EXPLAIN THIS PYTHON CODE FOR EMOTION DECTECTION IN STEP BY STEP import cv2 from deepface import DeepFace import numpy as np face_cascade_name = cv2.data.haarcascades + 'haarcascade_frontalface_alt.xml' #getting a haarcascade xml file face_cascade = cv2.CascadeClassifier() #processing it for our project if not face_cascade.load(cv2.samples.findFile(face_cascade_name)): #adding a fallback event print("Error loading xml file") video=cv2.VideoCapture(0) #requisting the input from the webcam or camera while video.idOpened(): #checking if are getting video feed and using it _,frame = video.read() gray=cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY) #changing the video to grayscale to make the face analisis work properly face=face_cascade.detectMultiScale(gray,scaleFactor=1.1,minNeighbors=5) for x,y,w,h in face: img=cv2.rectangle(frame,(x,y),(x+w,y+h),(0,0,255),1) #making a recentangle to show up and detect the face and setting it position and colour #making a try and except condition in case of any errors try: analyze = DeepFace.analyze(frame,actions=['emotions']) #same thing is happing here as the previous example, we are using the analyze class from deepface and using ‘frame’ as input print(analyze['dominant_emotion']) #here we will only go print out the dominant emotion also explained in the previous example except: print("no face") #this is the part where we display the output to the user cv2.imshow('video', frame) key=cv2.waitKey(1) if key==ord('q'): # here we are specifying the key which will stop the loop and stop all the processes going break video.release()
EXPLAIN THIS PYTHON CODE FOR EMOTION DECTECTION IN STEP BY STEP
import cv2
from deepface import DeepFace
import numpy as np
face_cascade_name = cv2.data.haarcascades + 'haarcascade_frontalface_alt.xml' #getting a haarcascade xml file
face_cascade = cv2.CascadeClassifier() #processing it for our project
if not face_cascade.load(cv2.samples.findFile(face_cascade_name)): #adding a fallback event
print("Error loading xml file")
video=cv2.VideoCapture(0) #requisting the input from the webcam or camera
while video.idOpened(): #checking if are getting video feed and using it
_,frame = video.read()
gray=cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY) #changing the video to grayscale to make the face analisis work properly
face=face_cascade.detectMultiScale(gray,scaleFactor=1.1,minNeighbors=5)
for x,y,w,h in face:
img=cv2.rectangle(frame,(x,y),(x+w,y+h),(0,0,255),1) #making a recentangle to show up and detect the face and setting it position and colour
#making a try and except condition in case of any errors
try:
analyze = DeepFace.analyze(frame,actions=['emotions']) #same thing is happing here as the previous example, we are using the analyze class from deepface and using ‘frame’ as input
print(analyze['dominant_emotion']) #here we will only go print out the dominant emotion also explained in the previous example
except:
print("no face")
#this is the part where we display the output to the user
cv2.imshow('video', frame)
key=cv2.waitKey(1)
if key==ord('q'): # here we are specifying the key which will stop the loop and stop all the processes going
break
video.release()
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