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b2e5bfb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 | #!/usr/bin/env python3
# Copyright 2022-2024,2026 NXP
# SPDX-License-Identifier: MIT
import argparse
import time
import cv2
import numpy as np
import tensorflow as tf
LABELS = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']
parser = argparse.ArgumentParser(description="Deepface emotion inference example")
parser.add_argument("-m", "--model",
default="original_model/emotion_uint8_float32.tflite",
type=str,
help="Path to the TFLite model file.")
parser.add_argument("-i", "--input", default="example_input.jpg", type=str,
help="Path to a grayscale face crop image (48x48).")
args = parser.parse_args()
interpreter = tf.lite.Interpreter(args.model)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
print("Loaded model:", args.model)
# Load grayscale image and resize to 48x48.
im = cv2.imread(args.input, cv2.IMREAD_GRAYSCALE)
if im is None:
raise FileNotFoundError(f"Could not open image: {args.input}")
start = time.time()
im = cv2.resize(im, (48, 48))
# Shape: (1, 48, 48, 1), normalized to [0, 1].
im = im[None, ..., None].astype(np.float32) / 255.0
# Quantize input: scale from float to uint8 using model quantization params.
input_scale, input_zero_point = input_details[0]["quantization"]
im_q = im / input_scale + input_zero_point
im_q = im_q.astype(np.uint8)
interpreter.set_tensor(input_details[0]['index'], im_q)
interpreter.invoke()
out = interpreter.get_tensor(output_details[0]['index'])
end = time.time()
print("Output tensor:", out)
print("Recognized emotion:", LABELS[int(out.argmax())])
print(f"Inference time: {(end - start) * 1000:.2f} ms")
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