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.gitattributes CHANGED
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  *.ftz filter=lfs diff=lfs merge=lfs -text
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  *.gz filter=lfs diff=lfs merge=lfs -text
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  *.h5 filter=lfs diff=lfs merge=lfs -text
 
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  *.joblib filter=lfs diff=lfs merge=lfs -text
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  *.lfs.* filter=lfs diff=lfs merge=lfs -text
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  *.mlmodel filter=lfs diff=lfs merge=lfs -text
 
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  *.ftz filter=lfs diff=lfs merge=lfs -text
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  *.gz filter=lfs diff=lfs merge=lfs -text
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  *.h5 filter=lfs diff=lfs merge=lfs -text
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+ *.t7 filter=lfs diff=lfs merge=lfs -text
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  *.joblib filter=lfs diff=lfs merge=lfs -text
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  *.lfs.* filter=lfs diff=lfs merge=lfs -text
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  *.mlmodel filter=lfs diff=lfs merge=lfs -text
Dockerfile ADDED
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+ FROM python:3.11
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+
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+ COPY requirements.txt requirements.txt
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+
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+ RUN apt-get update && apt-get install -y --no-install-recommends \
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+ bzip2 \
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+ g++ \
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+ git \
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+ graphviz \
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+ libgl1-mesa-glx \
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+ libhdf5-dev \
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+ openmpi-bin \
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+ wget \
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+ python3-tk && \
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+ rm -rf /var/lib/apt/lists/*
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+
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+ RUN pip install --upgrade pip
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+
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+ RUN pip install --no-cache-dir -r requirements.txt
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+
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+ RUN useradd -m -u 1000 myuser
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+
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+ USER myuser
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+
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+ COPY --chown=myuser app app
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+
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+ EXPOSE 8001
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+
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+ CMD ["python", "app/main.py"]
app/Hackathon_setup/__init__.py ADDED
File without changes
app/Hackathon_setup/exp_recognition.py ADDED
@@ -0,0 +1,71 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import numpy as np
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+ import cv2
3
+ from matplotlib import pyplot as plt
4
+ import torch
5
+ # In the below line,remove '.' while working on your local system.However Make sure that '.' is present before face_recognition_model while uploading to the server, Do not remove it.
6
+ from .exp_recognition_model import *
7
+ from PIL import Image
8
+ import base64
9
+ import io
10
+ import os
11
+ ## Add more imports if required
12
+
13
+ #############################################################################################################################
14
+ # Caution: Don't change any of the filenames, function names and definitions #
15
+ # Always use the current_path + file_name for refering any files, without it we cannot access files on the server #
16
+ #############################################################################################################################
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+
18
+ # Current_path stores absolute path of the file from where it runs.
19
+ current_path = os.path.dirname(os.path.abspath(__file__))
20
+
21
+
22
+ #1) The below function is used to detect faces in the given image.
23
+ #2) It returns only one image which has maximum area out of all the detected faces in the photo.
24
+ #3) If no face is detected,then it returns zero(0).
25
+
26
+ def detected_face(image):
27
+ eye_haar = current_path + '/haarcascade_eye.xml'
28
+ face_haar = current_path + '/haarcascade_frontalface_default.xml'
29
+ face_cascade = cv2.CascadeClassifier(face_haar)
30
+ eye_cascade = cv2.CascadeClassifier(eye_haar)
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+ gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
32
+ faces = face_cascade.detectMultiScale(gray, 1.3, 5)
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+ face_areas=[]
34
+ images = []
35
+ required_image=0
36
+ for i, (x,y,w,h) in enumerate(faces):
37
+ face_cropped = gray[y:y+h, x:x+w]
38
+ face_areas.append(w*h)
39
+ images.append(face_cropped)
40
+ required_image = images[np.argmax(face_areas)]
41
+ required_image = Image.fromarray(required_image)
42
+ return required_image
43
+
44
+
45
+ #1) Images captured from mobile is passed as parameter to the below function in the API call, It returns the Expression detected by your network.
46
+ #2) The image is passed to the function in base64 encoding, Code for decoding the image is provided within the function.
47
+ #3) Define an object to your network here in the function and load the weight from the trained network, set it in evaluation mode.
