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Pallavi Bhoj
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Parent(s):
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Upload code
Browse files- .gitattributes +1 -0
- Dockerfile +29 -0
- app/Hackathon_setup/__init__.py +0 -0
- app/Hackathon_setup/exp_recognition.py +71 -0
- app/Hackathon_setup/exp_recognition_model.py +31 -0
- app/Hackathon_setup/face_recognition.py +106 -0
- app/Hackathon_setup/face_recognition_model.py +68 -0
- app/Hackathon_setup/haarcascade_eye.xml +0 -0
- app/Hackathon_setup/haarcascade_frontalface_default.xml +0 -0
- app/Hackathon_setup/lbpcascade_frontalface.xml +1505 -0
- app/Hackathon_setup/siamese_model.t7 +3 -0
- app/__init__.py +1 -0
- app/config.py +25 -0
- app/main.py +148 -0
- app/static/Person1_1697805233.jpg +0 -0
- app/templates/expr_recognition.html +32 -0
- app/templates/face_recognition.html +32 -0
- app/templates/index.html +29 -0
- app/templates/predict_expr_recognition.html +37 -0
- app/templates/predict_face_recognition.html +37 -0
- app/templates/predict_similarity.html +38 -0
- app/templates/similarity.html +35 -0
- requirements.txt +17 -0
.gitattributes
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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
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Dockerfile
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FROM python:3.11
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COPY requirements.txt requirements.txt
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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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RUN pip install --upgrade pip
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RUN pip install --no-cache-dir -r requirements.txt
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RUN useradd -m -u 1000 myuser
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USER myuser
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COPY --chown=myuser app app
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EXPOSE 8001
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CMD ["python", "app/main.py"]
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app/Hackathon_setup/__init__.py
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app/Hackathon_setup/exp_recognition.py
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import numpy as np
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import cv2
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from matplotlib import pyplot as plt
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import torch
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# 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.
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from .exp_recognition_model import *
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from PIL import Image
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import base64
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import io
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import os
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## Add more imports if required
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#############################################################################################################################
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# Caution: Don't change any of the filenames, function names and definitions #
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# Always use the current_path + file_name for refering any files, without it we cannot access files on the server #
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#############################################################################################################################
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# Current_path stores absolute path of the file from where it runs.
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current_path = os.path.dirname(os.path.abspath(__file__))
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#1) The below function is used to detect faces in the given image.
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#2) It returns only one image which has maximum area out of all the detected faces in the photo.
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#3) If no face is detected,then it returns zero(0).
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def detected_face(image):
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eye_haar = current_path + '/haarcascade_eye.xml'
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face_haar = current_path + '/haarcascade_frontalface_default.xml'
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face_cascade = cv2.CascadeClassifier(face_haar)
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eye_cascade = cv2.CascadeClassifier(eye_haar)
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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faces = face_cascade.detectMultiScale(gray, 1.3, 5)
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face_areas=[]
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images = []
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required_image=0
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for i, (x,y,w,h) in enumerate(faces):
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face_cropped = gray[y:y+h, x:x+w]
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face_areas.append(w*h)
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images.append(face_cropped)
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required_image = images[np.argmax(face_areas)]
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required_image = Image.fromarray(required_image)
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return required_image
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#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.
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#2) The image is passed to the function in base64 encoding, Code for decoding the image is provided within the function.
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#3) Define an object to your network here in the function and load the weight from the trained network, set it in evaluation mode.
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#4) Perform necessary transformations to the input(detected face using the above function), this should return the Expression in string form ex: "Anger"
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#5) For loading your model use the current_path+'your model file name', anyhow detailed example is given in comments to the function
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##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
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def get_expression(img):
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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##########################################################################################
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##Example for loading a model using weight state dictionary: ##
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## face_det_net = facExpRec() #Example Network ##
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## model = torch.load(current_path + '/exp_recognition_net.t7', map_location=device) ##
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## face_det_net.load_state_dict(model['net_dict']) ##
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## ##
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##current_path + '/<network_definition>' is path of the saved model if present in ##
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##the same path as this file, we recommend to put in the same directory ##
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##########################################################################################
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##########################################################################################
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face = detected_face(img)
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if face==0:
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face = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY))
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# YOUR CODE HERE, return expression using your model
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return "YET TO BE CODED"
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app/Hackathon_setup/exp_recognition_model.py
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import torch
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import torchvision
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import torch.nn as nn
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from torchvision import transforms
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## Add more imports if required
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####################################################################################################################
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# Define your model and transform and all necessary helper functions here #
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# They will be imported to the exp_recognition.py file #
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####################################################################################################################
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# Definition of classes as dictionary
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classes = {0: 'ANGER', 1: 'DISGUST', 2: 'FEAR', 3: 'HAPPINESS', 4: 'NEUTRAL', 5: 'SADNESS', 6: 'SURPRISE'}
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# Example Network
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class facExpRec(torch.nn.Module):
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def __init__(self):
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pass # remove 'pass' once you have written your code
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#YOUR CODE HERE
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def forward(self, x):
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pass # remove 'pass' once you have written your code
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#YOUR CODE HERE
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# Sample Helper function
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def rgb2gray(image):
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return image.convert('L')
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# Sample Transformation function
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#YOUR CODE HERE for changing the Transformation values.
