目录
onnx测试
tensorflow预测tflite代码：
onnx测试
img_path=r'D:\data\val_result\1212\test\1\2\02370_1.jpg'
img_path=r'D:\data\face\1212\test\1\2\02674_1.jpg'
img=cv2.imread(img_path)
img_o = cv2.resize(img, (192, 192))
# img=cv2.cvtColor(img_o,cv2.COLOR_BGR2RGB)
img = img_o.astype(np.float32)
# img=img-127.5
img/=255.0
# dummy_input = np.ones([1, 192, 192, 3], dtype=np.float32)
dummy_input = np.array([img])
model_path = r'E:\project\angle_net\xuanzhuan_detect\ncnn-20220420-windows-vs2017\x64\bin/model_float32.onnx'
# model_path = r'C:\Users\Administrator\Pictures\mm\resources.tar\resources\face_landmark_with_attention_192x192/model_float32.onnx'
model_file_name = model_path.split(".")[0]
session_option = onnxruntime.SessionOptions()
session_option.optimized_model_filepath = f"{model_file_name}_cudaopt.onnx"
session_option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_EXTENDED
session = onnxruntime.InferenceSession(
model_path,
session_option,
providers=['CUDAExecutionProvider']
)
input_name = session.get_inputs()[0].name
output_names = [o.name for o in session.get_outputs()]
input_shape = session.get_inputs()[0].shape
# Warmup
output = session.run(
output_names,
{input_name: dummy_input.transpose((0,3,1,2))}
)
# Inference
for i in range(5):
start = time.time()
output = session.run(output_names,{input_name: dummy_input.transpose((0,3,1,2))})
for i in [1,2,3,4,5,6]:
points=output[i][0][0]
if len(points[0])==1404:
data_len = len(points[0]) // 3
# print(data_len)
for index in range(data_len):
cv2.circle(img_o, (int(points[0][index * 3]), int(points[0][index * 3 + 1])), 1, (0, 0, 213),
-1) # x,y,r,color
else:
data_len = len(points[0]) // 2
# print(data_len)
for index in range(data_len):
cv2.circle(img_o, (int(points[0][index * 2]), int(points[0][index * 2 + 1])), 1, (0, 0, 213),
-1) # x,y,r,color
cv2.imshow("result",img_o)
cv2.waitKey(0)
print(f'elapsed_time : {(time.time()-start)*1000} ms')
tensorflow预测tflite代码：
import os
import cv2
import tensorflow as tf
import numpy as np
from tensorflow.lite.python.interpreter import Interpreter
class MeshDetector(object):
def __init__(self, model_path):
"""Initialization"""
self.target_image = None
self.interpreter =Interpreter(model_path=model_path)
# Set model input
self.interpreter.allocate_tensors()
self.input_details = self.interpreter.get_input_details()
self.output_details = self.interpreter.get_output_details()
def _preprocess(self, image_bgr):
image_resized = cv2.resize(image_bgr, (192, 192))
image_rgb = cv2.cvtColor(image_resized, cv2.COLOR_BGR2RGB)
image_norm = (image_rgb-127.5)/127.5
return image_norm
def get_mesh(self, image):
# Preprocess the image before sending to the network.
image = self._preprocess(image)
image = tf.image.convert_image_dtype(image, tf.float32)
image = image[tf.newaxis, :]
# The actual detection.
self.interpreter.set_tensor(self.input_details[0]["index"], image)
self.interpreter.invoke()
# Save the results.
mesh = self.interpreter.get_tensor(self.output_details[3]["index"])[0].reshape(468, 3) / 192
score = self.interpreter.get_tensor(self.output_details[1]["index"])[0]
return mesh, score
def draw_mesh(self, image, mesh, mark_size=2, line_width=1):
"""Draw the mesh on an image"""
# The mesh are normalized which means we need to convert it back to fit
# the image size.
image_size = image.shape[0]
mesh = mesh * image_size
for point in mesh:
cv2.circle(image, (int(point[0]), int(point[1])),mark_size, (0, 255, 128), -1)
# Draw the contours.
# Eyes
left_eye_contour = np.array([mesh[33][0:2],
mesh[7][0:2],
mesh[163][0:2],
mesh[144][0:2],
mesh[145][0:2],
mesh[153][0:2],
mesh[154][0:2],
mesh[155][0:2],
mesh[133][0:2],
mesh[173][0:2],
mesh[157][0:2],
mesh[158][0:2],
mesh[159][0:2],
mesh[160][0:2],
mesh[161][0:2],
mesh[246][0:2], ]).astype(np.int32)
right_eye_contour = np.array([mesh[263][0:2],
mesh[249][0:2],
mesh[390][0:2],
mesh[373][0:2],
mesh[374][0:2],
mesh[380][0:2],
mesh[381][0:2],
mesh[382][0:2],
mesh[362][0:2],
mesh[398][0:2],
mesh[384][0:2],
mesh[385][0:2],
mesh[386][0:2],
mesh[387][0:2],
mesh[388][0:2],
mesh[466][0:2]]).astype(np.int32)
# Lips
cv2.polylines(image, [left_eye_contour, right_eye_contour], False,
(0, 0, 255), line_width, cv2.LINE_AA)
if __name__ == "__main__":
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
mesh_detector= MeshDetector(r'model_float32.tflite')
img_path = r'D:\data\face\1212\test\1\2\02674_1.jpg'
img = cv2.imread(img_path)
img = cv2.resize(img, (192, 192))
face_mesh, score= mesh_detector.get_mesh(img)
mesh_detector.draw_mesh(img, face_mesh, mark_size=1)
# cv2.imshow("Mesh", cv2.resize(img, (512, 512)))
cv2.imshow("Mesh",img)
cv2.waitKey()
