inference.py

## Code for real time prediction ##

import cv2
import tensorflow as tf
import numpy as np
import time
import argparse
import mlflow 


parser = argparse.ArgumentParser()

parser.add_argument('--video_name', type=str, required=True)
parser.add_argument('--model_dirname', type=str, required=True)
args = parser.parse_args()

class_names = ["Angry", "Disgust", "Fear", "Happy", "Neutral", "Sad", "Surprise"]

model = mlflow.keras.load_model(f"mlruns/Fer2013-Experiment/{args.model_dirname}/artifacts/model")

cap = cv2.VideoCapture(f'testing/{args.video_name}')

faceDetect = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_default.xml')

# used to record the time when we processed last frame 
prev_frame_time = 0
  
# used to record the time at which we processed current frame 
new_frame_time = 0


while True:
    ret, frame = cap.read()
    if not ret:
        break
    
    new_frame_time = time.time() 
  
    # Calculating the fps 
  
    # fps will be number of frame processed in given time frame 
    # since their will be most of time error of 0.001 second 
    # we will be subtracting it to get more accurate result 
    fps = str(int(1/(new_frame_time-prev_frame_time)))
    cv2.putText(frame, fps, (7, 70), cv2.FONT_HERSHEY_SIMPLEX , 3, (100, 255, 0), 3, cv2.LINE_AA) 
    prev_frame_time = new_frame_time 

    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    faces = faceDetect.detectMultiScale(gray, 1.3, 3)

    for x, y, w, h in faces:
        sub_face_img = frame[y : y + h, x : x + w]

        resized = cv2.resize(sub_face_img, (48, 48))

        normalize = resized / 255.0
        
        reshaped = np.reshape(normalize, (1, 48, 48, 3))
        result = model.predict(reshaped)
        label = np.argmax(result, axis=1)[0]
       
        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 255), 1)
        cv2.rectangle(frame, (x, y), (x + w, y + h), (50, 50, 255), 2)
        cv2.rectangle(frame, (x, y - 40), (x + w, y), (50, 50, 255), -1)
        cv2.putText(frame, class_names[label], (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 255, 255), 2)
    
    cv2.imshow("Frame", frame)
    k = cv2.waitKey(1)
    if k == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()