48
+ #4) Perform necessary transformations to the input(detected face using the above function), this should return the Expression in string form ex: "Anger"
49
+ #5) For loading your model use the current_path+'your model file name', anyhow detailed example is given in comments to the function
50
+ ##Caution: Don't change the definition or function name; for loading the model use the current_path for path example is given in comments to the function
51
+ def get_expression(img):
52
+ device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
53
+
54
+ ##########################################################################################
55
+ ##Example for loading a model using weight state dictionary: ##
56
+ ## face_det_net = facExpRec() #Example Network ##
57
+ ## model = torch.load(current_path + '/exp_recognition_net.t7', map_location=device) ##
58
+ ## face_det_net.load_state_dict(model['net_dict']) ##
59
+ ## ##
60
+ ##current_path + '/<network_definition>' is path of the saved model if present in ##
61
+ ##the same path as this file, we recommend to put in the same directory ##
62
+ ##########################################################################################
63
+ ##########################################################################################
64
+
65
+ face = detected_face(img)
66
+ if face==0:
67
+ face = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY))
68
+
69
+ # YOUR CODE HERE, return expression using your model
70
+
71
+ return "YET TO BE CODED"
app/Hackathon_setup/exp_recognition_model.py ADDED
@@ -0,0 +1,31 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+ import torchvision
3
+ import torch.nn as nn
4
+ from torchvision import transforms
5
+ ## Add more imports if required
6
+
7
+ ####################################################################################################################
8
+ # Define your model and transform and all necessary helper functions here #
9
+ # They will be imported to the exp_recognition.py file #
10
+ ####################################################################################################################
11
+
12
+ # Definition of classes as dictionary
13
+ classes = {0: 'ANGER', 1: 'DISGUST', 2: 'FEAR', 3: 'HAPPINESS', 4: 'NEUTRAL', 5: 'SADNESS', 6: 'SURPRISE'}
14
+
15
+ # Example Network
16
+ class facExpRec(torch.nn.Module):
17
+ def __init__(self):
18
+ pass # remove 'pass' once you have written your code
19
+ #YOUR CODE HERE
20
+
21
+ def forward(self, x):
22
+ pass # remove 'pass' once you have written your code
23
+ #YOUR CODE HERE
24
+
25
+ # Sample Helper function
26
+ def rgb2gray(image):
27
+ return image.convert('L')
28
+
29
+ # Sample Transformation function
30
+ #YOUR CODE HERE for changing the Transformation values.
31
+ trnscm = transforms.Compose([rgb2gray, transforms.Resize((48,48)), transforms.ToTensor()])
app/Hackathon_setup/face_recognition.py ADDED
@@ -0,0 +1,106 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import numpy as np
2
+ import cv2
3
+ from matplotlib import pyplot as plt
4
+ import torch
5
+ # In the below line,remove '.' while working on your local system. However Make sure that '.' is present before face_recognition_model while uploading to the server, Do not remove it.
6
+ from .face_recognition_model import *
7
+ from PIL import Image
8
+ import base64
9
+ import io
10
+ import os
11
+ import joblib
12
+ import pickle
13
+ # Add more imports if required
14
+
15
+
16
+
17
+ ###########################################################################################################################################
18
+ # Caution: Don't change any of the filenames, function names and definitions #
19
+ # Always use the current_path + file_name for refering any files, without it we cannot access files on the server #
20
+ ###########################################################################################################################################
21
+
22
+ # Current_path stores absolute path of the file from where it runs.
23
+ current_path = os.path.dirname(os.path.abspath(__file__))
24
+
25
+ #1) The below function is used to detect faces in the given image.
26
+ #2) It returns only one image which has maximum area out of all the detected faces in the photo.
27
+ #3) If no face is detected,then it returns zero(0).
28
+
29
+ def detected_face(image):
30
+ eye_haar = current_path + '/haarcascade_eye.xml'
31
+ face_haar = current_path + '/haarcascade_frontalface_default.xml'
32
+ face_cascade = cv2.CascadeClassifier(face_haar)
33
+ eye_cascade = cv2.CascadeClassifier(eye_haar)
34
+ gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
35
+ faces = face_cascade.detectMultiScale(gray, 1.3, 5)
36
+ face_areas=[]
37
+ images = []
38
+ required_image=0
39
+ for i, (x,y,w,h) in enumerate(faces):
40
+ face_cropped = gray[y:y+h, x:x+w]
41
+ face_areas.append(w*h)
42
+ images.append(face_cropped)
43
+ required_image = images[np.argmax(face_areas)]
44
+ required_image = Image.fromarray(required_image)
45
+ return required_image
46
+
47
+
48
+ #1) Images captured from mobile is passed as parameter to the below function in the API call. It returns the similarity measure between given images.
49
+ #2) The image is passed to the function in base64 encoding, Code for decoding the image is provided within the function.
50
+ #3) Define an object to your siamese network here in the function and load the weight from the trained network, set it in evaluation mode.
51
+ #4) Get the features for both the faces from the network and return the similarity measure, Euclidean,cosine etc can be it. But choose the Relevant measure.
