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mask_classifier/inference.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+
+#infrence code to detect font styles.
+
+
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.optim import lr_scheduler
+import numpy as np
+import torchvision
+from torchvision import datasets, models, transforms
+import matplotlib.pyplot as plt
+import time
+import os
+import pandas as pd
+from sklearn.model_selection import train_test_split
+import shutil
+import time
+import copy
+from PIL import Image
+import glob
+
+
+filepath = 'mask1_model_resnet50.pth'
+model = torch.load(filepath)
+
+
+class_names = ['with_mask',
+ 'without_mask'
+]
+
+
+
+def process_image(image):
+    ''' Scales, crops, and normalizes a PIL image for a PyTorch model,
+        returns an Numpy array
+    '''
+
+    # TODO: Process a PIL image for use in a PyTorch model
+    pil_image = Image.open(image)
+    #pil_image = image
+
+    image_transforms = transforms.Compose([
+        transforms.Resize(256),
+        transforms.CenterCrop(224),
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+    img = image_transforms(pil_image)
+    return img
+
+
+
+
+def classify_face(image_path):
+    device = torch.device("cpu")
+    img = process_image(image_path)
+    print('image_processed')
+    img = img.unsqueeze_(0)
+    img = img.float()
+
+    model.eval()
+    model.cpu()
+    output = model(img)
+    print(output,'##############output###########')
+    _, predicted = torch.max(output, 1)
+    print(predicted.data[0],"predicted")
+
+
+    classification1 = predicted.data[0]
+    index = int(classification1)
+    print(class_names[index])
+    return class_names[index]
+
+if __name__ == '__main__':
+    map_location=torch.device('cpu')
+    image_path = 'IMG20200402191409_02.jpg'
+    label = classify_face(image_path)
+    print("the label is", label)
+
+
+
+
+
+
+
+
+

mask_classifier/label_detect.py

@@ -0,0 +1,98 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+
+
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.optim import lr_scheduler
+import numpy as np
+import torchvision
+from torchvision import datasets, models, transforms
+import matplotlib.pyplot as plt
+import time
+import os
+import pandas as pd
+from sklearn.model_selection import train_test_split
+import shutil
+import time
+import copy
+from PIL import Image
+import glob
+import cv2
+
+
+filepath = 'mask1_model_resnet50.pth'
+model = torch.load(filepath)
+
+
+class_names = ['with_mask',
+ 'without_mask'
+]
+
+
+
+def process_image(image):
+    ''' Scales, crops, and normalizes a PIL image for a PyTorch model,
+        returns an Numpy array
+    '''
+
+    # TODO: Process a PIL image for use in a PyTorch model
+    #pil_image = Image.open(image)
+    pil_image = image
+
+    image_transforms = transforms.Compose([
+        transforms.Resize(256),
+        transforms.CenterCrop(224),
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+    img = image_transforms(pil_image)
+    return img
+
+
+
+
+def classify_face(image):
+    device = torch.device("cpu")
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    #im_pil = image.fromarray(image)
+    #image = np.asarray(im)
+    im = Image.fromarray(image)
+    image = process_image(im)
+    print('image_processed')
+    img = image.unsqueeze_(0)
+    img = image.float()
+
+    model.eval()
+    model.cpu()
+    output = model(image)
+    print(output,'##############output###########')
+    _, predicted = torch.max(output, 1)
+    print(predicted.data[0],"predicted")
+
+
+    classification1 = predicted.data[0]
+    index = int(classification1)
+    print(class_names[index])
+    return class_names[index]
+
+
+
+if __name__ == '__main__':
+    #map_location=torch.device('cpu')
+    image = cv2.imread('praj.jpg')
+    label = classify_face(image)
+    print("the label is", label)
+
+
+
+
+
+
+
+
+

mask_classifier/requirements.txt.

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+absl-py==0.9.0
+asn1crypto==1.3.0
+astor==0.8.0
+attrs==19.3.0
+backcall==0.1.0
+beautifulsoup4==4.8.2
+bleach==3.1.0
+blis==0.2.4
+Bottleneck==1.3.1
+certifi==2019.11.28
+cffi==1.14.0
+chardet==3.0.4
+click==7.1.1
+cryptography==2.8
+cycler==0.10.0
+cymem==2.0.2
+dataclasses==0.6
+decorator==4.4.1
+defusedxml==0.6.0
+efficientnet==1.1.0
+entrypoints==0.3
+fastai==1.0.60
+fastprogress==0.2.2
+gast==0.3.3
+glob3==0.0.1
+google-pasta==0.1.8
+grpcio==1.16.1
+h5py==2.10.0
+idna==2.8
+imageio==2.8.0
+importlib-metadata==1.5.0
+imutils==0.5.3
+ipykernel==5.1.4
+ipython==7.12.0
+ipython-genutils==0.2.0
+jedi==0.16.0
+Jinja2==2.11.1
+joblib==0.14.1
+jsonschema==3.2.0
+jupyter-client==5.3.4
+jupyter-core==4.6.1
+Keras==2.2.4
+Keras-Applications==1.0.8
+Keras-Preprocessing==1.1.0
+kiwisolver==1.1.0
+Markdown==3.1.1
+MarkupSafe==1.1.1
+matplotlib==3.1.3
+mistune==0.8.4
+mkl-fft==1.0.15
+mkl-random==1.1.0
+mkl-service==2.3.0
+murmurhash==1.0.2
+nbconvert==5.6.1
+nbformat==5.0.4
+networkx==2.4
+notebook==6.0.3
+numexpr==2.7.1
+numpy==1.18.1
+olefile==0.46
+packaging==20.1
+pandas==1.0.1
+pandocfilters==1.4.2
+parso==0.6.1
+pexpect==4.8.0
+pickleshare==0.7.5
+Pillow==7.1.0
+plac==0.9.6
+preshed==2.0.1
+prometheus-client==0.7.1
+prompt-toolkit==3.0.3
+protobuf==3.11.3
+ptyprocess==0.6.0
+pycparser==2.19
+pygame==1.9.6
+Pygments==2.5.2
+pyOpenSSL==19.1.0
+pyparsing==2.4.6
+pyrsistent==0.15.7
+PySocks==1.7.1
+python-dateutil==2.8.1
+pytz==2019.3
+PyWavelets==1.1.1
+PyYAML==5.3
+pyzmq==18.1.1
+requests==2.22.0
+scikit-image==0.16.2
+scikit-learn==0.22.1
+scipy==1.4.1
+Send2Trash==1.5.0
+six==1.14.0
+soupsieve==1.9.5
+spacy==2.1.8
+srsly==0.1.0
+tensorboard==1.14.0
+tensorflow==1.14.0
+tensorflow-estimator==1.14.0
+termcolor==1.1.0
+terminado==0.8.3
+testpath==0.4.4
+thinc==7.0.8
+torch==1.3.1
+torchvision==0.4.2
+tornado==6.0.3
+tqdm==4.42.1
+traitlets==4.3.3
+urllib3==1.25.8
+wasabi==0.2.2
+wcwidth==0.1.8
+webencodings==0.5.1
+Werkzeug==1.0.0
+wrapt==1.11.2
+zipp==2.2.0