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b8d9812e by 乔峰昇
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mnn

1 parent 386478a4
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.idea/workspace.xml
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......
infer_mnn.py
1
2 import os
3
4 import cv2 1 import cv2
5 from PIL import Image, ImageFont, ImageDraw 2 from PIL import Image, ImageFont, ImageDraw
6 import torch
7 from torchvision import transforms
8 import MNN 3 import MNN
4 import numpy as np
9 5
10 6
11 def keep_shape_resize(frame, size=128): 7 def keep_shape_resize(frame, size=128):
...@@ -24,12 +20,10 @@ def keep_shape_resize(frame, size=128): ...@@ -24,12 +20,10 @@ def keep_shape_resize(frame, size=128):
24 def image_infer_mnn(mnn_model_path, image_path, class_list): 20 def image_infer_mnn(mnn_model_path, image_path, class_list):
25 image = Image.open(image_path) 21 image = Image.open(image_path)
26 input_image = keep_shape_resize(image) 22 input_image = keep_shape_resize(image)
27 preprocess = transforms.Compose([transforms.ToTensor()]) 23 input_data = np.array(input_image).astype(np.float32).transpose((2, 0, 1)) / 255
28 input_data = preprocess(input_image)
29 interpreter = MNN.Interpreter(mnn_model_path) 24 interpreter = MNN.Interpreter(mnn_model_path)
30 session = interpreter.createSession() 25 session = interpreter.createSession()
31 input_tensor = interpreter.getSessionInput(session) 26 input_tensor = interpreter.getSessionInput(session)
32 input_data = input_data.cpu().numpy().squeeze()
33 tmp_input = MNN.Tensor((1, 3, 128, 128), MNN.Halide_Type_Float, input_data, MNN.Tensor_DimensionType_Caffe) 27 tmp_input = MNN.Tensor((1, 3, 128, 128), MNN.Halide_Type_Float, input_data, MNN.Tensor_DimensionType_Caffe)
34 input_tensor.copyFrom(tmp_input) 28 input_tensor.copyFrom(tmp_input)
35 interpreter.runSession(session) 29 interpreter.runSession(session)
...@@ -53,9 +47,7 @@ def video_infer_mnn(mnn_model_path, video_path): ...@@ -53,9 +47,7 @@ def video_infer_mnn(mnn_model_path, video_path):
53 image_data = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) 47 image_data = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
54 image_data = Image.fromarray(image_data) 48 image_data = Image.fromarray(image_data)
55 image_data = keep_shape_resize(image_data, 128) 49 image_data = keep_shape_resize(image_data, 128)
56 preprocess = transforms.Compose([transforms.ToTensor()]) 50 input_data = np.array(image_data).astype(np.float32).transpose((2, 0, 1)) / 255
57 input_data = preprocess(image_data)
58 input_data = input_data.cpu().numpy().squeeze()
59 tmp_input = MNN.Tensor((1, 3, 128, 128), MNN.Halide_Type_Float, input_data, MNN.Tensor_DimensionType_Caffe) 51 tmp_input = MNN.Tensor((1, 3, 128, 128), MNN.Halide_Type_Float, input_data, MNN.Tensor_DimensionType_Caffe)
60 input_tensor.copyFrom(tmp_input) 52 input_tensor.copyFrom(tmp_input)
61 interpreter.runSession(session) 53 interpreter.runSession(session)
...@@ -81,9 +73,7 @@ def camera_infer_mnn(mnn_model_path, camera_id): ...@@ -81,9 +73,7 @@ def camera_infer_mnn(mnn_model_path, camera_id):
81 image_data = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) 73 image_data = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
82 image_data = Image.fromarray(image_data) 74 image_data = Image.fromarray(image_data)
83 image_data = keep_shape_resize(image_data, 128) 75 image_data = keep_shape_resize(image_data, 128)
84 preprocess = transforms.Compose([transforms.ToTensor()]) 76 input_data = np.array(image_data).astype(np.float32).transpose((2, 0, 1)) / 255
85 input_data = preprocess(image_data)
86 input_data = input_data.cpu().numpy().squeeze()
87 tmp_input = MNN.Tensor((1, 3, 128, 128), MNN.Halide_Type_Float, input_data, MNN.Tensor_DimensionType_Caffe) 77 tmp_input = MNN.Tensor((1, 3, 128, 128), MNN.Halide_Type_Float, input_data, MNN.Tensor_DimensionType_Caffe)
88 input_tensor.copyFrom(tmp_input) 78 input_tensor.copyFrom(tmp_input)
89 interpreter.runSession(session) 79 interpreter.runSession(session)
...@@ -98,14 +88,12 @@ def camera_infer_mnn(mnn_model_path, camera_id): ...@@ -98,14 +88,12 @@ def camera_infer_mnn(mnn_model_path, camera_id):
98 break 88 break
99 89
100 90
101
102 if __name__ == '__main__': 91 if __name__ == '__main__':
103 class_list = ['mask', 'no_mask'] 92 class_list = ['mask', 'no_mask']
104 image_path = 'test_image/mask_2997.jpg' 93 image_path = 'test_image/mask_2995.jpg'
105 mnn_model_path = 'mobilenet_v2.mnn' 94 mnn_model_path = 'cls_abnormal_face_mnn_1.0.0_v0.0.1.mnn'
106 # image 95 # image
107 # image=image_infer_mnn(mnn_model_path,image_path,class_list) 96 image = image_infer_mnn(mnn_model_path, image_path, class_list)
108 # image.show() 97 image.show()
109
110 # camera 98 # camera
111 camera_infer_mnn(mnn_model_path, 0) 99 # camera_infer_mnn(mnn_model_path,0)
......
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