This code loads a pre-trained CNN model to recognize the digits, captures the video from the webcam, and analyzes each frame in real-time to recognize the digits. The code uses OpenCV to preprocess the images and extract the digits from the video frames. The recognized digits are printed on the video frames and displayed in real-time.
import cv2
import numpy as np
from keras.models import load_model
# Load the pre-trained CNN model
model = load_model('model.h5')
# Define the size of the image to be analyzed
IMG_SIZE = 28
# Define the function to preprocess the image
def preprocess_image(img):
img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
img = img.astype('float32') / 255.0
img = np.reshape(img, (1, IMG_SIZE, IMG_SIZE, 1))
return img
# Define the function to recognize the digit
def recognize_digit(img):
img_processed = preprocess_image(img)
digit = model.predict_classes(img_processed)[0]
return digit
# Capture the video from the webcam
cap = cv2.VideoCapture(0)
while True:
# Read a frame from the video stream
ret, frame = cap.read()
# Convert the frame to grayscale
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# Threshold the grayscale image
ret, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)
# Find the contours in the thresholded image
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# Loop through all the contours
for contour in contours:
# Find the bounding rectangle of the contour
x, y, w, h = cv2.boundingRect(contour)
# Ignore contours that are too small
if w < 10 or h < 10:
continue
# Draw the rectangle around the contour
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
# Extract the digit from the image
digit_img = gray[y:y+h, x:x+w]
# Recognize the digit
digit = recognize_digit(digit_img)
# Print the recognized digit on the frame
cv2.putText(frame, str(digit), (x, y), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
# Display the video stream
cv2.imshow('Handwritten Digit Recognition', frame)
# Wait for a key press
key = cv2.waitKey(1)
# If the 'q' key is pressed, exit the loop
if key == ord('q'):
break
# Release the resources
cap.release()
cv2.destroyAllWindows()