52
+ #5) For loading your model use the current_path+'your model file name', anyhow detailed example is given in comments to the function
53
+ #Caution: Don't change the definition or function name; for loading the model use the current_path for path example is given in comments to the function
54
+ def get_similarity(img1, img2):
55
+ device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
56
+
57
+ det_img1 = detected_face(img1)
58
+ det_img2 = detected_face(img2)
59
+ if(det_img1 == 0 or det_img2 == 0):
60
+ det_img1 = Image.fromarray(cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY))
61
+ det_img2 = Image.fromarray(cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY))
62
+ face1 = trnscm(det_img1).unsqueeze(0)
63
+ face2 = trnscm(det_img2).unsqueeze(0)
64
+ ##########################################################################################
65
+ ##Example for loading a model using weight state dictionary: ##
66
+ ## feature_net = light_cnn() #Example Network ##
67
+ ## model = torch.load(current_path + '/siamese_model.t7', map_location=device) ##
68
+ ## feature_net.load_state_dict(model['net_dict']) ##
69
+ ## ##
70
+ ##current_path + '/<network_definition>' is path of the saved model if present in ##
71
+ ##the same path as this file, we recommend to put in the same directory ##
72
+ ##########################################################################################
73
+ ##########################################################################################
74
+ feature_net = face_recognition_model.Siamese()
75
+ model = torch.load('./siamese_model.t7', map_location=device)
76
+ feature_net.load_state_dict(model['net_dict'])
77
+
78
+
79
+
80
+ output1, output2 = feature_net(Variable(face1).to(device), Variable(face2).to(device))
81
+ euclidean_distance = F.pairwise_distance(output1, output2)
82
+
83
+
84
+ return euclidean_distance.item()
85
+ # YOUR CODE HERE, load the model
86
+
87
+ # YOUR CODE HERE, return similarity measure using your model
88
+
89
+
90
+
91
+ #1) Image captured from mobile is passed as parameter to this function in the API call, It returns the face class in the string form ex: "Person1"
92
+ #2) The image is passed to the function in base64 encoding, Code to decode the image provided within the function
93
+ #3) Define an object to your network here in the function and load the weight from the trained network, set it in evaluation mode
94
+ #4) Perform necessary transformations to the input(detected face using the above function).
95
+ #5) Along with the siamese, you need the classifier as well, which is to be finetuned with the faces that you are training
96
+ ##Caution: Don't change the definition or function name; for loading the model use the current_path for path example is given in comments to the function
97
+ def get_face_class(img1):
98
+ device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
99
+
100
+ det_img1 = detected_face(img1)
101
+ if(det_img1 == 0):
102
+ det_img1 = Image.fromarray(cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY))
103
+ ##YOUR CODE HERE, return face class here
104
+ ##Hint: you need a classifier finetuned for your classes, it takes o/p of siamese as i/p to it
105
+ ##Better Hint: Siamese experiment is covered in one of the labs
106
+ return "YET TO BE CODED"
app/Hackathon_setup/face_recognition_model.py ADDED
@@ -0,0 +1,68 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import math
2
+ import torch
3
+ import torchvision
4
+ import torch.nn as nn
5
+ import torch.nn.functional as F
6
+ from torchvision import transforms
7
+ # Add more imports if required
8
+
9
+ # Sample Transformation function
10
+ # YOUR CODE HERE for changing the Transformation values.
11
+ trnscm = transforms.Compose([transforms.Resize((100,100)), transforms.ToTensor()])
12
+
13
+ ##Example Network
14
+ class Siamese(torch.nn.Module):
15
+ def __init__(self):
16
+ super(Siamese, self).__init__()
17
+ super(SiameseNetwork, self).__init__()
18
+ self.cnn1 = nn.Sequential(
19
+ nn.ReflectionPad2d(1),
20
+ # Pads the input tensor using the reflection of the input boundary, it similar to the padding.
