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VideoCapture shows "no camera access" placeholder image

I am trying to display a live video feed from camera. When I run the program, ret returns true but cv2.imshow() displays a placeholder image. Any help would be greatly appreciated.
import numpy as np
from cv2 import cv2
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
#initialize video from the webcam
video = cv2.VideoCapture(0)
print(cv2.VideoCapture(0).isOpened()) # ->returns True
while True:
# ret tells if the camera works properly. Frame is an actual frame from the video feed
ret, frame= video.read()
if ret ==True:
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# Detect the faces
faces = face_cascade.detectMultiScale(gray, 1.1, 4)
# Draw the rectangle around each face
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
# Display
cv2.imshow('img', frame)
if cv2.waitKey(30) & 0xff==27:
break
video.release()
cv2.destroyAllWindows()
cv2.imshow('img', frame) opens the following window.
So, I checked whether the Camera permission is allowed and it seems it already is. I am using MacOS Big Sur (version 11.6).

Auto zooming while face detected

hi guys I want to implement (zoom in and zoom out) like digital camera to the detected faces while real-time capturing using opencv, is there is any way I can do it without just cropping the frame then display it.
here is my code ... ,,,
import cv2
# Load the cascade
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
# To capture video from webcam.
cap = cv2.VideoCapture(0)
# To use a video file as input
# cap = cv2.VideoCapture('filename.mp4')
while True:
# Read the frame
_, img = cap.read()
# Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Detect the faces
faces = face_cascade.detectMultiScale(gray, 1.1, 4)
# Draw the rectangle around each face
for (x, y, w, h) in faces:
cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)
# Display
cv2.imshow('img', img)
# Stop if escape key is pressed
k = cv2.waitKey(30) & 0xff
if k==27:
break
# Release the VideoCapture object
cap.release()

Use cv2.videocapture without it opening a window

I am trying to use face detection but I do not want the video feed window to open up when I use videocapture, this is the code I'm working on:
import cv2
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
cap = cv2.VideoCapture(0)
while True:
_, img = cap.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.1, 4)
for (x, y, w, h) in faces:
cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)
cv2.imshow('img', img)
k = cv2.waitKey(30) & 0xff
if k==27:
break
cap.release()
I just want to somehow disable the video feed window that opens up automatically and instead get an output on the command line every time it detects a face.
I am using the code from here: https://github.com/adarsh1021/facedetection.
Thank you.

Reomve cv2.imshow('img', img) and replace it with print(faces)
cv2.imshow() is responsible for opening of image window.

Python: classify objects in images

camera = webcam; % Connect to the camera
nnet = alexnet; % Load the neural net
while true
picture = camera.snapshot; % Take a picture
picture = imresize(picture,[227,227]); % Resize the picture
label = classify(nnet, picture); % Classify the picture
image(picture); % Show the picture
title(char(label)); % Show the label
drawnow;
end
I found this matlab code in the internet. It displays a window with the picture from a webcam and very quickly also names the things in the picture ("keyboard","bootle","pencil","clock"...). I want to do that in python.
So far I have this:
import cv2
import sys
faceCascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
video_capture = cv2.VideoCapture(0)
while True:
ret, frame = video_capture.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = faceCascade.detectMultiScale(
gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30),
flags=cv2.cv.CV_HAAR_SCALE_IMAGE
)
# Draw a rectangle around the faces
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
cv2.imshow('Video', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
This is alreay very similar, but only detecting faces. The matlab code uses alexnet. I guess this is a pre-trained network based on imagenet data (http://www.image-net.org/). But it is no longer available.
How would I do this in python?
(There has been a similar question here, but it is 4 yrs. old and I think there are newer techniques now).

With the "tensorflow" package and the pre-trained network "vgg16", the solution is quite easy.
See https://github.com/machrisaa/tensorflow-vgg/blob/master/test_vgg16.py

count faces with python and opencv

I have script in python using opencv2 to detect face. I take video in my webcam and using Haar Cascade for detect faces. I want to get out of the number of detected faces in a one frame. I understand that this can be done by counting rectangles when a face is found. how to do it? How to count rectangles in one frame?
import cv2
import sys
faceCascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
video_capture = cv2.VideoCapture(0)
while True:
# Capture frame-by-frame
ret, frame = video_capture.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = faceCascade.detectMultiScale(
gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30),
flags=cv2.CASCADE_SCALE_IMAGE
)
# Draw a rectangle around the faces
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
# Display the resulting frame
cv2.imshow('Video', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# When everything is done, release the capture
video_capture.release()
cv2.destroyAllWindows()

Simple use of len(faces) should return the number of faces.