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trnscm = transforms.Compose([rgb2gray, transforms.Resize((48,48)), transforms.ToTensor()])
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app/Hackathon_setup/face_recognition.py
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import numpy as np
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import cv2
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from matplotlib import pyplot as plt
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import torch
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# 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.
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from .face_recognition_model import *
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from PIL import Image
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import base64
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import io
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import os
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import joblib
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import pickle
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# Add more imports if required
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###########################################################################################################################################
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# Caution: Don't change any of the filenames, function names and definitions #
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# Always use the current_path + file_name for refering any files, without it we cannot access files on the server #
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###########################################################################################################################################
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# Current_path stores absolute path of the file from where it runs.
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current_path = os.path.dirname(os.path.abspath(__file__))
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+
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+
#1) The below function is used to detect faces in the given image.
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#2) It returns only one image which has maximum area out of all the detected faces in the photo.
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#3) If no face is detected,then it returns zero(0).
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def detected_face(image):
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eye_haar = current_path + '/haarcascade_eye.xml'
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face_haar = current_path + '/haarcascade_frontalface_default.xml'
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face_cascade = cv2.CascadeClassifier(face_haar)
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eye_cascade = cv2.CascadeClassifier(eye_haar)
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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faces = face_cascade.detectMultiScale(gray, 1.3, 5)
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face_areas=[]
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images = []
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required_image=0
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for i, (x,y,w,h) in enumerate(faces):
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face_cropped = gray[y:y+h, x:x+w]
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face_areas.append(w*h)
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images.append(face_cropped)
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required_image = images[np.argmax(face_areas)]
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required_image = Image.fromarray(required_image)
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return required_image
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#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.
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#2) The image is passed to the function in base64 encoding, Code for decoding the image is provided within the function.
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#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.
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#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.
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#5) For loading your model use the current_path+'your model file name', anyhow detailed example is given in comments to the function
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#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
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def get_similarity(img1, img2):
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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det_img1 = detected_face(img1)
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det_img2 = detected_face(img2)
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if(det_img1 == 0 or det_img2 == 0):
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det_img1 = Image.fromarray(cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY))
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det_img2 = Image.fromarray(cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY))
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face1 = trnscm(det_img1).unsqueeze(0)
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face2 = trnscm(det_img2).unsqueeze(0)
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##########################################################################################
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##Example for loading a model using weight state dictionary: ##
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## feature_net = light_cnn() #Example Network ##
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## model = torch.load(current_path + '/siamese_model.t7', map_location=device) ##
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## feature_net.load_state_dict(model['net_dict']) ##
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## ##
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##current_path + '/<network_definition>' is path of the saved model if present in ##
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##the same path as this file, we recommend to put in the same directory ##
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##########################################################################################
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##########################################################################################
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feature_net = face_recognition_model.Siamese()
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model = torch.load('./siamese_model.t7', map_location=device)
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feature_net.load_state_dict(model['net_dict'])
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output1, output2 = feature_net(Variable(face1).to(device), Variable(face2).to(device))
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euclidean_distance = F.pairwise_distance(output1, output2)
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return euclidean_distance.item()
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# YOUR CODE HERE, load the model
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# YOUR CODE HERE, return similarity measure using your model
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#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"
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#2) The image is passed to the function in base64 encoding, Code to decode the image provided within the function
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93 |
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#3) Define an object to your network here in the function and load the weight from the trained network, set it in evaluation mode
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#4) Perform necessary transformations to the input(detected face using the above function).