21
+ nn.Conv2d(1, 4, kernel_size=3),
22
+ nn.ReLU(inplace=True),
23
+ nn.BatchNorm2d(4),
24
+
25
+ nn.ReflectionPad2d(1),
26
+ nn.Conv2d(4, 8, kernel_size=3),
27
+ nn.ReLU(inplace=True),
28
+ nn.BatchNorm2d(8),
29
+
30
+ nn.ReflectionPad2d(1),
31
+ nn.Conv2d(8, 8, kernel_size=3),
32
+ nn.ReLU(inplace=True),
33
+ nn.BatchNorm2d(8),
34
+ )
35
+
36
+ self.fc1 = nn.Sequential(
37
+ nn.Linear(8 * 100 * 100, 500),
38
+ nn.ReLU(inplace=True),
39
+
40
+ nn.Linear(500, 500),
41
+ nn.ReLU(inplace=True),
42
+
43
+ nn.Linear(500, 5))
44
+ #YOUR CODE HERE
45
+ # forward_once is for one image. This can be used while classifying the face images
46
+ # forward_once is for one image. This can be used while classifying the face images
47
+ def forward_once(self, x):
48
+ output = self.cnn1(x)
49
+ output = output.view(output.size()[0], -1)
50
+ output = self.fc1(output)
51
+ return output
52
+
53
+ def forward(self, x):
54
+ output1 = self.forward_once(input1)
55
+ output2 = self.forward_once(input2)
56
+ return output1, output2
57
+ # remove 'pass' once you have written your code
58
+ #YOUR CODE HERE
59
+
60
+ ##########################################################################################################
61
+ ## Sample classification network (Specify if you are using a pytorch classifier during the training) ##
62
+ ## classifier = nn.Sequential(nn.Linear(64, 64), nn.BatchNorm1d(64), nn.ReLU(), nn.Linear...) ##
63
+ ##########################################################################################################
64
+
65
+ # YOUR CODE HERE for pytorch classifier
66
+
67
+ # Definition of classes as dictionary
68
+ classes = ['person1','person2','person3','person4','person5','person6','person7']
app/Hackathon_setup/haarcascade_eye.xml ADDED
The diff for this file is too large to render. See raw diff
 
app/Hackathon_setup/haarcascade_frontalface_default.xml ADDED
The diff for this file is too large to render. See raw diff
 
app/Hackathon_setup/lbpcascade_frontalface.xml ADDED
@@ -0,0 +1,1505 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <?xml version="1.0"?>
2
+ <!--
3
+ number of positive samples 3000
4
+ number of negative samples 1500
5
+ -->
6
+ <opencv_storage>
7
+ <cascade type_id="opencv-cascade-classifier">
8
+ <stageType>BOOST</stageType>
9
+ <featureType>LBP</featureType>
10
+ <height>24</height>
11
+ <width>24</width>
12
+ <stageParams>
13
+ <boostType>GAB</boostType>
14
+ <minHitRate>0.9950000047683716</minHitRate>
15
+ <maxFalseAlarm>0.5000000000000000</maxFalseAlarm>
16
+ <weightTrimRate>0.9500000000000000</weightTrimRate>
17
+ <maxDepth>1</maxDepth>
18
+ <maxWeakCount>100</maxWeakCount></stageParams>
19
+ <featureParams>
20
+ <maxCatCount>256</maxCatCount></featureParams>
21
+ <stageNum>20</stageNum>
22
+ <stages>
23
+ <!-- stage 0 -->
24
+ <_>
25
+ <maxWeakCount>3</maxWeakCount>
26
+ <stageThreshold>-0.7520892024040222</stageThreshold>
27
+ <weakClassifiers>
28
+ <!-- tree 0 -->
29
+ <_>
30
+ <internalNodes>
31
+ 0 -1 46 -67130709 -21569 -1426120013 -1275125205 -21585
32
+ -16385 587145899 -24005</internalNodes>
33
+ <leafValues>
34
+ -0.6543210148811340 0.8888888955116272</leafValues></_>
35
+ <!-- tree 1 -->
36
+ <_>
37
+ <internalNodes>
38
+ 0 -1 13 -163512766 -769593758 -10027009 -262145 -514457854
39
+ -193593353 -524289 -1</internalNodes>
40
+ <leafValues>
41
+ -0.7739216089248657 0.7278633713722229</leafValues></_>
42
+ <!-- tree 2 -->
43
+ <_>
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+ <!-- stage 1 -->
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+ <_>
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+ <maxWeakCount>4</maxWeakCount>
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+ <stageThreshold>-0.4872078299522400</stageThreshold>
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+ <weakClassifiers>
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+ <!-- tree 0 -->
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+ <_>
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+ <internalNodes>
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+ <!-- tree 1 -->
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+ <_>
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+ <!-- tree 2 -->
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+ <!-- tree 3 -->
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+ <_>
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+ <!-- stage 2 -->
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+ <_>
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+ <maxWeakCount>4</maxWeakCount>
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+ <stageThreshold>-1.1592328548431396</stageThreshold>
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+ <weakClassifiers>
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+ <!-- tree 0 -->
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+ <_>
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+ <!-- tree 1 -->
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+ <_>
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+ <!-- tree 2 -->
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+ <!-- tree 3 -->
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+ <!-- stage 3 -->
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+ <_>
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+ <maxWeakCount>5</maxWeakCount>
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+ <stageThreshold>-0.7562355995178223</stageThreshold>
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+ <weakClassifiers>
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+ <!-- tree 0 -->
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+ <_>
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+ <!-- tree 1 -->
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+ <_>
