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refactor: fix bugs and refactor
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## Name - Soumyajit Chakraborty
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## place - kolkata
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## date - 10 / 08 / 2020
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import cv2 as cv
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face_cascade = cv.CascadeClassifier("..\libs\haarcascade_frontalface_default.xml")
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face_cascade_eye = cv.CascadeClassifier("..\libs\haarcascade_eye.xml")
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# face_glass = cv.CascadeClassifier('..\libs\haarcascade_eye_tree_eyeglasses.xml')
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cap = cv.VideoCapture(0)
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while cap.isOpened():
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falg, img = cap.read() # start reading the camera output i mean frames
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# cap.read() returning a bool value and a frame onject type value
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gray = cv.cvtColor(
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img, cv.COLOR_BGR2GRAY
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) # converting to grayscale image to perform smoother
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faces = face_cascade.detectMultiScale(
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img, 1.1, 7
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) # we use detectMultiscale library function to detect the predefined structures of a face
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eyes = face_cascade_eye.detectMultiScale(img, 1.1, 7)
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# using for loops we are trying to read each and every frame and map
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for (x, y, w, h) in faces:
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cv.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 1)
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for (a, b, c, d) in eyes:
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cv.rectangle(img, (a, b), (a + c, b + d), (255, 0, 0), 1)
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cv.imshow("img", img)
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c = cv.waitKey(1)
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if c == ord("q"):
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break
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cv.release()
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cv.destroyAllWindows()
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def detect_faces_and_eyes():
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"""
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Detects faces and eyes in real-time using the webcam.
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Press 'q' to exit the program.
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"""
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# Load the pre-trained classifiers for face and eye detection
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face_cascade = cv.CascadeClassifier(r"..\libs\haarcascade_frontalface_default.xml")
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eye_cascade = cv.CascadeClassifier(r"..\libs\haarcascade_eye.xml")
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# Open the webcam
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cap = cv.VideoCapture(0)
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while cap.isOpened():
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# Read a frame from the webcam
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flag, img = cap.read()
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# Convert the frame to grayscale for better performance
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gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
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# Detect faces in the frame
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faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=7)
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# Detect eyes in the frame
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eyes = eye_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=7)
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# Draw rectangles around faces and eyes
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for x, y, w, h in faces:
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cv.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 1)
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for a, b, c, d in eyes:
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cv.rectangle(img, (a, b), (a + c, b + d), (255, 0, 0), 1)
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# Display the resulting frame
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cv.imshow("Face and Eye Detection", img)
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# Check for the 'q' key to exit the program
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key = cv.waitKey(1)
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if key == ord("q"):
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break
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# Release the webcam and close all windows
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cap.release()
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cv.destroyAllWindows()
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if __name__ == "__main__":
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# Call the main function
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detect_faces_and_eyes()
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Original file line numberDiff line numberDiff line change
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import cv2 as cv
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# import numpy as np
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# Read the image in grayscale
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img = cv.imread(r"..\img\hand1.jpg", cv.IMREAD_GRAYSCALE)
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img = cv.imread("..\img\hand1.jpg", 0)
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flag, frame = cv.threshold(img, 70, 255, cv.THRESH_BINARY)
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# Apply thresholding to create a binary image
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_, thresholded = cv.threshold(img, 70, 255, cv.THRESH_BINARY)
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contor, _ = cv.findContours(frame.copy(), cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
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# Find contours in the binary image
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contours, _ = cv.findContours(thresholded.copy(), cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
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hull = [cv.convexHull(c) for c in contor]
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# Convex Hull for each contour
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convex_hulls = [cv.convexHull(contour) for contour in contours]
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final = cv.drawContours(img, hull, -1, (0, 0, 0))
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cv.imshow("original_image", img)
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cv.imshow("thres", frame)
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cv.imshow("final_hsv", final)
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# Draw contours and convex hulls on the original image
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original_with_contours = cv.drawContours(img.copy(), contours, -1, (0, 0, 0), 2)
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original_with_convex_hulls = cv.drawContours(img.copy(), convex_hulls, -1, (0, 0, 0), 2)
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# Display the images
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cv.imshow("Original Image", img)
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cv.imshow("Thresholded Image", thresholded)
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cv.imshow("Contours", original_with_contours)
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cv.imshow("Convex Hulls", original_with_convex_hulls)
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# Wait for a key press and close windows
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cv.waitKey(0)
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cv.destroyAllWindows()

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