adding model and updated files for stage 1

.gitattributes

@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 /recognition_api/app/Hackathon_setup/siamese_model.t7 filter=lfs diff=lfs merge=lfs -text
+app/Hackathon_setup/siamese_model.t7 filter=lfs diff=lfs merge=lfs -text

Dockerfile

@@ -0,0 +1,29 @@
+FROM python:3.11
+
+COPY requirements.txt requirements.txt
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+      bzip2 \
+      g++ \
+      git \
+      graphviz \
+      libgl1-mesa-glx \
+      libhdf5-dev \
+      openmpi-bin \
+      wget \
+      python3-tk && \
+    rm -rf /var/lib/apt/lists/*
+
+RUN pip install --upgrade pip
+
+RUN pip install --no-cache-dir -r requirements.txt
+
+RUN useradd -m -u 1000 myuser
+
+USER myuser
+
+COPY --chown=myuser app app
+
+EXPOSE 8001
+
+CMD ["python", "app/main.py"]

app/Hackathon_setup/exp_recognition.py

@@ -0,0 +1,71 @@
+import numpy as np
+import cv2
+from matplotlib import pyplot as plt
+import torch
+# 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.
+from .exp_recognition_model import *
+from PIL import Image
+import base64
+import io
+import os
+## Add more imports if required
+
+#############################################################################################################################
+#   Caution: Don't change any of the filenames, function names and definitions                                              #
+#   Always use the current_path + file_name for refering any files, without it we cannot access files on the server         #
+#############################################################################################################################
+
+# Current_path stores absolute path of the file from where it runs.
+current_path = os.path.dirname(os.path.abspath(__file__))
+
+
+#1) The below function is used to detect faces in the given image.
+#2) It returns only one image which has maximum area out of all the detected faces in the photo.
+#3) If no face is detected,then it returns zero(0).
+
+def detected_face(image):
+    eye_haar = current_path + '/haarcascade_eye.xml'
+    face_haar = current_path + '/haarcascade_frontalface_default.xml'
+    face_cascade = cv2.CascadeClassifier(face_haar)
+    eye_cascade = cv2.CascadeClassifier(eye_haar)
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
+    face_areas=[]
+    images = []
+    required_image=0
+    for i, (x,y,w,h) in enumerate(faces):
+        face_cropped = gray[y:y+h, x:x+w]
+        face_areas.append(w*h)
+        images.append(face_cropped)
+        required_image = images[np.argmax(face_areas)]
+        required_image = Image.fromarray(required_image)
+    return required_image
+    
+
+#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.
+#2) The image is passed to the function in base64 encoding, Code for decoding the image is provided within the function.
+#3) Define an object to your network here in the function and load the weight from the trained network, set it in evaluation mode.
+#4) Perform necessary transformations to the input(detected face using the above function), this should return the Expression in string form ex: "Anger"
+#5) For loading your model use the current_path+'your model file name', anyhow detailed example is given in comments to the function 
+##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
+def get_expression(img):
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    
+    ##########################################################################################
+    ##Example for loading a model using weight state dictionary:                            ##
+    ## face_det_net = facExpRec() #Example Network                                          ##
+    ## model = torch.load(current_path + '/exp_recognition_net.t7', map_location=device)    ##
+    ## face_det_net.load_state_dict(model['net_dict'])                                      ##
+    ##                                                                                      ##
+    ##current_path + '/<network_definition>' is path of the saved model if present in       ##
+    ##the same path as this file, we recommend to put in the same directory                 ##
+    ##########################################################################################
+    ##########################################################################################
+
+    face = detected_face(img)
+    if face==0:
+        face = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY))
+    
+    # YOUR CODE HERE, return expression using your model
+
+    return "YET TO BE CODED"