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+ <!-- tree 2 -->
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+ <_>
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+ <!-- tree 3 -->
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+ <!-- tree 4 -->
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155
+ <!-- stage 4 -->
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+ <_>
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+ <stageThreshold>-0.8085358142852783</stageThreshold>
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+ <weakClassifiers>
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+ <!-- tree 0 -->
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+ <_>
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+ <!-- tree 1 -->
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+ <!-- tree 2 -->
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+ <!-- tree 3 -->
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+ <_>
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+ <!-- tree 4 -->
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+ <_>
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195
+ <!-- stage 5 -->
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+ <_>
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+ <!-- tree 0 -->
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+ -21846</internalNodes>
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+ <!-- tree 1 -->
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+ <!-- tree 2 -->
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+ <!-- tree 3 -->
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+ <_>
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+ <!-- tree 4 -->
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+ <_>
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235
+ <!-- stage 6 -->
236
+ <_>
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+ <!-- tree 0 -->
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+ <_>
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+ <!-- tree 1 -->
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+ <_>
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+ <!-- tree 2 -->
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+ <_>
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+ <!-- tree 3 -->
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+ <!-- tree 4 -->
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+ <!-- stage 7 -->
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+ <!-- tree 0 -->
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+ <!-- tree 1 -->
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+ <_>
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+ <!-- tree 2 -->
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+ <!-- tree 3 -->
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+ <!-- tree 4 -->
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+ <!-- tree 5 -->
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322
+ <!-- stage 8 -->
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+ <!-- tree 0 -->
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+ <!-- tree 1 -->
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+ <!-- tree 2 -->
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+ <!-- tree 3 -->
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+ <!-- tree 4 -->
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+ <!-- tree 5 -->
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+ <!-- tree 6 -->
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+ <!-- stage 9 -->
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+ <!-- tree 0 -->
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+ <!-- tree 1 -->
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+ <!-- tree 2 -->
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+ <!-- tree 3 -->
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+ <!-- tree 4 -->
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+ <!-- tree 5 -->
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+ <!-- tree 6 -->
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+ <!-- stage 10 -->
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+ <!-- tree 0 -->
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+ <!-- tree 1 -->
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+ <!-- tree 2 -->
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+ <!-- tree 3 -->
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+ <!-- tree 4 -->
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+ <!-- tree 5 -->
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+ <!-- tree 6 -->
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+ <!-- stage 11 -->
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+ <!-- tree 1 -->
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+ <!-- tree 2 -->
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+ <!-- tree 3 -->
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+ <!-- tree 5 -->
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+ <!-- stage 12 -->
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+ <!-- tree 0 -->
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+ <!-- tree 1 -->
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+ <!-- tree 2 -->
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+ <!-- tree 3 -->
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+ <!-- tree 4 -->
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+ <!-- tree 6 -->
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+ <!-- tree 7 -->
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+ <!-- tree 1 -->
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+ <!-- tree 5 -->
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+ <!-- tree 6 -->
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+ <!-- tree 7 -->
