Exemplo n.º 1
0
def fun_util():
    emojis = get_emojis()
    disp_probab, disp_class = 0, 0
    while True:
        img = cam.read()[1]
        img = cv2.flip(img, 1)
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        faces = detector(gray)
        if len(faces) > 0:
            for i, face in enumerate(faces):
                shape_68 = shape_predictor_68(img, face)
                shape = face_utils.shape_to_np(shape_68)
                mask = create_mask(shape, img)
                masked = cv2.bitwise_and(gray, mask)
                maskAligned = fa.align(mask, gray, face)
                faceAligned = fa.align(masked, gray, face)
                (x0, y0, x1, y1) = get_bounding_rect(maskAligned)
                faceAligned = faceAligned[y0:y1, x0:x1]
                faceAligned = cv2.resize(faceAligned, (100, 100))
                (x, y, w, h) = face_utils.rect_to_bb(face)
                #cv2.rectangle(img, (x, y), (x+w, y+h), (255, 255, 0), 2)
                cv2.imshow('faceAligned', faceAligned)
                cv2.imshow('face #{}'.format(i), img[y:y + h, x:x + w])
                pred_probab, pred_class = keras_predict(cnn_model, faceAligned)
                img = blend(img, emojis[pred_class], (x, y, w, h))
        cv2.imshow('img', img)
        if cv2.waitKey(1) == ord('q'):
            break
Exemplo n.º 2
0
def emojify(model):
	emojis = get_emojis()
	disp_probab, disp_class = 0, 0
	while True:
		img = cam.read()[1]
		img = cv2.flip(img, 1)
		gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
		faces = detector(gray)
		if len(faces) > 0:
			for i, face in enumerate(faces):
				shape_68 = shape_predictor_68(img, face)
				shape = face_utils.shape_to_np(shape_68)
				mask = create_mask(shape, img)
				masked = cv2.bitwise_and(gray, mask)
				maskAligned = fa.align(mask, gray, face)
				faceAligned = fa.align(masked, gray, face)
				(x0, y0, x1, y1) = get_bounding_rect(maskAligned)
				faceAligned = faceAligned[y0:y1, x0:x1]
				cv2.imshow('faceAligned', faceAligned)
				(x, y, w, h) = face_utils.rect_to_bb(face)
				cv2.imshow('face #{}'.format(i), img[y:y+h, x:x+w])
				np_image = load(faceAligned)
				pred_probab = model.predict(np_image)
				pred_class = np.argmax(pred_probab,axis = 1)
				img = blend(img, emojis[int(pred_class)], (x, y, w, h))
		cv2.imshow('img', img)
		if cv2.waitKey(1) == ord('q'):
			break
Exemplo n.º 3
0
    c = 0
    for image in os.listdir(FACES + folder):
        path = FACES + folder + image
        img = cv2.imread(path)
        print(image)
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        faces = detector(gray)
        if len(faces) > 0:
            for face in faces:
                shape_68 = shape_predictor_68(img, face)
                shape = face_utils.shape_to_np(shape_68)
                mask = create_mask(shape, img)
                masked = cv2.bitwise_and(gray, mask)
                maskAligned = fa.align(mask, gray, face)
                faceAligned = fa.align(masked, gray, face)
                (x0, y0, x1, y1) = get_bounding_rect(maskAligned)
                faceAligned = faceAligned[y0:y1, x0:x1]
                faceAligned = cv2.resize(faceAligned, (128, 128))
                (x, y, w, h) = face_utils.rect_to_bb(face)
                cv2.rectangle(img, (x, y), (x + w, y + h), (255, 255, 0), 2)
                cv2.imshow('faceAligned', faceAligned)

                if not os.path.isdir(DATASET + folder):
                    os.mkdir(DATASET + folder)

                cv2.imwrite(DATASET + folder + image, faceAligned)
                c += 1
        cv2.imshow('img', img)
        if cv2.waitKey(1) == ord('q'):
            break