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from transformers import DetrFeatureExtractor, DetrForObjectDetection
import requests
import torch
feature_extractor = DetrFeatureExtractor.from_pretrained("facebook/detr-resnet-50")
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
# Core Pkgs
import time
from json import load
import streamlit as st
import cv2
from PIL import Image,ImageEnhance
import numpy as np
from io import BytesIO
from transformers import pipeline
st.set_page_config(page_title="Do Transform Images", initial_sidebar_state = "auto" )
st.title("Image Transformation & Detection App")
st.text("Build with Streamlit and OpenCV")
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')
obj_detector = pipeline('object-detection')
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 detect_objects(im):
inputs = feature_extractor(images=im, return_tensors="pt")
outputs = model(**inputs)
# convert outputs (bounding boxes and class logits) to COCO API
target_sizes = torch.tensor([im.size[::-1]])
results = feature_extractor.post_process(outputs, target_sizes=target_sizes)[0]
boxes = []
f=None
for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
box = [round(i, 2) for i in box.tolist()]
# let's only keep detections with score > 0.9
if score > 0.9:
st.success(
f"Detected {model.config.id2label[label.item()]} with confidence "
f"{round(score.item(), 3)} at location {box}"
)
boxes.append(box)
new_img = np.array(im.convert('RGB'))
img = cv2.cvtColor(new_img,1)
for (x, y, w, h) in boxes:
cv2.rectangle(img,(int(x),int(y)),(int(w), int(h)), (0, 0, 255))
return st.image(img)#st.image(box)
@st.cache
def load_image(img):
im = Image.open(img)
return im
activities = ["Detection","About"]
choice = st.sidebar.selectbox("Select Activty",activities)
def change_photo_state():
st.session_state["photo"]="done"
uploaded_photo = st.file_uploader("Upload Image",type=['jpg','png','jpeg'], on_change=change_photo_state)
camera_photo = st.camera_input("Take a photo", on_change=change_photo_state)
if "photo" not in st.session_state:
st.session_state["photo"]="not done"
if choice == 'Detection':
st.subheader("Process your images ...")
if st.session_state["photo"]=="done":
if uploaded_photo:
our_image= load_image(uploaded_photo)
if camera_photo:
our_image= load_image(camera_photo)
if uploaded_photo==None and camera_photo==None:
our_image=load_image("image.jpg")
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","detect_objects"]
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':
with st.spinner('Wait for it...'):
time.sleep(5)
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 feature_choice == 'detect_objects':
detect_objects(our_image)
elif choice == 'About':
st.subheader("About Face Detection App")
st.markdown("Built with Streamlit by [Soumen Sarker](https://soumen-sarker-personal-site.streamlitapp.com/)")
st.markdown("Credit [here](https://huggingface.co/models?pipeline_tag=object-detection)")
#st.success("Isshor Saves @Soumen Sarker") |