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+ <!-- tree 8 -->
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+ <!-- stage 14 -->
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+ <!-- tree 1 -->
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+ <!-- tree 2 -->
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+ <!-- tree 3 -->
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+ <!-- tree 7 -->
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+ <!-- stage 15 -->
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+ <!-- tree 8 -->
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+ <!-- stage 16 -->
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app/Hackathon_setup/siamese_model.t7 ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:89ba48883946d0b23823b102e7f5faea3bd0a2d9e3e43e42b2c81cba73f46098
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+ size 161026623
app/__init__.py ADDED
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+ __version__ = "0.0.1"
app/config.py ADDED
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+ import sys
2
+ from typing import List
3
+
4
+ from pydantic import AnyHttpUrl, BaseSettings
5
+
6
+ class Settings(BaseSettings):
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+ API_V1_STR: str = "/api/v1"
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+
9
+ # Meta
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+
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+ # BACKEND_CORS_ORIGINS is a comma-separated list of origins
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+ # e.g: http://localhost,http://localhost:4200,http://localhost:3000
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+ BACKEND_CORS_ORIGINS: List[AnyHttpUrl] = [
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+ "http://localhost:3000", # type: ignore
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+ "http://localhost:8000", # type: ignore
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+ "https://localhost:3000", # type: ignore
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+ "https://localhost:8000", # type: ignore
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+ ]
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+
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+ PROJECT_NAME: str = "Recognition API"
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+
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+ class Config:
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+ case_sensitive = True
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+
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+ settings = Settings()
app/main.py ADDED
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+ import sys
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+ from pathlib import Path
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+ sys.path.append(str(Path(__file__).resolve().parent.parent))
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+ #print(sys.path)
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+ from typing import Any
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+
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+ from fastapi import FastAPI, Request, APIRouter, File, UploadFile
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+ from fastapi.staticfiles import StaticFiles
9
+ from fastapi.templating import Jinja2Templates
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+ from fastapi.middleware.cors import CORSMiddleware
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+ from app.config import settings
12
+ from app import __version__
13
+ from app.Hackathon_setup import face_recognition, exp_recognition
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+
15
+ import numpy as np
16
+ from PIL import Image
17
+
18
+
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+ app = FastAPI(
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+ title=settings.PROJECT_NAME, openapi_url=f"{settings.API_V1_STR}/openapi.json"
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+ )
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+
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+ # To store files uploaded by users
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+ app.mount("/static", StaticFiles(directory="app/static"), name="static")
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+
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+ # To access Templates directory
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+ templates = Jinja2Templates(directory="app/templates")
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+
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+ simi_filename1 = None
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+ simi_filename2 = None
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+ face_rec_filename = None
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+ expr_rec_filename = None
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+
34
+
35
+ #################################### Home Page endpoints #################################################
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+ @app.get("/")
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+ async def root(request: Request):
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+ return templates.TemplateResponse("index.html", {'request': request,})
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+
40
+
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+ #################################### Face Similarity endpoints #################################################
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+ @app.get("/similarity/")