app/Hackathon_setup/exp_recognition_model.py

@@ -0,0 +1,31 @@
+import torch
+import torchvision
+import torch.nn as nn
+from torchvision import transforms
+## Add more imports if required
+
+####################################################################################################################
+# Define your model and transform and all necessary helper functions here                                          #
+# They will be imported to the exp_recognition.py file                                                             #
+####################################################################################################################
+
+# Definition of classes as dictionary
+classes = {0: 'ANGER', 1: 'DISGUST', 2: 'FEAR', 3: 'HAPPINESS', 4: 'NEUTRAL', 5: 'SADNESS', 6: 'SURPRISE'}
+
+# Example Network
+class facExpRec(torch.nn.Module):
+    def __init__(self):
+        pass   # remove 'pass' once you have written your code
+        #YOUR CODE HERE
+        
+    def forward(self, x):
+        pass   # remove 'pass' once you have written your code
+        #YOUR CODE HERE
+        
+# Sample Helper function
+def rgb2gray(image):
+    return image.convert('L')
+    
+# Sample Transformation function
+#YOUR CODE HERE for changing the Transformation values.
+trnscm = transforms.Compose([rgb2gray, transforms.Resize((48,48)), transforms.ToTensor()])

app/Hackathon_setup/face_recognition.py

@@ -0,0 +1,107 @@
+import numpy as np
+import cv2
+from matplotlib import pyplot as plt
+import torch
+from torch.autograd import Variable
+# 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.
+from .face_recognition_model import *
+from PIL import Image
+import base64
+import io
+import os
+import joblib
+import pickle
+# Add more imports if required
+
+
+
+###########################################################################################################################################
+#         Caution: Don't change any of the filenames, function names and definitions                                                      #
+#        Always use the current_path + file_name for refering any files, without it we cannot access files on the server                  # 
+###########################################################################################################################################
+
+# Current_path stores absolute path of the file from where it runs. 
+current_path = os.path.dirname(os.path.abspath(__file__))
+
+#1) The below function is used to detect faces in the given image.
+#2) It returns only one image which has maximum area out of all the detected faces in the photo.
+#3) If no face is detected,then it returns zero(0).
+
+def detected_face(image):
+    eye_haar = current_path + '/haarcascade_eye.xml'
+    face_haar = current_path + '/haarcascade_frontalface_default.xml'
+    face_cascade = cv2.CascadeClassifier(face_haar)
+    eye_cascade = cv2.CascadeClassifier(eye_haar)
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
+    face_areas=[]
+    images = []
+    required_image=0
+    for i, (x,y,w,h) in enumerate(faces):
+        face_cropped = gray[y:y+h, x:x+w]
+        face_areas.append(w*h)
+        images.append(face_cropped)
+        required_image = images[np.argmax(face_areas)]
+        required_image = Image.fromarray(required_image)
+    return required_image
+
+
+#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.
+#2) The image is passed to the function in base64 encoding, Code for decoding the image is provided within the function.
+#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.
+#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.
+#5) For loading your model use the current_path+'your model file name', anyhow detailed example is given in comments to the function 
+#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
+def get_similarity(img1, img2):
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    
+    det_img1 = detected_face(img1)
+    det_img2 = detected_face(img2)
+    if(det_img1 == 0 or det_img2 == 0):
+        det_img1 = Image.fromarray(cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY))
+        det_img2 = Image.fromarray(cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY))
+    face1 = trnscm(det_img1).unsqueeze(0)
+    face2 = trnscm(det_img2).unsqueeze(0)
+    ##########################################################################################
+    ##Example for loading a model using weight state dictionary:                            ##
+    ## feature_net = light_cnn() #Example Network                                           ##
+    ## model = torch.load(current_path + '/siamese_model.t7', map_location=device)          ##
+    ## feature_net.load_state_dict(model['net_dict'])                                       ##
+    ##                                                                                      ##
+    ##current_path + '/<network_definition>' is path of the saved model if present in       ##
+    ##the same path as this file, we recommend to put in the same directory                 ##
+    ##########################################################################################
+    ##########################################################################################
+    
+    # YOUR CODE HERE, load the model
+    feature_net = Siamese()                                   
+    model = torch.load( current_path+'/siamese_model.t7', map_location=device)          
+    feature_net.load_state_dict(model['net_dict']) 
+    
+    # YOUR CODE HERE, return similarity measure using your model
+
+    output1, output2 = feature_net(Variable(face1).to(device), Variable(face2).to(device))
+    euclidean_distance = F.pairwise_distance(output1, output2)
+    
+    # print(f'Similarity: {euclidean_distance.item():.2f}')
+    return euclidean_distance.item()
+    
+#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"
+#2) The image is passed to the function in base64 encoding, Code to decode the image provided within the function
+#3) Define an object to your network here in the function and load the weight from the trained network, set it in evaluation mode
+#4) Perform necessary transformations to the input(detected face using the above function).
+#5) Along with the siamese, you need the classifier as well, which is to be finetuned with the faces that you are training
+##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
+def get_face_class(img1):
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    
+    det_img1 = detected_face(img1)
+    if(det_img1 == 0):
+        det_img1 = Image.fromarray(cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY))
+
+    
+    ##YOUR CODE HERE, return face class here
+    ##Hint: you need a classifier finetuned for your classes, it takes o/p of siamese as i/p to it
+    ##Better Hint: Siamese experiment is covered in one of the labs
+
+    return "YET TO BE CODED"

