real_origin.py

# import the necessary packages
from scipy.spatial import distance as dist
from imutils import perspective
from imutils import contours
import numpy as np
import argparse
import imutils
import cv2
import math

# Class for point
class point:
  x = 0
  y = 0

# Points for Left Right Top and Bottom
plL = point()
plR = point()
plU = point()
plD = point()
 
def midpoint(ptA, ptB):
	return ((ptA[0] + ptB[0]) * 0.5, (ptA[1] + ptB[1]) * 0.5)
 
# Starting to capture video feed from the webcam
cam = cv2.VideoCapture(1)

while True:

	# load the image, convert it to grayscale, and blur it slightly
	_ ,image = cam.read()
	gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
	gray = cv2.GaussianBlur(gray, (7, 7), 0)
	
	# perform edge detection, then perform a dilation + erosion to
	# close gaps in between object edges
	edged = cv2.Canny(gray, 50, 100)
	edged = cv2.dilate(edged, None, iterations=1)
	edged = cv2.erode(edged, None, iterations=1)
	
	# find contours in the edge map
	cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,
		cv2.CHAIN_APPROX_SIMPLE)
	cnts = cnts[0] if imutils.is_cv2() else cnts[1]
	
	# sort the contours from left-to-right and, then initialize the
	# distance colors and reference object
	(cnts, _) = contours.sort_contours(cnts)
	colors = ((0, 0, 255), (240, 0, 159), (0, 165, 255), (255, 255, 0),
		(255, 0, 255))
	refObj = None
	pixelsPerMetric = None

	# loop over the contours individually
	for c in cnts:
		# if the contour is not sufficiently large, ignore it
		if cv2.contourArea(c) < 100:
			continue
	
		# compute the rotated bounding box of the contour
		box = cv2.minAreaRect(c)
		box = cv2.cv.BoxPoints(box) if imutils.is_cv2() else cv2.boxPoints(box)
		box = np.array(box, dtype="int")
	
		# order the points in the contour such that they appear
		# in top-left, top-right, bottom-right, and bottom-left
		# order, then draw the outline of the rotated bounding
		# box
		box = perspective.order_points(box)
	
		# compute the center of the bounding box
		cX = np.average(box[:, 0])
		cY = np.average(box[:, 1])

		# if this is the first contour we are examining (i.e.,
		# the left-most contour), we presume this is the
		# reference object
		
		if refObj is None:
			
			box1 = np.zeros(box.shape)
			
			# compute the Euclidean distance between the midpoints,
			# then construct the reference object
			
			rcX = 0
			rcY = 0
			refObj = (box1, (rcX, rcY), 27.6)

			# pixels per metric ratio was calculated manually as 
			# = total no. of pixels on x axis of frame/ width of frame in cm
			# i get total no of pixels on X axis using
			# x,y,c = image.shape

		# draw the contours on the image
		orig = image.copy()
		cv2.drawContours(orig, [box.astype("int")], -1, (0, 255, 0), 2)
		cv2.drawContours(orig, [refObj[0].astype("int")], -1, (0, 255, 0), 2)
	
		# stack the reference coordinates and the object coordinates
		# to include the object center
		refCoords = np.vstack([refObj[0], refObj[1]])
		objCoords = np.vstack([box, (cX, cY)])

		# Give the points values
		plL.x = box[0][0]
		plL.y = box[0][1]

		plR.x = box[1][0]
		plR.y = box[1][1]

		plU.x = box[2][0]
		plU.y = box[2][1]

		plD.x = box[3][0]
		plD.y = box[3][1]

		# Finding Height and width
		for (x, y) in box:
			cv2.circle(orig, (int(x), int(y)), 5, (0, 0, 255), -1)
		
		# unpack the ordered bounding box, then compute the midpoint
		# between the top-left and top-right coordinates, followed by
		# the midpoint between bottom-left and bottom-right coordinates
		(tl, tr, br, bl) = box
		(tltrX, tltrY) = midpoint(tl, tr)
		(blbrX, blbrY) = midpoint(bl, br)
	
		# compute the midpoint between the top-left and top-right points,
		# followed by the midpoint between the top-righ and bottom-right
		(tlblX, tlblY) = midpoint(tl, bl)
		(trbrX, trbrY) = midpoint(tr, br)
	
