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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 ")
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)
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) |