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Full Body Human Detection Using OpenCV

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	`1`	`+# Full Body Human Detection`
	`2`	`+`
	`3`	`+Uses OpenCV to Detect Human using pre trained data.`
	`4`	`+`
	`5`	`+## Image Processing`
	`6`	`+`
	`7`	`+Image Processing is most commonly termed as 'Digital Image Processing' and the domain in which it is frequently used is 'Computer Vision'.`
	`8`	`+`
	`9`	`+Both Image Processing algorithms and Computer Vision (CV) algorithms take an image as input; however, in image processing,the output is also an image, whereas in computer vision the output can be some features/information about the image.`
	`10`	`+`
	`11`	`+## OpenCV`
	`12`	`+`
	`13`	`+![](https://logodix.com/logo/1989939.png)`
	`14`	`+`
	`15`	`+## Installation`
	`16`	`+`
	`17`	`+### Windows`
	`18`	`+`
	`19`	+`$ pip install opencv-python`
	`20`	`+`
	`21`	`+### MacOS`
	`22`	`+`
	`23`	+`$ brew install opencv3 --with-contrib --with-python3`
	`24`	`+`
	`25`	`+### Linux`
	`26`	`+`
	`27`	+`$ sudo apt-get install libopencv-dev python-opencv`

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	`1`	`+import cv2`
	`2`	`+img_file = "Human.jpg"`
	`3`	`+# trained human data Link:"https://raw.githubusercontent.com/opencv/opencv/master/data/haarcascades/haarcascade_fullbody.xml"`
	`4`	`+classifier_Human = 'car_dect.xml'`
	`5`	`+img = cv2.imread(img_file) # create image reader`
	`6`	`+# convert image black and white i.e. grayscale`
	`7`	`+black_and_white = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)`
	`8`	`+Human_detect = cv2.CascadeClassifier(classifier_Human) # create classifier`
	`9`	`+humans = Human_detect.detectMultiScale(black_and_white) # detect cars`
	`10`	`+`
	`11`	`+for (x, y, w, h) in cars: # the above variable will return 4 cordinates i.e height,width,postionx,positiony`
	`12`	`+ # this loop will create grren rectangle when car is detected`
	`13`	`+ cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 3)`
	`14`	`+`
	`15`	`+cv2.imshow('Human image', img) # display image`
	`16`	`+cv2.waitKey()`

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