		# draw the midpoints on the image
		cv2.circle(orig, (int(tltrX), int(tltrY)), 5, (255, 0, 0), -1)
		cv2.circle(orig, (int(blbrX), int(blbrY)), 5, (255, 0, 0), -1)
		cv2.circle(orig, (int(tlblX), int(tlblY)), 5, (255, 0, 0), -1)
		cv2.circle(orig, (int(trbrX), int(trbrY)), 5, (255, 0, 0), -1)
	
		# draw lines between the midpoints
		cv2.line(orig, (int(tltrX), int(tltrY)), (int(blbrX), int(blbrY)),
			(255, 0, 255), 2)
		cv2.line(orig, (int(tlblX), int(tlblY)), (int(trbrX), int(trbrY)),
			(255, 0, 255), 2)
		
		# compute the Euclidean distance between the midpoints
		dA = dist.euclidean((tltrX, tltrY), (blbrX, blbrY))
		dB = dist.euclidean((tlblX, tlblY), (trbrX, trbrY))
	
		# if the pixels per metric has not been initialized, then
		# compute it as the ratio of pixels to supplied metric
		# (in this case, inches)
		if pixelsPerMetric is None:
			pixelsPerMetric = 27.6

		# compute the size of the object
		dimA = dA / pixelsPerMetric
		dimB = dB / pixelsPerMetric
	
		# draw the object sizes on the image
		cv2.putText(orig, "{:.1f}cm".format(dimA),
			(int(tltrX - 15), int(tltrY - 10)), cv2.FONT_HERSHEY_SIMPLEX,
			0.65, (255, 255, 255), 2)
		cv2.putText(orig, "{:.1f}cm".format(dimB),
			(int(trbrX + 10), int(trbrY)), cv2.FONT_HERSHEY_SIMPLEX,
			0.65, (255, 255, 255), 2)

		# Finding Angle of the length (the larger side) and not of the width
		
		rp1 = point()
		rp2 = point()
		
		Angle = 0

		if(dA>=dB):
			rp1.x = tltrX
			rp1.y = tltrY
			rp2.x = blbrX
			rp2.y = blbrY
		else:
			rp1.x = tlblX
			rp1.y = tlblY
			rp2.x = trbrX 
			rp2.y = trbrY

		# Extending the line of which angle is to be calculated
		
		delX = (rp2.x - rp1.x)/(math.sqrt(((rp2.x-rp1.x) ** 2)+((rp2.y-rp1.y) ** 2))) 
		delY = (rp2.y - rp1.y)/(math.sqrt(((rp2.x-rp1.x) ** 2)+((rp2.y-rp1.y) ** 2)))

		cv2.line(orig, (int(rp1.x - delX*250), int(rp1.y - delY*250)), 
		   (int(rp2.x + delX*250), int(rp2.y + delY*250)),(205, 0, 0), 2)
		
		x,y,z = image.shape

		# The x axis, makes it easy to see the angle
		cv2.line(orig, (0 , y/3), (x*20,y/3),
			(0, 0, 0), 2)
		
		gradient = (rp2.y - rp1.y)*1.0/(rp2.x - rp1.x)*1.0
		Angle = math.atan(gradient)
		Angle = Angle*57.2958

		if(Angle < 0):
			Angle = Angle + 180
		
		cv2.putText(orig, "{:.4f}".format(Angle) + " Degrees",
			(330, 460), cv2.FONT_HERSHEY_SIMPLEX,
			0.75, (0, 255, 255), 2)

		# loop over the original points
		for ((xA, yA), (xB, yB), color) in zip(refCoords, objCoords, colors):
			# draw circles corresponding to the current points and
			cv2.circle(orig, (int(xA), int(yA)), 5, color, -1)
			# connect them with a line
			cv2.circle(orig, (int(xB), int(yB)), 5, color, -1)
			cv2.line(orig, (int(xA), int(yA)), (int(xB), int(yB)), color, 2)
	
			# compute the Euclidean distance between the coordinates,
			# and then convert the distance in pixels to distance in
			# units
			D = dist.euclidean((xA, yA), (xB, yB)) / refObj[2]
			(mX, mY) = midpoint((xA, yA), (xB, yB))
			cv2.putText(orig, "{:.1f}cm".format(D), (int(mX), int(mY - 10)),
				cv2.FONT_HERSHEY_SIMPLEX, 0.55, color, 2)
	
			# show the output image
			cv2.imshow("Image", orig)
		
	if cv2.waitKey(1) & 0xFF == ord('q'):
         break