Spaces:
Sleeping
Sleeping
import streamlit as st | |
from PIL import Image | |
import face_recognition | |
import cv2 | |
import numpy as np | |
import requests | |
import os | |
st.title("AIMLJan24 - Face Recognition") | |
# create list of encoding of all images in photos folder | |
# Load images for face recognition | |
Images = [] # List to store Images | |
classnames = [] # List to store classnames | |
directory = "photos" | |
myList = os.listdir(directory) | |
st.write("Photographs found in folder : ") | |
for cls in myList: | |
if os.path.splitext(cls)[1] in [".jpg", ".jpeg"]: | |
img_path = os.path.join(directory, cls) | |
curImg = cv2.imread(img_path) | |
Images.append(curImg) | |
st.write(os.path.splitext(cls)[0]) | |
classnames.append(os.path.splitext(cls)[0]) | |
# Load images for face recognition | |
encodeListknown = [face_recognition.face_encodings(img)[0] for img in Images] | |
# camera to take photo of user in question | |
file_name = st.camera_input("Take a picture") #st.file_uploader("Upload image ") | |
# Function to update Aadhaar data | |
def update_data(name): | |
# url = "https://attendanceviaface.000webhostapp.com" | |
# url1 = "/update.php" | |
# data = {'name': name, 'aadhaar': '998877'} | |
# response = requests.post(url + url1, data=data) | |
url = "https://aimljan24f1.glitch.me/adduserdata" #?rollno=222&name="+name | |
data = {'rollno':'222','name': name} | |
response = requests.post(url , data=data ) | |
if response.status_code == 200: | |
st.success("Data updated on: " + url) | |
else: | |
st.warning("Data not updated") | |
if file_name is not None: | |
col1, col2 = st.columns(2) | |
test_image = Image.open(file_name) | |
image = np.asarray(test_image) | |
imgS = cv2.resize(image, (0, 0), None, 0.25, 0.25) | |
imgS = cv2.cvtColor(imgS, cv2.COLOR_BGR2RGB) | |
facesCurFrame = face_recognition.face_locations(imgS) | |
encodesCurFrame = face_recognition.face_encodings(imgS, facesCurFrame) | |
name = "Unknown" # Default name for unknown faces | |
match_found = False # Flag to track if a match is found | |
# Checking if faces are detected | |
if len(encodesCurFrame) > 0: | |
for encodeFace, faceLoc in zip(encodesCurFrame, facesCurFrame): | |
# Assuming that encodeListknown is defined and populated in your code | |
matches = face_recognition.compare_faces(encodeListknown, encodeFace) | |
faceDis = face_recognition.face_distance(encodeListknown, encodeFace) | |
matchIndex = np.argmin(faceDis) | |
if matches[matchIndex]: | |
name = classnames[matchIndex].upper() | |
match_found = True # Set the flag to True | |
y1, x2, y2, x1 = faceLoc | |
y1, x2, y2, x1 = (y1 * 4), (x2 * 4), (y2 * 4) ,(x1 * 4) | |
# Make a copy of the image array before drawing on it | |
image_copy = image.copy() | |
cv2.rectangle(image_copy, (x1, y1), (x2, y2), (0, 255, 0), 2) | |
cv2.rectangle(image_copy, (x1, y2 - 35), (x2, y2), (0, 255, 0), cv2.FILLED) | |
cv2.putText(image_copy, name, (x1 + 6, y2 - 6), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2) | |
# update the database | |
update_data(name) | |
st.image(image_copy, use_column_width=True, output_format="PNG") | |
else: | |
st.warning("No faces detected in the image. Face recognition failed.") | |
# image = Image.open(file_name) | |
# col1.image(image, use_column_width=True) | |
# pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog") | |
# st.title("AIMLJan24 First App on Hugging face - Hot Dog? Or Not?") | |
# file_name = st.file_uploader("Upload the test image to find is this hot dog ! ") | |
# if file_name is not None: | |
# col1, col2 = st.columns(2) | |
# image = Image.open(file_name) | |
# col1.image(image, use_column_width=True) | |
# predictions = pipeline(image) | |
# col2.header("Probabilities") | |
# for p in predictions: | |
# col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%") | |
# # my first app | |
# import streamlit as st | |
# x = st.slider('Select a value') | |
# st.write(x, 'squared is', x * x) |