def train_classifier(filename, randomize = False, overfit = False, verbose = False):
data, target = load_dataset(randomize=randomize, overfit=overfit)
hist_data = []
hog = HOG(orientations=18, pixelsPerCell=(10, 10), cellsPerBlock=(1, 1), normalize=True)
if os.path.exists(MODELS_DIR + filename):
print 'loading model'
model = joblib.load(MODELS_DIR + filename)
else:
print 'training model'
i = 0
for image in data:
image = dataset.center_extent(image, (20, 20))
hist = hog.describe(image)
hist_data.append(hist)
i += 1
# model = LinearSVC(random_state=42)
model = svm.SVC(probability=True, kernel='sigmoid')
# model = svm.NuSVC(probability=True)
start = time.time()
model.fit(hist_data, target)
seconds = time.time() - start
m, s = divmod(seconds, 60)
h, m = divmod(m, 60)
print "Took %d:%02d:%02d to train model." % (h, m, s)
joblib.dump(model, MODELS_DIR + filename)
correct = 0
total = 0
for i in range(data.shape[0]):
image = data[i]
input_image = image
input_image = dataset.center_extent(input_image, (20, 20))
hist = hog.describe(input_image)
pred = model.predict_proba(hist).reshape([14]).tolist()
max_value = max(pred)
max_index = pred.index(max_value)
if verbose:
print '-' * 50
print 'Predicted: ' + str(max_index) + ' with ' + str(max_value) + ' confidence'
print 'Actual: ' + str(target[i])
cv2.imshow('input', image)
cv2.waitKey(0)
if max_index == target[i]:
correct += 1
total += 1
print str(correct) + ' out of ' + str(total)
print 'accuracy: ' + str(correct / total)
return model
def generate_training_data(problem_id, exclusions, threshold, leeway, transformations, label):
try:
problem = Problem.objects.get(id=problem_id)
except Problem.DoesNotExist:
return None
global nn, f_output
im = cv2.imread(problem.image.path)
im_height, im_width = im.shape[:2]
im_gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
im_blurred = cv2.GaussianBlur(im_gray, (9, 9), 0)
im_edged = cv2.Canny(im_blurred, 30, 150)
(im_contoured, contours, _) = cv2.findContours(im_edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = sorted([(contour, cv2.boundingRect(contour)[0]) for contour in contours], key =lambda x: x[1])
rectangles = [cv2.boundingRect(contour[0]) for contour in contours]
leeway = leeway
if leeway == None:
leeway = 0
rectangles, bad_rectangles = valid_rectangles(rectangles, leeway)
while bad_rectangles > 0:
rectangles, bad_rectangles = valid_rectangles(rectangles, leeway)
i = 0
for rectangle in rectangles:
x, y, w, h = rectangle
if 7 <= w <= im_width / 4 and 20 <= h <= im_height / 4:
if i not in exclusions:
roi = im_gray[y: y + h, x: x + w]
im_thresh = roi.copy()
if threshold == None:
threshold = mahotas.thresholding.otsu(roi)
im_thresh[im_thresh > threshold] = 255
im_thresh = cv2.bitwise_not(im_thresh)
im_thresh = dataset.center_extent(im_thresh, (28, 28))
training_sample = im_thresh.copy()
training_samples.append((training_sample.reshape((784, 1)), label))
if 'rot90' in transformations:
training_samples.append((np.rot90(training_sample, 3).reshape((784, 1)), label))
if 'rot180' in transformations:
training_samples.append((np.rot90(training_sample, 2).reshape((784, 1)), label))
if 'rot270' in transformations:
training_samples.append((np.rot90(training_sample, 1).reshape((784, 1)), label))
if 'fliplr' in transformations:
training_samples.append((np.fliplr(training_sample).reshape((784, 1)), label))
if 'flipud' in transformations:
training_samples.append((np.flipud(training_sample).reshape((784, 1)), label))
# print i
# cv2.imshow('roi', roi)
# cv2.imshow('thresh', im_thresh)
# cv2.waitKey(0)
color = (0, 0, 255)
cv2.rectangle(im, (x, y), (x + w, y + h), color, 2)
i += 1
print problem_id, i
def classify_problem(problem_id):
try:
problem = Problem.objects.get(id=problem_id)
except Problem.DoesNotExist:
problem = None
return []
global nn, f_output
im = cv2.imread(problem.image.path)
im_height, im_width = im.shape[:2]
im_gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
im_blurred = cv2.GaussianBlur(im_gray, (9, 9), 0)
im_edged = cv2.Canny(im_blurred, 30, 150)
(im_contoured, contours, _) = cv2.findContours(im_edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = sorted([(contour, cv2.boundingRect(contour)[0]) for contour in contours], key =lambda x: x[1])
rectangles = [cv2.boundingRect(contour[0]) for contour in contours]
leeway = 20
rectangles, bad_rectangles = valid_rectangles(rectangles, leeway)
while bad_rectangles > 0:
rectangles, bad_rectangles = valid_rectangles(rectangles, leeway)
classification = ''
for rectangle in rectangles:
x, y, w, h = rectangle
if w <= im_width / 2 and h <= im_height / 2:
roi = im_gray[y: y + h, x: x + w]
im_thresh = roi.copy()
threshold = mahotas.thresholding.otsu(roi)
im_thresh[im_thresh > threshold] = 255
im_thresh = cv2.bitwise_not(im_thresh)
im_thresh = dataset.center_extent(im_thresh, (20, 20))
# CNN
# im_thresh = cv2.copyMakeBorder(im_thresh , 4, 4, 4, 4, cv2.BORDER_CONSTANT, value=(0, 0, 0)) #28 x 28 image
# network_input = im_thresh.reshape([1, 1, 28, 28])
# network_output = f_output(network_input).reshape([14]).tolist()
#
# confidence = max(network_output)
# prediction = network_output.index(confidence)
# character = possible_values[prediction]
#
# print 'Predicted: {} with {} confidence.'.format(character, confidence)
#
# print '-' * 100
# color = (128, 128, 128)
# if confidence >= .98:
# color = (255, 0, 0)
# elif .5 < confidence < .98:
# color = (0, 255, 0)
# else:
# color = (0, 0, 255)
# SVM
hist = hog.describe(im_thresh)
output_vector = svm.predict_proba(hist).reshape([14]).tolist()
confidence = max(output_vector)
prediction = output_vector.index(confidence)
character = possible_values[prediction]
color = (128, 128, 128)
if confidence >= .98:
color = (255, 0, 0)
elif .5 < confidence < .98:
color = (0, 255, 0)
else:
color = (0, 0, 255)
cv2.rectangle(im, (x, y), (x + w, y + h), color, 2)
cv2.putText(im, str(character), (x - 10 + w / 2, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 1)
classification += str(character) + ' '
classification = classification[:-1]
print '-> Classification: ', classification
if __name__ == '__main__':
cv2.imshow('image', im)
cv2.waitKey(0)
# Saves processed image
image_path, extension = os.path.splitext(problem.image.path)
processed_image_path = image_path + '_processed' + extension
cv2.imwrite(processed_image_path, im)
# Updated the problem object with the processed photo
opened = open(processed_image_path, 'rb')
processed_image = File(opened)
problem.processed_image.save(processed_image_path, processed_image)
problem.save()
os.remove(processed_image_path)
classification = classification.strip('\r\n')
prediction_solution = ''
try:
prediction_solution = eval(classification)
except SyntaxError:
prediction_solution = 'Error'
finally:
prediction_solution = str(prediction_solution)
return classification, prediction_solution
