clarification, debut code du notebook

loadAffwild.py

-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

loadAffwildDS.py

+import os
+import cv2
+from utils import *
+import imageProcess as ip
+from config import input_shape
+
+
+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.
+    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)
+                elif False:
+                    print(
+                        "Erreur pour la donnée : Aucun ou plusieurs visages détectés", end='\r')
+
+                # 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, frameRate=1/20):
+    print(f"\nCHARGEMENT DE {maxNbrImages} DONNEES DEPUIS AFFWILD...")
+
+    foldername = "data/affwild/videos/"
+    facesList = []
+    labelsList = []
+    maxNbrImages -= 1
+    k = 0
+    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, frameRate)
+
+    # 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

loadFer2013ds.py

@@ -3,6 +3,8 @@ import csv
 import numpy as np
 import cv2
 import matplotlib.pyplot as plt
+from config import input_shape
+from utils import *
 
 
 def strToArray(string):  # Fer2013 provides images as string so it needs to be transformed
@@ -23,7 +25,7 @@ def strToArray(string):  # Fer2013 provides images as string so it needs to be t
     A.append(int(nbr))
 
     A = np.array(A)
-    A = np.reshape(A, (48, 48))
+    A = np.reshape(A, input_shape)
 
     return A
 
@@ -33,7 +35,7 @@ def strToArray(string):  # Fer2013 provides images as string so it needs to be t
 def loadFer2013Data(maxNbrImages=35887):
     print(f"\nCHARGEMENT DE {maxNbrImages} DONNEES DEPUIS FER2013 ...")
 
-    nbrImagesFer2013 = 35887
+    maxNbrImages = min(maxNbrImages, 35887)
     filename = "data/fer2013.csv"
     emotions = ["Angry", "Disgust", "Fear",
                 "Happy", "Sad", "Suprise", "Neutral"]
@@ -54,7 +56,7 @@ def loadFer2013Data(maxNbrImages=35887):
 
             emotionNbr, stringImage, typeImage = row
 
-            X.append(strToArray(stringImage))
+            X.append(normAndResize(strToArray(stringImage), input_shape))
             Y.append(emotionNbr)
 
             print(f"Donnée {i} sur {maxNbrImages} chargée", end='\r')

loadRavdessDs.py

@@ -21,6 +21,9 @@ 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
@@ -39,18 +42,15 @@ def extractDataFromVideo(filename, videoName, facesList, labelsList, maxNbrImage
                 if len(newFaces) == 1:
                     facesList += newFaces
                     labelsList.append(emotionNbr)
-                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
+                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'):
                 break
 
             # Display the resulting frame
-            if True:
+            if False:
                 cv2.imshow('Frame', frame)
 
         # Break the loop
@@ -77,6 +77,7 @@ def loadRavdessData(maxNbrImages=10000000000):
                 "Sad", "Angry", "Fear", "Disgust", "Suprise"]
     facesList = []
     labelsList = []
+    maxNbrImages -= 1
 
     # For each actor...
     for actorName in os.listdir(foldername):
@@ -110,7 +111,8 @@ def loadRavdessData(maxNbrImages=10000000000):
 
     # List of colored images N*M*3 faces to array of gray images 48*48*1
     N = len(facesList)
-    print(f"TRAITEMENT RAVDESS: 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]

test.py

@@ -21,7 +21,7 @@ from loadExpWDS import *
 from loadAffwild import *
 from utils import *
 
-# X, Y = loadFer2013Data(10)
-# W, Z = loadRavdessData(10)
-# A, B = loadExpWData(10)
-C, D = loadAffwildData(1000)
\ No newline at end of file
+X, Y = loadFer2013Data(10)
+W, Z = loadRavdessData(10)
+A, B = loadExpWData(10)
+C, D = loadAffwildData(10)
\ No newline at end of file

utils.py

@@ -2,6 +2,8 @@ import numpy as np
 import cv2
 import matplotlib.pyplot as plt
 from config import emotions
+import tensorflow as tf
+
 
 def afficher(image):
     if len(image.shape) == 3:
@@ -34,6 +36,8 @@ def normAndResize(image, input_shape):
 
     return image
 
+
 def emotionToNumber(emotion):
-    emotions = ["Angry", "Disgust", "Fear", "Happy", "Sad", "Suprise", "Neutral"]
+    emotions = ["Angry", "Disgust", "Fear",
+                "Happy", "Sad", "Suprise", "Neutral"]
     return emotions.index(emotion)