/Measuring-Distance-From-Camera

To measure the distance from the Camera

Primary LanguagePython

Measuring-Distance-From-Camera

To measure the distance from the Camera

Table of Contents -

About Project

This Project aims for the measuring the distance from the point to the camera and giving the distance as the output. The code is executed in python language in Jupyter Notebook. I am providing the detailed explanation of each and every line of the code.

Detailed Explanation about Project

  • First install necessary libraries.

    1. from imutils import paths - paths is used to load the available images in a directory.
    2. import numpy as np - For numerical processing.
    3. import imutils - To make basic image processing functions such as skeletonization, displaying Matplotlib images, sorting contours, translation, rotation, resizing with OpenCV
    4. import cv2 - OpenCV.
    5. import matplotlib.pyplot as plt - To generate high quality line plots, scatter plots, histograms, bar charts.

  • Next is we define the find_marker function and it take one parameter - image. Taking example from pyimagesearch.com we are taking 8.5 x 11 inch piece of paper as our object and as we need to find the object, hence we would first convert this image into a grayscale image using cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) and then we would blur it using Gaussian Blur as cv2.GaussianBlur(gray, (5, 5), 0) so cv2.gaussianblur() function to apply Gaussian Smoothing on the input source image i.e gray in this case as input src image, then passing kernel size in the form of [height width] -> height and width should be odd and can have different values and finally we can detect the edges using canny edge detection. It is used for noise reduction. First argument is our input image. Second and third arguments are our minVal and maxVal respectively. After this edge of the paper i.e object is being clearly reveled.

    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5, 5), 0)
edged = cv2.Canny(gray, 35, 125)

  • Next is to find the contours in the edged image and keep the largest one. So for this we use - cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE), so Contours can be explained simply as a curve joining all the continuous points (along the boundary), having same color or intensity. The contours are a useful tool for shape analysis and object detection and recognition. We use RETR_EXTERNAL (Contour Retrieval Mode) because it returns only the extreme outer flags i.e only the outer boundary. Now coming to cv2.CHAIN_APPROX_SIMPLE, If we pass cv2.CHAIN_APPROX_NONE - all the boundary points are stored, but we only need the endpoints, so it removes all redundant points and compresses the contour, thereby saving memory. Then we use that imutils.grab_contours(cnts) that grabs the appropriate tuple value based on whether we are using OpenCV 2.4, 3, or 4. Finally we would be computing the bounding box of the of the paper region and return it

    cnts = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
c = max(cnts, key = cv2.contourArea)
return cv2.minAreaRect(c)

  • Then we would apply the formula for distance to camera i.e D = (W x F) / P where W => Width of the object, P => To measure the apparent width in pixels P, F => Focal length. Here we take knownWidth, focalLength, perWidth as W, F, P respectivily.

    def distance_to_camera(knownWidth, focalLength, perWidth):
	return (knownWidth * focalLength) / perWidth

  • Then for each and every image in the list, we would first sort the list and would iterate the sorted list. We load off the image using cv2.imread(imagePath), and find the marker (object) of the image find_marker(image) and distance in inches using function distance_to_camera(KNOWN_WIDTH, focalLength, marker[1][0]). Now to create the box around that rectangle, if we use OpenCV 2.4, then use cv2.cv.BoxPoints, else if use OpenCV 3, then use cv2.boxPoints, so overall we write it as - cv2.cv.BoxPoints(marker) if imutils.is_cv2() else cv2.boxPoints(marker). Now to draw the counters - cv2.drawContours function is used. It can also be used to draw any shape provided you have its boundary points. Its first argument is source image, second argument is the contours which should be passed as a Python list, third argument is index of contours (useful when drawing individual contour. To draw all contours, pass -1) and remaining arguments are color, thickness etc.

  • Now to display the text, we use cv2.putText(), we use cv2.putText(image, "%.2fft" % (inches / 12), (image.shape[1] - 200, image.shape[0] - 20), cv2.FONT_HERSHEY_SIMPLEX,2.0, (0, 255, 0), 3). In this parameter are :

    • input image
    • text => inches/12 is done to get the result in foot and %.2fft is done to get the value upto 2 decimal points.
    • (image.shape[1] - 200, image.shape[0] - 20) is done because it is the coordinates of the bottom - right corner of the text string in the image. The coordinates are represented as tuples of two values i.e. (X coordinate value, Y coordinate value). Originally the coordinates are displayed on bottom left, but if we want on bottom right, therefore to shift out the coordinates.
    • font => It denotes the font type. Some of font types are FONT_HERSHEY_SIMPLEX, FONT_HERSHEY_PLAIN. Here we use FONT_HERSHEY_SIMPLEX.
    • FontScale => taken as 2.0.
    • Color of Font => Taken Green, therefore - (0,255,0)
    • Thickness => taken as 3
    for imagePath in sorted(paths.list_images("images")):
  image = cv2.imread(imagePath)
  marker = find_marker(image)
  inches = distance_to_camera(KNOWN_WIDTH, focalLength, marker[1][0])
  box = cv2.cv.BoxPoints(marker) if imutils.is_cv2() else cv2.boxPoints(marker)
  box = np.int0(box)
  cv2.drawContours(image, [box], -1, (0, 255, 0), 2)
  text = cv2.putText(image, "%.2fft" % (inches / 12), (image.shape[1] - 200, image.shape[0] - 20), cv2.FONT_HERSHEY_SIMPLEX,2.0, (0, 255, 0), 3)
  cv2.imshow('image',image)

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