We will discuss how we can apply Face Detection using OpenCV. We go straightforward with a practical reproducible example.
The logic it the following: We get the image from the URL (or from the hard disk). We convert it to an
numpy array and then to a grayscale. Then by applying the proper CascadeClassifier we get the bounding boxes of the faces. Finally, using PIllow (or even OpenCV) we can draw the boxes on the initial image.
import cv2 as cv import numpy as np import PIL from PIL import Image import requests from io import BytesIO from PIL import ImageDraw ## I have commented out the cat and eye cascade. Notice that the xml files are in the opencv folder that you have downloaded and installed ## so it is good a idea to write the whole path face_cascade = cv.CascadeClassifier('C:\\opencv\\build\\etc\\haarcascades\\haarcascade_frontalface_default.xml') #cat_cascade = cv.CascadeClassifier('C:\\opencv\\build\\etc\\haarcascades\\haarcascade_frontalcatface.xml') #eye_cascade = cv.CascadeClassifier('C:\\opencv\\build\\etc\\haarcascades\\haarcascade_eye.xml') URL = "https://images.unsplash.com/photo-1525267219888-bb077b8792cc?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=1050&q=80" response = requests.get(URL) img = Image.open(BytesIO(response.content)) img_initial = img.copy() ## convert it to np array img = np.asarray(img) gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY) faces = face_cascade.detectMultiScale(gray) ## And lets just print those faces out to the screen #print(faces) drawing=ImageDraw.Draw(img_initial) ## For each item in faces, lets surround it with a red box for x,y,w,h in faces: ## That might be new syntax for you! Recall that faces is a list of rectangles in (x,y,w,h) ## format, that is, a list of lists. Instead of having to do an iteration and then manually ## pull out each item, we can use tuple unpacking to pull out individual items in the sublist ## directly to variables. A really nice python feature # ## Now we just need to draw our box drawing.rectangle((x,y,x+w,y+h), outline="red") display(img_initial)
#python #facedetection #ai #opencv #opencv-python
Real Time Object Detection in Python And OpenCV
Github Link: https://github.com/Chando0185/Object_Detection
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#python project #object detection #python opencv #opencv object detection #object detection in python #python opencv for object detection
In recent news, US-based NLP startup, Hugging Face has raised a whopping $40 million in funding. The company is building a large open-source community to help the NLP ecosystem grow. Its transformers library is a python-based library that exposes an API for using a variety of well-known transformer architectures such as BERT, RoBERTa, GPT-2, and DistilBERT. Here is a list of the top alternatives to Hugging Face .
#opinions #alternatives to hugging face #chatbot #hugging face #hugging face ai #hugging face chatbot #hugging face gpt-2 #hugging face nlp #hugging face transformer #ibm watson #nlp ai #nlp models #transformers
Detect the Presence of Live Human Face with Open Source Tools
#face-liveness-detection #opencv-python #face-recognition #keras #progaming #opencv
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Download source code at: https://drive.google.com/file/d/1qmRZ46mDrwq0VFaE86tzmuRwZHji6jQK/edit
#python #face detection #opencv
The ongoing novel coronavirus pandemic situation is known to each and every individual. Almost every country has been affected by the devastating Coronavirus(COVID-19) disease. The pandemic threatens to reverse hard-won gains made in global health and human capital over the past decade. The mandated confinement and social distancing measures in force during an extended period of time, will make it beneficial to flatten the curve of transmission. Artificial intelligence could play an important part in the post-COVID recovery, helping to boost productivity and foster a new generation of innovative companies. While this situation happens to worsen day by day, it is very much essential for everyone to follow some rules in order to remain safe than to get badly affected with its consequences.
The frontline warriors are working hard to save many lives but it is more of an individual’s responsibility to fight one’s own battle and not compromise their health. Few ways suggested for it are: Washing hands regularly, maintaining social distance, wear face mask regularly and staying quarantined, if unwell
Here, I have tried to design a custom deep learning model of Face Mask Detector using OpenCV, Keras/Tensorflow libraries which detects if an individual is wearing a face mask or not and alerting for the same.
#face-mask #face-mask-detection #opencv #covid19 #deep-learning