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real_time.py
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real_time.py
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# 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()
#---------- Ends Points for Left Right Top and Bottom ----------------
def midpoint(ptA, ptB):
return ((ptA[0] + ptB[0]) * 0.5, (ptA[1] + ptB[1]) * 0.5)
#^^^^ You can use following code to take values from command line ^^^^^
"""# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True,
help="path to the input image")
ap.add_argument("-w", "--width", type=float, required=True,
help="width of the left-most object in the image (in inches)")
args = vars(ap.parse_args())"""
# load the image, convert it to grayscale, and blur it slightly
cam = cv2.VideoCapture(1)
while True:
_ ,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:
# unpack the ordered bounding box, then compute the
# midpoint between the top-left and top-right points,
# followed by the midpoint between the top-right and
# bottom-right
(tl, tr, br, bl) = box
(tlblX, tlblY) = midpoint(tl, bl)
(trbrX, trbrY) = midpoint(tr, br)
# compute the Euclidean distance between the midpoints,
# then construct the reference object
D = dist.euclidean((tlblX, tlblY), (trbrX, trbrY))
refObj = (box, (cX, cY), D / 2.8)
continue
# 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 them 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]
################# end ########################
#++++++++++++++++ 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 %%%%%%%%%%%%%%%%%%%%%%%%%%
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
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*350), int(rp1.y - delY*350)),
(int(rp2.x + delX*250), int(rp2.y + delY*250)),(205, 0, 0), 2)
x,y,z = image.shape
#The X axis in black
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
# connect them with a line
cv2.circle(orig, (int(xA), int(yA)), 5, color, -1)
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