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app.py
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app.py
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# Core Pkgs
import streamlit as st
import cv2
from PIL import Image,ImageEnhance
import numpy as np
import os
@st.cache
def load_image(img):
im = Image.open(img)
return im
face_cascade = cv2.CascadeClassifier('frecog/haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier('frecog/haarcascade_eye.xml')
smile_cascade = cv2.CascadeClassifier('frecog/haarcascade_smile.xml')
def detect_faces(our_image):
new_img = np.array(our_image.convert('RGB'))
img = cv2.cvtColor(new_img,1)
gray = cv2.cvtColor(new_img, cv2.COLOR_BGR2GRAY)
# Detect faces
faces = face_cascade.detectMultiScale(gray, 1.1, 4)
# Draw rectangle around the faces
for (x, y, w, h) in faces:
cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)
return img,faces
def detect_eyes(our_image):
new_img = np.array(our_image.convert('RGB'))
img = cv2.cvtColor(new_img,1)
gray = cv2.cvtColor(new_img, cv2.COLOR_BGR2GRAY)
eyes = eye_cascade.detectMultiScale(gray, 1.3, 5)
for (ex,ey,ew,eh) in eyes:
cv2.rectangle(img,(ex,ey),(ex+ew,ey+eh),(0,255,0),2)
return img
def detect_smiles(our_image):
new_img = np.array(our_image.convert('RGB'))
img = cv2.cvtColor(new_img,1)
gray = cv2.cvtColor(new_img, cv2.COLOR_BGR2GRAY)
# Detect Smiles
smiles = smile_cascade.detectMultiScale(gray, 1.1, 4)
# Draw rectangle around the Smiles
for (x, y, w, h) in smiles:
cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)
return img
def cartonize_image(our_image):
new_img = np.array(our_image.convert('RGB'))
img = cv2.cvtColor(new_img,1)
gray = cv2.cvtColor(new_img, cv2.COLOR_BGR2GRAY)
# Edges
gray = cv2.medianBlur(gray, 5)
edges = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 9, 9)
#Color
color = cv2.bilateralFilter(img, 9, 300, 300)
#Cartoon
cartoon = cv2.bitwise_and(color, color, mask=edges)
return cartoon
def cannize_image(our_image):
new_img = np.array(our_image.convert('RGB'))
img = cv2.cvtColor(new_img,1)
img = cv2.GaussianBlur(img, (11, 11), 0)
canny = cv2.Canny(img, 100, 150)
return canny
def main():
"""Face Detection App"""
st.title("Face Detection App")
st.text("Build with Streamlit and OpenCV")
activities = ["Detection","About"]
choice = st.sidebar.selectbox("Select Activty",activities)
if choice == 'Detection':
st.subheader("Face Detection")
image_file = st.file_uploader("Upload Image",type=['jpg','png','jpeg'])
if image_file is not None:
our_image = Image.open(image_file)
st.text("Original Image")
# st.write(type(our_image))
st.image(our_image)
enhance_type = st.sidebar.radio("Enhance Type",["Original","Gray-Scale","Contrast","Brightness","Blurring"])
if enhance_type == 'Gray-Scale':
new_img = np.array(our_image.convert('RGB'))
img = cv2.cvtColor(new_img,1)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# st.write(new_img)
st.image(gray)
elif enhance_type == 'Contrast':
c_rate = st.sidebar.slider("Contrast",0.5,3.5)
enhancer = ImageEnhance.Contrast(our_image)
img_output = enhancer.enhance(c_rate)
st.image(img_output)
elif enhance_type == 'Brightness':
c_rate = st.sidebar.slider("Brightness",0.5,3.5)
enhancer = ImageEnhance.Brightness(our_image)
img_output = enhancer.enhance(c_rate)
st.image(img_output)
elif enhance_type == 'Blurring':
new_img = np.array(our_image.convert('RGB'))
blur_rate = st.sidebar.slider("Brightness",0.5,3.5)
img = cv2.cvtColor(new_img,1)
blur_img = cv2.GaussianBlur(img,(11,11),blur_rate)
st.image(blur_img)
elif enhance_type == 'Original':
st.image(our_image,width=300)
else:
st.image(our_image,width=300)
# Face Detection
task = ["Faces","Smiles","Eyes","Cannize","Cartonize"]
feature_choice = st.sidebar.selectbox("Find Features",task)
if st.button("Process"):
if feature_choice == 'Faces':
result_img,result_faces = detect_faces(our_image)
st.image(result_img)
st.success("Found {} faces".format(len(result_faces)))
elif feature_choice == 'Smiles':
result_img = detect_smiles(our_image)
st.image(result_img)
elif feature_choice == 'Eyes':
result_img = detect_eyes(our_image)
st.image(result_img)
elif feature_choice == 'Cartonize':
result_img = cartonize_image(our_image)
st.image(result_img)
elif feature_choice == 'Cannize':
result_canny = cannize_image(our_image)
st.image(result_canny)
elif choice == 'About':
st.subheader("About Face Detection App")
st.markdown("Built with Streamlit by [Gourav Ojha](https://github.com/gouravojha)")
st.text("Gourav Ojha")
if __name__ == '__main__':
main()