Upload 6 files

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+app/Hackathon_setup/expression_model.t7 filter=lfs diff=lfs merge=lfs -text
+app/Hackathon_setup/siamese_model.t7 filter=lfs diff=lfs merge=lfs -text

app/Hackathon_setup/exp_recognition.py

@@ -0,0 +1,69 @@
+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) Define an object to your network here in the function and load the weight from the trained network, set it in evaluation mode.
+#2) Perform necessary transformations to the input(detected face using the above function), this should return the Expression in string form ex: "Anger"
+#3) 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/expression_model.t7

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:12c2daf67685fd96a1b39cd1518a1a18f307b269fc2401d3b59e07e17f5c8f41
+size 45333465

app/Hackathon_setup/face_recognition.py

@@ -0,0 +1,92 @@
+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 .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) Define an object to your siamese network here in the function and load the weight from the trained network, set it in evaluation mode.
+#2) 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.
+#3) 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
+    
+    # YOUR CODE HERE, return similarity measure using your model
+    
+    return 0
+    
+#1) Define an object to your network here in the function and load the weight from the trained network, set it in evaluation mode
+#2) Perform necessary transformations to the input(detected face using the above function).
+#3) 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 +1,31 @@
+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 Siamese(torch.nn.Module):
+    def __init__(self):
+        super(Siamese, self).__init__()
+        #YOUR CODE HERE
+        
+    def forward(self, x):
+        pass   # remove 'pass' once you have written your code
+        #YOUR CODE HERE
+        
+##########################################################################################################
+## 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/siamese_model.t7

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d351b6ab270b5289cccf0218825514066ac1ef7f2811de7fb58acfaddf83368f
+size 161027032