app/Hackathon_setup/face_recognition_model.py

@@ -0,0 +1,64 @@
+import math
+import torch
+import torchvision
+import torch.nn as nn
+import torch.nn.functional as F
+from torchvision import transforms
+# Add more imports if required
+
+# Sample Transformation function
+# YOUR CODE HERE for changing the Transformation values.
+trnscm = transforms.Compose([transforms.Resize((100,100)), transforms.ToTensor()])
+
+##Example Network
+class SiameseNetwork(nn.Module):
+    def __init__(self):
+        super(SiameseNetwork, self).__init__()
+        self.cnn1 = nn.Sequential(
+            nn.ReflectionPad2d(1),       #Pads the input tensor using the reflection of the input boundary, it similar to the padding.
+            nn.Conv2d(1, 4, kernel_size=3),
+            nn.ReLU(inplace=True),
+            nn.BatchNorm2d(4),
+
+            nn.ReflectionPad2d(1),
+            nn.Conv2d(4, 8, kernel_size=3),
+            nn.ReLU(inplace=True),
+            nn.BatchNorm2d(8),
+
+
+            nn.ReflectionPad2d(1),
+            nn.Conv2d(8, 8, kernel_size=3),
+            nn.ReLU(inplace=True),
+            nn.BatchNorm2d(8),
+        )
+
+        self.fc1 = nn.Sequential(
+            nn.Linear(8*100*100, 500),
+            nn.ReLU(inplace=True),
+
+            nn.Linear(500, 500),
+            nn.ReLU(inplace=True),
+
+            nn.Linear(500, 5))
+
+    # forward_once is for one image. This can be used while classifying the face images
+    def forward_once(self, x):
+        output = self.cnn1(x)
+        output = output.view(output.size()[0], -1)
+        output = self.fc1(output)
+        return output
+
+    def forward(self, input1, input2):
+        output1 = self.forward_once(input1)
+        output2 = self.forward_once(input2)
+        return output1, output2
+
+##########################################################################################################
+## Sample classification network (Specify if you are using a pytorch classifier during the training)    ##
+## classifier = nn.Sequential(nn.Linear(64, 64), nn.BatchNorm1d(64), nn.ReLU(), nn.Linear...)           ##
+##########################################################################################################
+
+# YOUR CODE HERE for pytorch classifier
+
+# Definition of classes as dictionary
+classes = ['person1','person2','person3','person4','person5','person6','person7']

