I am working on my research related project in Human Emotion Detection using OpenCV + Python. I follow a tutorial with using CK+ data sets to train. But when i try to Run the code to training the data set it gives OutOfMemory Error. How can i solved this problem. Please help me. I am a beginner for OpenCV and Python. I put my error code and source code bellow.
OpenCV Error: Insufficient memory (Failed to allocate 495880000 bytes) in cv::OutOfMemoryError, file C:\projects\opencv-python\opencv\modules\core\src\alloc.cpp, line 55
OpenCV Error: Assertion failed (u != 0) in cv::Mat::create, file C:\projects\opencv-python\opencv\modules\core\src\matrix.cpp, line 436
Traceback (most recent call last):
File "D:/Documents/Private/Pycharm/EmotionDetection/training.py", line 71, in <module>
correct = run_recognizer()
File "D:/Documents/Private/Pycharm/EmotionDetection/training.py", line 49, in run_recognizer
fishface.train(training_data, np.asarray(training_labels))
cv2.error: C:\projects\opencv-python\opencv\modules\core\src\matrix.cpp:436: error: (-215) u != 0 in function cv::Mat::create
This is Source Code.
import cv2
import glob
import random
import numpy as np
emotions = ["neutral", "anger", "contempt", "disgust", "fear", "happy", "sadness", "surprise"] # Emotion list
fishface = cv2.face.EigenFaceRecognizer_create() # Initialize fisher face classifier
data = {}
def get_files(emotion): # Define function to get file list, randomly shuffle it and split 80/20
files = glob.glob("dataset\\%s\\*" % emotion)
random.shuffle(files)
training = files[:int(len(files) * 0.8)] # get first 80% of file list
prediction = files[-int(len(files) * 0.2):] # get last 20% of file list
return training, prediction
def make_sets():
training_data = []
training_labels = []
prediction_data = []
prediction_labels = []
for emotion in emotions:
training, prediction = get_files(emotion)
# Append data to training and prediction list, and generate labels 0-7
for item in training:
image = cv2.imread(item) # open image
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # convert to grayscale
training_data.append(gray) # append image array to training data list
training_labels.append(emotions.index(emotion))
for item in prediction: # repeat above process for prediction set
image = cv2.imread(item)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
prediction_data.append(gray)
prediction_labels.append(emotions.index(emotion))
return training_data, training_labels, prediction_data, prediction_labels
def run_recognizer():
training_data, training_labels, prediction_data, prediction_labels = make_sets()
print("training fisher face classifier")
print("size of training set is:", len(training_labels), "images")
fishface.train(training_data, np.asarray(training_labels))
print("predicting classification set")
cnt = 0
correct = 0
incorrect = 0
for image in prediction_data:
pred, conf = fishface.predict(image)
if pred == prediction_labels[cnt]:
correct += 1
cnt += 1
else:
cv2.imwrite("difficult\\%s_%s_%s.jpg" % (emotions[prediction_labels[cnt]], emotions[pred], cnt), image) # <-- this one is new
incorrect += 1
cnt += 1
return (100 * correct) / (correct + incorrect)
# Now run it
meta_score = []
for i in range(0, 10):
correct = run_recognizer()
print("got", correct, "percent correct!")
meta_score.append(correct)
print("\n\nend score:", np.mean(meta_score), "percent correct!")
If you are using 32-bit system,this is not possible because there is not sufficient memory you can address. your images are too big for your build system. If you have 32 bit operating system, upgrade to 64-bit else you are probably using 32-bit build environments and you should switch to 64-bit build tools.