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+ async def similarity_root(request: Request):
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+ return templates.TemplateResponse("similarity.html", {'request': request,})
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+
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+
47
+ @app.post("/predict_similarity/")
48
+ async def create_upload_files(request: Request, file1: UploadFile = File(...), file2: UploadFile = File(...)):
49
+ global simi_filename1
50
+ global simi_filename2
51
+
52
+ if 'image' in file1.content_type:
53
+ contents = await file1.read()
54
+ simi_filename1 = 'app/static/' + file1.filename
55
+ with open(simi_filename1, 'wb') as f:
56
+ f.write(contents)
57
+
58
+ if 'image' in file2.content_type:
59
+ contents = await file2.read()
60
+ simi_filename2 = 'app/static/' + file2.filename
61
+ with open(simi_filename2, 'wb') as f:
62
+ f.write(contents)
63
+
64
+ img1 = Image.open(simi_filename1)
65
+ img1 = np.array(img1).reshape(img1.size[1], img1.size[0], 3).astype(np.uint8)
66
+
67
+ img2 = Image.open(simi_filename2)
68
+ img2 = np.array(img2).reshape(img2.size[1], img2.size[0], 3).astype(np.uint8)
69
+
70
+ result = face_recognition.get_similarity(img1, img2)
71
+ #print(result)
72
+
73
+ return templates.TemplateResponse("predict_similarity.html", {"request": request,
74
+ "result": np.round(result, 3),
75
+ "simi_filename1": '../static/'+file1.filename,
76
+ "simi_filename2": '../static/'+file2.filename,})
77
+
78
+
79
+ #################################### Face Recognition endpoints #################################################
80
+ @app.get("/face_recognition/")
81
+ async def face_recognition_root(request: Request):
82
+ return templates.TemplateResponse("face_recognition.html", {'request': request,})
83
+
84
+
85
+ @app.post("/predict_face_recognition/")
86
+ async def create_upload_files(request: Request, file3: UploadFile = File(...)):
87
+ global face_rec_filename
88
+
89
+ if 'image' in file3.content_type:
90
+ contents = await file3.read()
91
+ face_rec_filename = 'app/static/' + file3.filename
92
+ with open(face_rec_filename, 'wb') as f:
93
+ f.write(contents)
94
+
95
+ img1 = Image.open(face_rec_filename)
96
+ img1 = np.array(img1).reshape(img1.size[1], img1.size[0], 3).astype(np.uint8)
97
+
98
+ result = face_recognition.get_face_class(img1)
99
+ print(result)
100
+
101
+ return templates.TemplateResponse("predict_face_recognition.html", {"request": request,
102
+ "result": result,
103
+ "face_rec_filename": '../static/'+file3.filename,})
104
+
105
+
106
+ #################################### Expresion Recognition endpoints #################################################
107
+ @app.get("/expr_recognition/")
108
+ async def expr_recognition_root(request: Request):
109
+ return templates.TemplateResponse("expr_recognition.html", {'request': request,})
110
+
111
+
112
+ @app.post("/predict_expr_recognition/")
113
+ async def create_upload_files(request: Request, file4: UploadFile = File(...)):
114
+ global expr_rec_filename
115
+
116
+ if 'image' in file4.content_type:
117
+ contents = await file4.read()
118
+ expr_rec_filename = 'app/static/' + file4.filename
119
+ with open(expr_rec_filename, 'wb') as f:
120
+ f.write(contents)
121
+
122
+ img1 = Image.open(expr_rec_filename)
123
+ img1 = np.array(img1).reshape(img1.size[1], img1.size[0], 3).astype(np.uint8)
124
+
125
+ result = exp_recognition.get_expression(img1)
126
+ print(result)
127
+
128
+ return templates.TemplateResponse("predict_expr_recognition.html", {"request": request,
129
+ "result": result,
130
+ "expr_rec_filename": '../static/'+file4.filename,})
131
+
132
+
133
+
134
+ # Set all CORS enabled origins
135
+ if settings.BACKEND_CORS_ORIGINS:
136
+ app.add_middleware(
137
+ CORSMiddleware,
138
+ allow_origins=[str(origin) for origin in settings.BACKEND_CORS_ORIGINS],
139
+ allow_credentials=True,
140
+ allow_methods=["*"],
141
+ allow_headers=["*"],
142
+ )
143
+
144
+
145
+ # Start app
146
+ if __name__ == "__main__":
147
+ import uvicorn
148
+ uvicorn.run(app, host="0.0.0.0", port=8001)
app/static/Person1_1697805233.jpg ADDED
app/templates/expr_recognition.html ADDED
@@ -0,0 +1,32 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <!DOCTYPE html>
2
+ <html lang="en">
3
+ <head>
4
+ <title>Index</title>
5
+ </head>
6
+ <body>
7
+ <div>
8
+ <h1 style="background-color:LightGray;">
9
+ <center>Expression Recognition</center>
10
+ </h1>
11
+ </div>
12
+ <div>
13
+ <fieldset>
14
+ <ul>
15
+ <!li>
16
+ <br>
17
+ <form action="/predict_expr_recognition/" enctype="multipart/form-data" method="post">
18
+ <span style="font-weight:bold;font-family:sans-serif">Upload Image:</span> <br><br>
19
+ <input name="file4" type="file" onchange="readURL(this);" />
20
+ <br><br><br>
21
+ <button type="submit">Recognize Expression</button>
22
+ </form>
23
+ <!/li>
24
+ <br><br>
25
+ <form action="/" method="get">
26
+ <button type="submit">Home</button>
27
+ </form>
28
+ </ul>
29
+ </fieldset>
30
+ </div>
31
+ </body>
32
+ </html>
app/templates/face_recognition.html ADDED
@@ -0,0 +1,32 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <!DOCTYPE html>
2
+ <html lang="en">
3
+ <head>
4
+ <title>Index</title>
5
+ </head>
6
+ <body>
7
+ <div>
8
+ <h1 style="background-color:LightGray;">
9
+ <center>Face Recognition</center>
10
+ </h1>
11
+ </div>
12
+ <div>
13
+ <fieldset>
14
+ <ul>
15
+ <!li>
16
+ <br>
17
+ <form action="/predict_face_recognition/" enctype="multipart/form-data" method="post">
18
+ <span style="font-weight:bold;font-family:sans-serif">Upload Image:</span> <br><br>
19
+ <input name="file3" type="file" onchange="readURL(this);" />
20
+ <br><br><br>
21
+ <button type="submit">Recognize Face</button>
22
+ </form>
23
+ <!/li>
24
+ <br><br>
25
+ <form action="/" method="get">
26
+ <button type="submit">Home</button>
27
+ </form>
