| import onnxruntime as rt | |
| import cv2 | |
| import numpy as np | |
| import time | |
| providers = ['CPUExecutionProvider'] | |
| m_q = rt.InferenceSession( | |
| "eff_quantized.onnx", providers=providers) | |
| def emotions_detector(img_array): | |
| time_init = time.time() | |
| # Check if image is in grayscale and convert to rgb | |
| if len(img_array.shape) == 2: | |
| img_array = cv2.cvtColor(img_array, cv2.COLOR_GRAY2RGB) | |
| # resize layer | |
| test_image = cv2.resize(img_array, (256, 256)) | |
| im = np.float32(test_image) | |
| img_array = np.expand_dims(im, axis=0) | |
| onnx_pred = m_q.run(['dense_2'], {"input_1": img_array}) | |
| time_elapsed = time.time() - time_init | |
| return onnx_pred, time_elapsed | |