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How to reduce the size of the rectangular boxes on face detection in a group photo?

import cv2
import face_recognition as fc
image = fc.load_image_file('classA.jpg')
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
face_locations = fc.face_locations(image)
print(face_locations)
for i in range(len(face_locations)):
image = cv2.rectangle(image, (face_locations[i][3],face_locations[i][0],face_locations[i][1],face_locations[i][2]),(0,255,0),2)
cv2.imshow(' Students present in class detected',image)
cv2.waitKey(0)
I was expecting detected face should have one rectangle box only of the face not whole body

You need a top left corner and a bottom right corner. Like:
top_left = (face_locations[i][3], face_locations[i][0])
bottom_right = (face_locations[i][1], face_locations[i][2])
image = cv2.rectangle(image, top_left, bottom_right, (0,255,0), 2)
Hope it works for you.

How to get confidence value in Face Detection using Haar Cascade

I use open cv and haar cascade features to detect faces from an image. And after I load the cascade.xml library it will test the face read and draw a green rectangle in the face. My question is how to get confidence value from this library, like percentage or accusation value?
import cv2 as cv
img = cv.imread('Bryan/2.PNG')
cv.imshow('Bryan', img)
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
cv.imshow('Gray', gray)
haar_cascade = cv.CascadeClassifier('cascade.xml')
faces_rect = haar_cascade.detectMultiScale(
gray, scaleFactor=1.1, minNeighbors=6)
print(f'Number of faces of found = {len(faces_rect)}')
for (x, y, w, h) in faces_rect:
cv.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), thickness=2)
cv.imshow('Detected faces', img)
cv.waitKey(0)

How to customize parameters for face detection by using face cascade

I have a simple face detection implementation as following
import cv2
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
filename = "path/to/image"
img = cv2.imread(filename)
cv2.imshow("Original image", img)
face_region = face_cascade.detectMultiScale(img, 1.1, 4)
for (x, y, w, h) in face_region:
cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)
cv2.imshow("Output", img)
cv2.waitKey(0)
after running the code, I got the following result
As you can see that, the implementation detects two faces! How can I get rid of this kind of false detection?

first delete the textual data like in this link Delete OCR word from Image (OpenCV,Python)
after that try to use you face detection code...then it will improve your accuracy

OpenCV+Python face detector change rectangle size (25% bigger)

I want to change the rectangle size of this face detector i need it about 25% bigger:
import cv2
# Load the cascade
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
# Read the input image
img = cv2.imread('test.jpg')
# Convert into grayscale
gray = cv2.cvtColor(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)
# Display the output
cv2.imshow('img', img)
cv2.waitKey()
xml: https://raw.githubusercontent.com/opencv/opencv/master/data/haarcascades/haarcascade_frontalface_default.xml
but I don't know how to do it

OpenCV image cropping error

I executed a program that reads all .jpg files from directory, performs face detection, crops the faces and saves them.
The problem is that when run an official python program I am able to detect all faces, but it saves only few faces from every image.
What am I doing wrong?
import cv2
import sys
import glob
cascPath = "haarcascade_frontalface_default.xml"
# Create the haar cascade
faceCascade = cv2.CascadeClassifier(cascPath)
files=glob.glob("*.jpg")
for file in files:
# Read the image
image = cv2.imread(file)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Detect faces in the image
faces = faceCascade.detectMultiScale(
gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30),
flags = cv2.cv.CV_HAAR_SCALE_IMAGE
)
print "Found {0} faces!".format(len(faces))
# Crop Padding
left = 10
right = 10
top = 10
bottom = 10
# Draw a rectangle around the faces
for (x, y, w, h) in faces:
print x, y, w, h
# Dubugging boxes
# cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)
image = image[y-top:y+h+bottom, x-left:x+w+right]
print "cropped_{1}{0}".format(str(file),str(x))
cv2.imwrite("cropped_{1}_{0}".format(str(file),str(x)), image)

As Gall said in the comments, the problem comes from your indentation. Your last three lines are not executed for each face as their indentation does not make them part of the loop over the faces. You want something like this:
# Draw a rectangle around the faces
for (x, y, w, h) in faces:
image = image[y-top:y+h+bottom, x-left:x+w+right]
cv2.imwrite("cropped_{1}_{0}".format(str(file),str(x)), image)
Note that with this code, there is a possibility of filename collision (2 faces with same in x in an image). You may want want to use a unique string to avoid that problem. A simple counter would do the trick.

problem your code is that when the first person cuts out the image, following already tries to cut not from the original image, and already from a certain first person so it's code you look think everyone will understand
import numpy as np
import cv2
import sys
import glob
cascPath = "haarcascade_frontalface_default.xml"
faceCascade = cv2.CascadeClassifier(cascPath)
img = cv2.imread('3.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = faceCascade.detectMultiScale(
gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30),
#flags = cv2.cv.CV_HAAR_SCALE_IMAGE
flags=0
)
print "Found {0} faces!".format(len(faces))
left = 10
right = 10
top = 10
bottom = 10
i=0
count=0
for (x, y, w, h) in faces:
print x, y, w, h, i
i=i+1
img = img[y-top:y+h+bottom, x-left:x+w+right]
cv2.imwrite('foo{}.png'.format(count), img)
count += 1
img=cv2.imread('3.jpg')