app/Hackathon_setup/lbpcascade_frontalface.xml

@@ -0,0 +1,1505 @@
+<?xml version="1.0"?>
+<!--
+number of positive samples 3000
+number of negative samples 1500
+-->
+<opencv_storage>
+<cascade type_id="opencv-cascade-classifier">
+  <stageType>BOOST</stageType>
+  <featureType>LBP</featureType>
+  <height>24</height>
+  <width>24</width>
+  <stageParams>
+    <boostType>GAB</boostType>
+    <minHitRate>0.9950000047683716</minHitRate>
+    <maxFalseAlarm>0.5000000000000000</maxFalseAlarm>
+    <weightTrimRate>0.9500000000000000</weightTrimRate>
+    <maxDepth>1</maxDepth>
+    <maxWeakCount>100</maxWeakCount></stageParams>
+  <featureParams>
+    <maxCatCount>256</maxCatCount></featureParams>
+  <stageNum>20</stageNum>
+  <stages>
+    <!-- stage 0 -->
+    <_>
+      <maxWeakCount>3</maxWeakCount>
+      <stageThreshold>-0.7520892024040222</stageThreshold>
+      <weakClassifiers>
+        <!-- tree 0 -->
+        <_>
+          <internalNodes>
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+          <leafValues>
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+        <!-- tree 1 -->
+        <_>
+          <internalNodes>
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+          <leafValues>
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+        <!-- tree 2 -->
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+          <internalNodes>
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+          <leafValues>
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+    <!-- stage 1 -->
+    <_>
+      <maxWeakCount>4</maxWeakCount>
+      <stageThreshold>-0.4872078299522400</stageThreshold>
+      <weakClassifiers>
+        <!-- tree 0 -->
+        <_>
+          <internalNodes>
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+        <!-- tree 1 -->
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+        <!-- tree 2 -->
+        <_>
+          <internalNodes>
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+        <!-- tree 3 -->
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+    <!-- stage 2 -->
+    <_>
+      <maxWeakCount>4</maxWeakCount>
+      <stageThreshold>-1.1592328548431396</stageThreshold>
+      <weakClassifiers>
+        <!-- tree 0 -->
+        <_>
+          <internalNodes>
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+        <!-- tree 1 -->
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+        <!-- tree 2 -->
+        <_>
+          <internalNodes>
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+          <leafValues>
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+        <!-- tree 3 -->
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+          <leafValues>
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+    <!-- stage 3 -->
+    <_>
+      <maxWeakCount>5</maxWeakCount>
+      <stageThreshold>-0.7562355995178223</stageThreshold>
+      <weakClassifiers>
+        <!-- tree 0 -->
+        <_>
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+        <!-- tree 1 -->
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+        <!-- tree 2 -->
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+          <leafValues>
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+        <!-- tree 3 -->
+        <_>
+          <internalNodes>
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+          <leafValues>
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+        <!-- tree 4 -->
+        <_>
+          <internalNodes>
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+          <leafValues>
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+    <!-- stage 4 -->
+    <_>
+      <maxWeakCount>5</maxWeakCount>
+      <stageThreshold>-0.8085358142852783</stageThreshold>
+      <weakClassifiers>
+        <!-- tree 0 -->
+        <_>
+          <internalNodes>
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+        <!-- tree 1 -->
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+        <!-- tree 2 -->
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+          <internalNodes>
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+          <leafValues>
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+        <!-- tree 3 -->
+        <_>
+          <internalNodes>
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+        <!-- tree 4 -->
+        <_>
+          <internalNodes>
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+    <!-- stage 5 -->
+    <_>
+      <maxWeakCount>5</maxWeakCount>
+      <stageThreshold>-0.5549971461296082</stageThreshold>
+      <weakClassifiers>
+        <!-- tree 0 -->
+        <_>
+          <internalNodes>
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+        <!-- tree 1 -->
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+        <!-- tree 2 -->
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+        <!-- tree 3 -->
+        <_>
+          <internalNodes>
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+        <!-- tree 4 -->
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+          <leafValues>
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+    <!-- stage 6 -->
+    <_>
+      <maxWeakCount>5</maxWeakCount>
+      <stageThreshold>-0.8776460289955139</stageThreshold>
+      <weakClassifiers>
+        <!-- tree 0 -->
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+    <!-- stage 8 -->
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+    <!-- stage 9 -->
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+    <!-- stage 10 -->
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+    <!-- stage 11 -->
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+        15 4 3 6</rect></_>
+    <_>
+      <rect>
+        15 6 3 2</rect></_>
+    <_>
+      <rect>
+        15 11 3 4</rect></_>
+    <_>
+      <rect>
+        15 13 3 2</rect></_>
+    <_>
+      <rect>
+        15 17 2 2</rect></_>
+    <_>
+      <rect>
+        15 17 3 2</rect></_>
+    <_>
+      <rect>
+        16 1 2 3</rect></_>
+    <_>
+      <rect>
+        16 3 2 4</rect></_>
+    <_>
+      <rect>
+        16 6 1 1</rect></_>
+    <_>
+      <rect>
+        16 16 2 2</rect></_>
+    <_>
+      <rect>
+        17 1 2 2</rect></_>
+    <_>
+      <rect>
+        17 1 2 5</rect></_>
+    <_>
+      <rect>
+        17 12 2 2</rect></_>
+    <_>
+      <rect>
+        18 0 2 2</rect></_></features></cascade>
+</opencv_storage>

