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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') | |
| #@st_cache | |
| #od(): | |
| #obj_detector = pipeline('object-detection') | |
| #return obj_detector | |
| 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) | |
| 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 = ["Detect_any_objects", "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': | |
| 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_any_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-website.streamlitapp.com/)") | |
| st.markdown("Credit [here](https://huggingface.co/models?pipeline_tag=object-detection)") | |
| #st.success("Isshor Saves @Soumen Sarker") |