28
+ </ul>
29
+ </fieldset>
30
+ </div>
31
+ </body>
32
+ </html>
app/templates/index.html ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <!DOCTYPE html>
2
+ <html lang="en">
3
+ <head>
4
+ <title>Index</title>
5
+ </head>
6
+ <body>
7
+ <div>
8
+ <h1 style="background-color:LightGray;">
9
+ <center>Recognition Application</center>
10
+ </h1>
11
+ </div>
12
+ <div>
13
+ <fieldset>
14
+ <ul>
15
+ <li><span style="font-weight:bold;font-family:sans-serif">Select a task:</span>
16
+ <br><br><br>
17
+ <form action="{{ url_for('similarity_root') }}"><button>Face Similarity</button></form>
18
+ <br><br>
19
+ <form action="{{ url_for('face_recognition_root') }}"><button>Face Recognition</button></form>
20
+ <br><br>
21
+ <form action="{{ url_for('expr_recognition_root') }}"><button>Expression Recognition</button></form>
22
+ <br>
23
+ </li>
24
+ <br>
25
+ </ul>
26
+ </fieldset>
27
+ </div>
28
+ </body>
29
+ </html>
app/templates/predict_expr_recognition.html ADDED
@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <!DOCTYPE html>
2
+ <html lang="en">
3
+ <head>
4
+ <title>Predict</title>
5
+ </head>
6
+ <body>
7
+ <div>
8
+ <h1 style="background-color:LightGray;">
9
+ <center>Expression Recognition</center>
10
+ </h1>
11
+ </div>
12
+ <div>
13
+ <fieldset>
14
+ <h2>
15
+ <center>
16
+ <span style="font-weight:bold;font-family:sans-serif">Prediction: </span>
17
+ <span style="font-weight:bold;color:blue"> {{result}}</span>
18
+ </center>
19
+ </h2>
20
+ <h3><center><span style="font-weight:bold;font-family:sans-serif">Input image:</span></Input></center></h3>
21
+ <p>
22
+ <center>
23
+ <img src="{{expr_rec_filename}}" alt={{expr_rec_filename1}} width='150' height='150'>
24
+ </center>
25
+ </p>
26
+ <br>
27
+ <form action="/expr_recognition/" method="get">
28
+ <center><button type="submit">Check Another Input</button></center>
29
+ </form>
30
+ <br>
31
+ <form action="/" method="get">
32
+ <center><button type="submit">Home</button></center>
33
+ </form>
34
+ </fieldset>
35
+ </div>
36
+ </body>
37
+ </html>
app/templates/predict_face_recognition.html ADDED
@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <!DOCTYPE html>
2
+ <html lang="en">
3
+ <head>
4
+ <title>Predict</title>
5
+ </head>
6
+ <body>
7
+ <div>
8
+ <h1 style="background-color:LightGray;">
9
+ <center>Face Recognition</center>
10
+ </h1>
11
+ </div>
12
+ <div>
13
+ <fieldset>
14
+ <h2>
15
+ <center>
16
+ <span style="font-weight:bold;font-family:sans-serif">Prediction: </span>
17
+ <span style="font-weight:bold;color:blue"> {{result}}</span>
18
+ </center>
19
+ </h2>
20
+ <h3><center><span style="font-weight:bold;font-family:sans-serif">Input image:</span></Input></center></h3>
21
+ <p>
22
+ <center>
23
+ <img src="{{face_rec_filename}}" alt={{face_rec_filename1}} width='150' height='150'>
24
+ </center>
25
+ </p>
26
+ <br>
27
+ <form action="/face_recognition/" method="get">
28
+ <center><button type="submit">Check Another Input</button></center>
29
+ </form>
30
+ <br>
31
+ <form action="/" method="get">
32
+ <center><button type="submit">Home</button></center>
33
+ </form>
34
+ </fieldset>
35
+ </div>
36
+ </body>
37
+ </html>
app/templates/predict_similarity.html ADDED
@@ -0,0 +1,38 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <!DOCTYPE html>
2
+ <html lang="en">
3
+ <head>
4
+ <title>Predict</title>
5
+ </head>
6
+ <body>
7
+ <div>
8
+ <h1 style="background-color:LightGray;">
9
+ <center>Face Similarity</center>
10
+ </h1>
11
+ </div>
12
+ <div>
13
+ <fieldset>
14
+ <h2>
15
+ <center>
16
+ <span style="font-weight:bold;font-family:sans-serif">Dissimilarity:</span>
17
+ <span style="font-weight:bold;color:blue"> {{result}}</span>
18
+ </center>
19
+ </h2>
20
+ <h3><center><span style="font-weight:bold;font-family:sans-serif">Input images:</span></Input></center></h3>
21
+ <p>
22
+ <center>
23
+ <img src="{{simi_filename1}}" alt={{simi_filename1}} width='150' height='150'>
24
+ <img src="{{simi_filename2}}" alt={{simi_filename2}} width='150' height='150'>
25
+ </center>
26
+ </p>
27
+ <br>
28
+ <form action="/similarity/" method="get">
29
+ <center><button type="submit">Check Another Input</button></center>
30
+ </form>
31
+ <br>
32
+ <form action="/" method="get">
33
+ <center><button type="submit">Home</button></center>
34
+ </form>
35
+ </fieldset>
36
+ </div>
37
+ </body>
38
+ </html>
app/templates/similarity.html ADDED
@@ -0,0 +1,35 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <!DOCTYPE html>
2
+ <html lang="en">
3
+ <head>
4
+ <title>Index</title>
5
+ </head>
6
+ <body>
7
+ <div>
8
+ <h1 style="background-color:LightGray;">
9
+ <center>Face Similarity</center>
10
+ </h1>
11
+ </div>
12
+ <div>
13
+ <fieldset>
14
+ <ul>
15
+ <!li>
16
+ <br>
17
+ <form action="/predict_similarity/" enctype="multipart/form-data" method="post">
18
+ <span style="font-weight:bold;font-family:sans-serif">Upload First Image:</span> <br><br>
19
+ <input name="file1" type="file" onchange="readURL(this);" />
20
+ <br><br><br>
21
+ <span style="font-weight:bold;font-family:sans-serif">Upload Second Image:</span> <br><br>
22
+ <input name="file2" type="file" onchange="readURL(this);" />
23
+ <br><br><br><br>
24
+ <button type="submit">Check Similarity</button>
25
+ </form>
26
+ <!/li>
27
+ <br><br>
28
+ <form action="/" method="get">
29
+ <button type="submit">Home</button>
30
+ </form>
31
+ </ul>
32
+ </fieldset>
33
+ </div>
34
+ </body>
35
+ </html>
requirements.txt ADDED
@@ -0,0 +1,17 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ uvicorn==0.17.6
2
+ fastapi==0.99.1
3
+ pydantic==1.10.10
4
+ requests==2.23.0
5
+ jinja2==3.1.2
6
+ python-multipart==0.0.6
7
+
8
+ scikit-learn==1.2.2
9
+ joblib==1.3.2
10
+ Pillow==9.4.0
11
+ torch==2.1.0
12
+ torchvision==0.16.0
13
+ matplotlib==3.7.1
14
+ numpy
15
+ pandas
16
+ #opencv-python==4.8.0.76
17
+ opencv-python==4.5.5.64