app/Hackathon_setup/siamese_model.t7

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:99c137f47231a60eb9e4022a80f0884cbd0f89a14fc6280fceaa54a577d5f6d4
+size 161026623

app/__init__.py

@@ -0,0 +1 @@
+__version__ = "0.0.1"

app/config.py

@@ -0,0 +1,25 @@
+import sys
+from typing import List
+
+from pydantic import AnyHttpUrl, BaseSettings
+
+class Settings(BaseSettings):
+    API_V1_STR: str = "/api/v1"
+
+    # Meta
+ 
+    # BACKEND_CORS_ORIGINS is a comma-separated list of origins
+    # e.g: http://localhost,http://localhost:4200,http://localhost:3000
+    BACKEND_CORS_ORIGINS: List[AnyHttpUrl] = [
+        "http://localhost:3000",  # type: ignore
+        "http://localhost:8000",  # type: ignore
+        "https://localhost:3000",  # type: ignore
+        "https://localhost:8000",  # type: ignore
+    ]
+
+    PROJECT_NAME: str = "Recognition API"
+
+    class Config:
+        case_sensitive = True
+
+settings = Settings()

app/main.py

@@ -0,0 +1,148 @@
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).resolve().parent.parent))
+#print(sys.path)
+from typing import Any
+
+from fastapi import FastAPI, Request, APIRouter, File, UploadFile
+from fastapi.staticfiles import StaticFiles
+from fastapi.templating import Jinja2Templates
+from fastapi.middleware.cors import CORSMiddleware
+from app.config import settings
+from app import __version__
+from app.Hackathon_setup import face_recognition, exp_recognition
+
+import numpy as np
+from PIL import Image
+
+
+app = FastAPI(
+    title=settings.PROJECT_NAME, openapi_url=f"{settings.API_V1_STR}/openapi.json"
+)
+
+# To store files uploaded by users
+app.mount("/static", StaticFiles(directory="app/static"), name="static")
+
+# To access Templates directory
+templates = Jinja2Templates(directory="app/templates")
+
+simi_filename1 = None
+simi_filename2 = None
+face_rec_filename = None
+expr_rec_filename = None
+
+
+####################################  Home Page endpoints  #################################################
+@app.get("/")
+async def root(request: Request):
+    return templates.TemplateResponse("index.html", {'request': request,})
+
+
+####################################  Face Similarity endpoints  #################################################
+@app.get("/similarity/")
+async def similarity_root(request: Request):
+    return templates.TemplateResponse("similarity.html", {'request': request,})
+
+
+@app.post("/predict_similarity/")
+async def create_upload_files(request: Request, file1: UploadFile = File(...), file2: UploadFile = File(...)):
+    global simi_filename1
+    global simi_filename2
+    
+    if 'image' in file1.content_type:
+        contents = await file1.read()
+        simi_filename1 = 'app/static/' + file1.filename
+        with open(simi_filename1, 'wb') as f:
+            f.write(contents)
+    
+    if 'image' in file2.content_type:
+        contents = await file2.read()
+        simi_filename2 = 'app/static/' + file2.filename
+        with open(simi_filename2, 'wb') as f:
+            f.write(contents)
+    
+    img1 = Image.open(simi_filename1)
+    img1 =  np.array(img1).reshape(img1.size[1], img1.size[0], 3).astype(np.uint8)
+    
+    img2 = Image.open(simi_filename2)