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#5) Along with the siamese, you need the classifier as well, which is to be finetuned with the faces that you are training
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##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
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def get_face_class(img1):
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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det_img1 = detected_face(img1)
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if(det_img1 == 0):
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det_img1 = Image.fromarray(cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY))
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##YOUR CODE HERE, return face class here
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##Hint: you need a classifier finetuned for your classes, it takes o/p of siamese as i/p to it
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##Better Hint: Siamese experiment is covered in one of the labs
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return "YET TO BE CODED"
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app/Hackathon_setup/face_recognition_model.py
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|
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 @@
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1 |
+
<?xml version="1.0"?>
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2 |
+
<!--
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3 |
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number of positive samples 3000
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4 |
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number of negative samples 1500
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5 |
+
-->
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6 |
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7 |
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8 |
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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17 12 2 2</rect></_>
|
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+
<_>
|
1503 |
+
<rect>
|
1504 |
+
18 0 2 2</rect></_></features></cascade>
|
1505 |
+
</opencv_storage>
|
app/Hackathon_setup/siamese_model.t7
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:89ba48883946d0b23823b102e7f5faea3bd0a2d9e3e43e42b2c81cba73f46098
|
3 |
+
size 161026623
|
app/__init__.py
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
__version__ = "0.0.1"
|
app/config.py
ADDED
@@ -0,0 +1,25 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
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|
|
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|
|
|
|
|
1 |
+
import sys
|
2 |
+
from typing import List
|
3 |
+
|
4 |
+
from pydantic import AnyHttpUrl, BaseSettings
|
5 |
+
|
6 |
+
class Settings(BaseSettings):
|
7 |
+
API_V1_STR: str = "/api/v1"
|
8 |
+
|
9 |
+
# Meta
|
10 |
+
|
11 |
+
# BACKEND_CORS_ORIGINS is a comma-separated list of origins
|
12 |
+
# e.g: http://localhost,http://localhost:4200,http://localhost:3000
|
13 |
+
BACKEND_CORS_ORIGINS: List[AnyHttpUrl] = [
|
14 |
+
"http://localhost:3000", # type: ignore
|
15 |
+
"http://localhost:8000", # type: ignore
|
16 |
+
"https://localhost:3000", # type: ignore
|
17 |
+
"https://localhost:8000", # type: ignore
|
18 |
+
]
|
19 |
+
|
20 |
+
PROJECT_NAME: str = "Recognition API"
|
21 |
+
|
22 |
+
class Config:
|
23 |
+
case_sensitive = True
|
24 |
+
|
25 |
+
settings = Settings()
|
app/main.py
ADDED
@@ -0,0 +1,148 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
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|
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|
|
|
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|
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|
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|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import sys
|
2 |
+
from pathlib import Path
|
3 |
+
sys.path.append(str(Path(__file__).resolve().parent.parent))
|
4 |
+
#print(sys.path)
|
5 |
+
from typing import Any
|
6 |
+
|
7 |
+
from fastapi import FastAPI, Request, APIRouter, File, UploadFile
|
8 |
+
from fastapi.staticfiles import StaticFiles
|
9 |
+
from fastapi.templating import Jinja2Templates
|
10 |
+
from fastapi.middleware.cors import CORSMiddleware
|
11 |
+
from app.config import settings
|
12 |
+
from app import __version__
|
13 |
+
from app.Hackathon_setup import face_recognition, exp_recognition
|
14 |
+
|
15 |
+
import numpy as np
|
16 |
+
from PIL import Image
|
17 |
+
|
18 |
+
|
19 |
+
app = FastAPI(
|
20 |
+
title=settings.PROJECT_NAME, openapi_url=f"{settings.API_V1_STR}/openapi.json"
|
21 |
+
)
|
22 |
+
|
23 |
+
# To store files uploaded by users
|
24 |
+
app.mount("/static", StaticFiles(directory="app/static"), name="static")
|
25 |
+
|
26 |
+
# To access Templates directory
|
27 |
+
templates = Jinja2Templates(directory="app/templates")
|
28 |
+
|
29 |
+
simi_filename1 = None
|
30 |
+
simi_filename2 = None
|
31 |
+
face_rec_filename = None
|
32 |
+
expr_rec_filename = None
|
33 |
+
|
34 |
+
|
35 |
+
#################################### Home Page endpoints #################################################
|
36 |
+
@app.get("/")
|
37 |
+
async def root(request: Request):
|
38 |
+
return templates.TemplateResponse("index.html", {'request': request,})
|
39 |
+
|
40 |
+
|
41 |
+
#################################### Face Similarity endpoints #################################################
|
42 |
+
@app.get("/similarity/")
|
43 |
+
async def similarity_root(request: Request):
|
44 |
+
return templates.TemplateResponse("similarity.html", {'request': request,})
|
45 |
+
|
46 |
+
|
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 @@
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|
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|
|
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|
|
|
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 @@
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|
|
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 @@
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
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|
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|
|
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|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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
|