = np.stack(listOfY_train)
+    BigX_train = stackImages(listOfX_train)
+    BigY_train = stackImages(listOfY_train)
 
-    BigX_test = np.stack(listOfX_test)
-    BigY_test = np.stack(listOfY_test)
+    BigX_test = stackImages(listOfX_test)
+    BigY_test = stackImages(listOfY_test)
     # Shuffle the whole
     shuffler = np.random.permutation(len(BigX_train))
     BigX_train = BigX_train[shuffler]