+    img2 =  np.array(img2).reshape(img2.size[1], img2.size[0], 3).astype(np.uint8)
+    
+    result = face_recognition.get_similarity(img1, img2)
+    #print(result)
+    
+    return templates.TemplateResponse("predict_similarity.html", {"request": request,
+                                                       "result": np.round(result, 3),
+                                                       "simi_filename1": '../static/'+file1.filename,
+                                                       "simi_filename2": '../static/'+file2.filename,})
+    
+
+####################################  Face Recognition endpoints  #################################################
+@app.get("/face_recognition/")
+async def face_recognition_root(request: Request):
+    return templates.TemplateResponse("face_recognition.html", {'request': request,})
+
+
+@app.post("/predict_face_recognition/")
+async def create_upload_files(request: Request, file3: UploadFile = File(...)):
+    global face_rec_filename
+    
+    if 'image' in file3.content_type:
+        contents = await file3.read()
+        face_rec_filename = 'app/static/' + file3.filename
+        with open(face_rec_filename, 'wb') as f:
+            f.write(contents)
+    
+    img1 = Image.open(face_rec_filename)
+    img1 =  np.array(img1).reshape(img1.size[1], img1.size[0], 3).astype(np.uint8)
+        
+    result = face_recognition.get_face_class(img1)
+    print(result)
+    
+    return templates.TemplateResponse("predict_face_recognition.html", {"request": request,
+                                                       "result": result,
+                                                       "face_rec_filename": '../static/'+file3.filename,})
+
+
+####################################  Expresion Recognition endpoints  #################################################
+@app.get("/expr_recognition/")
+async def expr_recognition_root(request: Request):
+    return templates.TemplateResponse("expr_recognition.html", {'request': request,})
+
+
+@app.post("/predict_expr_recognition/")
+async def create_upload_files(request: Request, file4: UploadFile = File(...)):
+    global expr_rec_filename
+    
+    if 'image' in file4.content_type:
+        contents = await file4.read()
+        expr_rec_filename = 'app/static/' + file4.filename
+        with open(expr_rec_filename, 'wb') as f:
+            f.write(contents)
+    
+    img1 = Image.open(expr_rec_filename)
+    img1 =  np.array(img1).reshape(img1.size[1], img1.size[0], 3).astype(np.uint8)
+        
+    result = exp_recognition.get_expression(img1)
+    print(result)
+    
+    return templates.TemplateResponse("predict_expr_recognition.html", {"request": request,
+                                                       "result": result,
+                                                       "expr_rec_filename": '../static/'+file4.filename,})
+
+
+
+# Set all CORS enabled origins
+if settings.BACKEND_CORS_ORIGINS:
+    app.add_middleware(
+        CORSMiddleware,
+        allow_origins=[str(origin) for origin in settings.BACKEND_CORS_ORIGINS],
+        allow_credentials=True,
+        allow_methods=["*"],
+        allow_headers=["*"],
+    )
+
+
+# Start app
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8001)

