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README (4).md

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+---
+title: Transform Image
+emoji: 🏃
+colorFrom: purple
+colorTo: yellow
+sdk: streamlit
+sdk_version: 1.10.0
+app_file: app.py
+pinned: false
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app (6).py

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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)
+
+@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 = ["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")

gitattributes (3)

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requirements (5).txt

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+Pillow
+altair<5
+streamlit==1.22.0
+opencv-python
+transformers
+torch
+timm