Face Recognition

[ Baseline ] Face Recognition

Starter Code for Face Recognition

In this baseline we will be using basic Mean Squared Error to compare the missing person image to all target faces ans generate our predictions.

Downloading Dataset¶

Installing puzzle datasets via aicrowd-cli

In [1]:

!pip install aicrowd-cli

# Make sure to re-run below code whenever you restart colab notebook
%load_ext aicrowd.magic

Collecting aicrowd-cli
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Installing collected packages: smmap, requests, gitdb, commonmark, colorama, rich, requests-toolbelt, pyzmq, GitPython, aicrowd-cli
  Attempting uninstall: requests
    Found existing installation: requests 2.23.0
    Uninstalling requests-2.23.0:
      Successfully uninstalled requests-2.23.0
  Attempting uninstall: pyzmq
    Found existing installation: pyzmq 22.3.0
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ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.27.1 which is incompatible.
datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.
Successfully installed GitPython-3.1.18 aicrowd-cli-0.1.12 colorama-0.4.4 commonmark-0.9.1 gitdb-4.0.9 pyzmq-22.1.0 requests-2.27.1 requests-toolbelt-0.9.1 rich-10.16.2 smmap-5.0.0

In [2]:

# Logging in from our AIcrowd account. Make sure you have accepted the puzzle rules before logging in!  

%aicrowd login

Please login here: https://api.aicrowd.com/auth/u6VjR_tLbwFfCfFxSIYu4231vKRqwrwOrSIoegjGtEk
API Key valid
Gitlab access token valid
Saved details successfully!

In [3]:

# Creating a new data directory and downloading the dataset 

!rm -rf data
!mkdir data
%aicrowd ds dl -c face-recognition -o data

In [4]:

# unzipping the data

!unzip data/data.zip -d /content/data > /dev/null

Importing Libraries¶

In [5]:

import pandas as pd
import os
import numpy as np
import random
from tqdm.notebook import tqdm
from google.colab.patches import cv2_imshow
import cv2

random.seed(42)

Reading Dataset¶

In [6]:

# Getting all image ids from a folder

image_ids = os.listdir("data/missing")
len(image_ids)

Out[6]:

In [7]:

# Reading a sample missing person image


sample_image_id = random.choice(image_ids)
sample_missing = cv2.imread(os.path.join("data/missing", sample_image_id))
cv2_imshow(sample_missing)

In [11]:

# Reading the corrosponding target faces

sample_target = cv2.imread(os.path.join("data/target", sample_image_id))
cv2_imshow(cv2.resize(sample_target, (512, 512)))