app/templates/expr_recognition.html

@@ -0,0 +1,32 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <title>Index</title>
+</head>
+<body>
+    <div>
+        <h1 style="background-color:LightGray;">
+            <center>Expression Recognition</center>
+        </h1>
+    </div>
+    <div>
+        <fieldset>
+        <ul>
+            <!li>
+            <br>
+            <form action="/predict_expr_recognition/" enctype="multipart/form-data" method="post">
+                <span style="font-weight:bold;font-family:sans-serif">Upload Image:</span> <br><br>
+                <input name="file4" type="file" onchange="readURL(this);" />
+                <br><br><br>
+                <button type="submit">Recognize Expression</button>
+            </form>
+            <!/li>
+            <br><br>
+            <form action="/" method="get">
+                <button type="submit">Home</button>
+            </form>
+        </ul>
+        </fieldset>
+    </div>
+</body>
+</html>

app/templates/face_recognition.html

@@ -0,0 +1,32 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <title>Index</title>
+</head>
+<body>
+    <div>
+        <h1 style="background-color:LightGray;">
+            <center>Face Recognition</center>
+        </h1>
+    </div>
+    <div>
+        <fieldset>
+        <ul>
+            <!li>
+            <br>
+            <form action="/predict_face_recognition/" enctype="multipart/form-data" method="post">
+                <span style="font-weight:bold;font-family:sans-serif">Upload Image:</span> <br><br>
+                <input name="file3" type="file" onchange="readURL(this);" />
+                <br><br><br>
+                <button type="submit">Recognize Face</button>
+            </form>
+            <!/li>
+            <br><br>
+            <form action="/" method="get">
+                <button type="submit">Home</button>
+            </form>
+        </ul>
+        </fieldset>
+    </div>
+</body>
+</html>

app/templates/index.html

@@ -0,0 +1,29 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <title>Index</title>
+</head>
+<body>
+    <div>
+        <h1 style="background-color:LightGray;">
+            <center>Recognition Application</center>
+        </h1>
+    </div>
+    <div>
+        <fieldset>
+        <ul>
+            <li><span style="font-weight:bold;font-family:sans-serif">Select a task:</span> 
+            <br><br><br>
+            <form action="{{ url_for('similarity_root') }}"><button>Face Similarity</button></form>
+            <br><br>
+            <form action="{{ url_for('face_recognition_root') }}"><button>Face Recognition</button></form>
+            <br><br>
+            <form action="{{ url_for('expr_recognition_root') }}"><button>Expression Recognition</button></form>
+            <br>
+            </li>
+            <br>
+        </ul>
+        </fieldset>
+    </div>
+</body>
+</html>

app/templates/predict_expr_recognition.html

@@ -0,0 +1,37 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <title>Predict</title>
+</head>
+<body>
+    <div>
+        <h1 style="background-color:LightGray;">
+            <center>Expression Recognition</center>
+        </h1>
+    </div>
+    <div>
+        <fieldset>
+            <h2>
+                <center>
+                    <span style="font-weight:bold;font-family:sans-serif">Prediction: </span> 
+                    <span style="font-weight:bold;color:blue"> {{result}}</span>
+                </center>
+            </h2>
+            <h3><center><span style="font-weight:bold;font-family:sans-serif">Input image:</span></Input></center></h3> 
+            <p>
+              <center>
+              <img src="{{expr_rec_filename}}" alt={{expr_rec_filename1}} width='150' height='150'>
+              </center>
+            </p>
+            <br>
+            <form action="/expr_recognition/" method="get">
+                <center><button type="submit">Check Another Input</button></center>
+            </form>
+            <br>
+            <form action="/" method="get">
+                <center><button type="submit">Home</button></center>
+            </form>
+        </fieldset>
+    </div>
+</body>
+</html>

