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b8d9812e by 乔峰昇
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mnn

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Showing 4 changed files with 80 additions and 38 deletions
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......
infer_mnn.py
import os
import cv2
from PIL import Image, ImageFont, ImageDraw
import torch
from torchvision import transforms
import MNN
import numpy as np
def keep_shape_resize(frame, size=128):
......@@ -24,12 +20,10 @@ def keep_shape_resize(frame, size=128):
def image_infer_mnn(mnn_model_path, image_path, class_list):
image = Image.open(image_path)
input_image = keep_shape_resize(image)
preprocess = transforms.Compose([transforms.ToTensor()])
input_data = preprocess(input_image)
input_data = np.array(input_image).astype(np.float32).transpose((2, 0, 1)) / 255
interpreter = MNN.Interpreter(mnn_model_path)
session = interpreter.createSession()
input_tensor = interpreter.getSessionInput(session)
input_data = input_data.cpu().numpy().squeeze()
tmp_input = MNN.Tensor((1, 3, 128, 128), MNN.Halide_Type_Float, input_data, MNN.Tensor_DimensionType_Caffe)
input_tensor.copyFrom(tmp_input)
interpreter.runSession(session)
......@@ -53,9 +47,7 @@ def video_infer_mnn(mnn_model_path, video_path):
image_data = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
image_data = Image.fromarray(image_data)
image_data = keep_shape_resize(image_data, 128)
preprocess = transforms.Compose([transforms.ToTensor()])
input_data = preprocess(image_data)
input_data = input_data.cpu().numpy().squeeze()
input_data = np.array(image_data).astype(np.float32).transpose((2, 0, 1)) / 255
tmp_input = MNN.Tensor((1, 3, 128, 128), MNN.Halide_Type_Float, input_data, MNN.Tensor_DimensionType_Caffe)
input_tensor.copyFrom(tmp_input)
interpreter.runSession(session)
......@@ -81,9 +73,7 @@ def camera_infer_mnn(mnn_model_path, camera_id):
image_data = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
image_data = Image.fromarray(image_data)
image_data = keep_shape_resize(image_data, 128)
preprocess = transforms.Compose([transforms.ToTensor()])
input_data = preprocess(image_data)
input_data = input_data.cpu().numpy().squeeze()
input_data = np.array(image_data).astype(np.float32).transpose((2, 0, 1)) / 255
tmp_input = MNN.Tensor((1, 3, 128, 128), MNN.Halide_Type_Float, input_data, MNN.Tensor_DimensionType_Caffe)
input_tensor.copyFrom(tmp_input)
interpreter.runSession(session)
......@@ -98,14 +88,12 @@ def camera_infer_mnn(mnn_model_path, camera_id):
break
if __name__ == '__main__':
class_list = ['mask', 'no_mask']
image_path = 'test_image/mask_2997.jpg'
mnn_model_path = 'mobilenet_v2.mnn'
image_path = 'test_image/mask_2995.jpg'
mnn_model_path = 'cls_abnormal_face_mnn_1.0.0_v0.0.1.mnn'
# image
# image=image_infer_mnn(mnn_model_path,image_path,class_list)
# image.show()
image = image_infer_mnn(mnn_model_path, image_path, class_list)
image.show()
# camera
camera_infer_mnn(mnn_model_path, 0)
# camera_infer_mnn(mnn_model_path,0)
......
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