app/templates/predict_face_recognition.html

@@ -0,0 +1,37 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <title>Predict</title>
+</head>
+<body>
+    <div>
+        <h1 style="background-color:LightGray;">
+            <center>Face Recognition</center>
+        </h1>
+    </div>
+    <div>
+        <fieldset>
+            <h2>
+                <center>
+                    <span style="font-weight:bold;font-family:sans-serif">Prediction: </span> 
+                    <span style="font-weight:bold;color:blue"> {{result}}</span>
+                </center>
+            </h2>
+            <h3><center><span style="font-weight:bold;font-family:sans-serif">Input image:</span></Input></center></h3> 
+            <p>
+              <center>
+              <img src="{{face_rec_filename}}" alt={{face_rec_filename1}} width='150' height='150'>
+              </center>
+            </p>
+            <br>
+            <form action="/face_recognition/" method="get">
+                <center><button type="submit">Check Another Input</button></center>
+            </form>
+            <br>
+            <form action="/" method="get">
+                <center><button type="submit">Home</button></center>
+            </form>
+        </fieldset>
+    </div>
+</body>
+</html>

app/templates/predict_similarity.html

@@ -0,0 +1,38 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <title>Predict</title>
+</head>
+<body>
+    <div>
+        <h1 style="background-color:LightGray;">
+            <center>Face Similarity</center>
+        </h1>
+    </div>
+    <div>
+        <fieldset>
+            <h2>
+                <center>
+                    <span style="font-weight:bold;font-family:sans-serif">Dissimilarity:</span> 
+                    <span style="font-weight:bold;color:blue"> {{result}}</span>
+                </center>
+            </h2>
+            <h3><center><span style="font-weight:bold;font-family:sans-serif">Input images:</span></Input></center></h3> 
+            <p>
+              <center>
+              <img src="{{simi_filename1}}" alt={{simi_filename1}} width='150' height='150'>
+              <img src="{{simi_filename2}}" alt={{simi_filename2}} width='150' height='150'>
+              </center>
+            </p>
+            <br>
+            <form action="/similarity/" method="get">
+                <center><button type="submit">Check Another Input</button></center>
+            </form>
+            <br>
+            <form action="/" method="get">
+                <center><button type="submit">Home</button></center>
+            </form>
+        </fieldset>
+    </div>
+</body>
+</html>

app/templates/similarity.html

@@ -0,0 +1,35 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <title>Index</title>
+</head>
+<body>
+    <div>
+        <h1 style="background-color:LightGray;">
+            <center>Face Similarity</center>
+        </h1>
+    </div>
+    <div>
+        <fieldset>
+        <ul>
+            <!li>
+            <br>
+            <form action="/predict_similarity/" enctype="multipart/form-data" method="post">
+                <span style="font-weight:bold;font-family:sans-serif">Upload First Image:</span> <br><br>
+                <input name="file1" type="file" onchange="readURL(this);" />
+                <br><br><br>
+                <span style="font-weight:bold;font-family:sans-serif">Upload Second Image:</span> <br><br>
+                <input name="file2" type="file" onchange="readURL(this);" />
+                <br><br><br><br>
+                <button type="submit">Check Similarity</button>
+            </form>
+            <!/li>
+            <br><br>
+            <form action="/" method="get">
+                <button type="submit">Home</button>
+            </form>
+        </ul>
+        </fieldset>
+    </div>
+</body>
+</html>

requirements.txt

@@ -0,0 +1,17 @@
+uvicorn==0.17.6
+fastapi==0.99.1
+pydantic==1.10.10
+requests==2.23.0
+jinja2==3.1.2
+python-multipart==0.0.6
+
+scikit-learn==1.2.2
+joblib==1.3.2
+Pillow==9.4.0
+torch==2.1.0
+torchvision==0.16.0
+matplotlib==3.7.1
+numpy
+pandas
+#opencv-python==4.8.0.76
+opencv-python==4.5.5.64