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segments.py
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segments.py
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import numpy as np
import cv2
import cv2.aruco as aruco
import os
import matplotlib.pyplot as plt
#https://pysource.com/2018/02/14/perspective-transformation-opencv-3-4-with-python-3-tutorial-13/
def order_points(pts):
# initialzie a list of coordinates that will be ordered
# such that the first entry in the list is the top-left,
# the second entry is the top-right, the third is the
# bottom-right, and the fourth is the bottom-left
rect = np.zeros((4, 2), dtype = "float32")
# the top-left point will have the smallest sum, whereas
# the bottom-right point will have the largest sum
s = pts.sum(axis = 1)
rect[0] = pts[np.argmin(s)]
rect[2] = pts[np.argmax(s)]
# now, compute the difference between the points, the
# top-right point will have the smallest difference,
# whereas the bottom-left will have the largest difference
diff = np.diff(pts, axis = 1)
rect[1] = pts[np.argmin(diff)]
rect[3] = pts[np.argmax(diff)]
# return the ordered coordinates
return rect
def four_point_transform(image, pts):
# obtain a consistent order of the points and unpack them
# individually
rect = order_points(pts)
(tl, tr, br, bl) = rect
# compute the width of the new image, which will be the
# maximum distance between bottom-right and bottom-left
# x-coordiates or the top-right and top-left x-coordinates
widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
maxWidth = max(int(widthA), int(widthB))
# compute the height of the new image, which will be the
# maximum distance between the top-right and bottom-right
# y-coordinates or the top-left and bottom-left y-coordinates
heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
maxHeight = max(int(heightA), int(heightB))
# now that we have the dimensions of the new image, construct
# the set of destination points to obtain a "birds eye view",
# (i.e. top-down view) of the image, again specifying points
# in the top-left, top-right, bottom-right, and bottom-left
# order
dst = np.array([
[0, 0],
[maxWidth - 1, 0],
[maxWidth - 1, maxHeight - 1],
[0, maxHeight - 1]], dtype = "float32")
# print(dst)
# compute the perspective transform matrix and then apply it
M = cv2.getPerspectiveTransform(rect, dst)
warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))
# return the warped image
return warped
def sort_contours(cnts, method="top-to-bottom"):
# initialize the reverse flag and sort index
reverse = False
i = 0
# handle if we need to sort in reverse
if method == "right-to-left" or method == "bottom-to-top":
reverse = True
# handle if we are sorting against the y-coordinate rather than
# the x-coordinate of the bounding box
if method == "top-to-bottom" or method == "bottom-to-top":
i = 1
# construct the list of bounding boxes and sort them from top to
# bottom
boundingBoxes = [cv2.boundingRect(c) for c in cnts]
(cnts, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes),
key=lambda b:b[1][i], reverse=reverse))
# return the list of sorted contours and bounding boxes
return (cnts, boundingBoxes)
def segment(path):
valid_images = [".jpg",".gif",".png",".tga"]
# for s in os.listdir(path):
# ext = os.path.splitext(s)[1]
# if ext.lower() not in valid_images:
# continue
# imagec=cv2.imread(os.path.join(path,s))
imagec=cv2.imread(path)
# imagec=cv2.imread('test/1.jpg')
imagec=cv2.resize(imagec,(2500,3500))
image=cv2.cvtColor(imagec,cv2.COLOR_BGR2GRAY)
# cv2.imshow('vaa',image)
h,w = image.shape
image = cv2.blur(image,(3,3))
im2 = np.zeros(image.shape)
im2=np.uint8(im2)
aruco_dict = aruco.Dictionary_get(aruco.DICT_4X4_50)
parameters = aruco.DetectorParameters_create()
corners, ids, rejectedImgPoints = aruco.detectMarkers(
image, aruco_dict, parameters=parameters)
# frame_markers=aruco.drawDetectedMarkers(image, corners, ids, borderColor=(0, 220, 0))
# aruco.drawDetectedMarkers(image, rejectedImgPoints, borderColor=(0, 0, 0))
# cv2.imshow('00g',cv2.resize(image,(600,600)))
for rejected in rejectedImgPoints:
rejected = rejected.reshape((4, 2))
# cv2.line(image, tuple(rejected[0]), tuple(rejected[1]), (0, 0, 255), thickness=2)
# cv2.line(image, tuple(rejected[1]), tuple(rejected[2]), (0, 0, 255), thickness=2)
# cv2.line(image, tuple(rejected[2]), tuple(rejected[3]), (0, 0, 255), thickness=2)
# cv2.line(image, tuple(rejected[3]), tuple(rejected[0]), (0, 0, 255), thickness=2)
# cv2.rectangle(image, (rejected[0][0],rejected[0][1]), (rejected[2][0],rejected[2][1]), (255, 255, 255), -1)
# cv2.rectangle(im2, (rejected[0][0],rejected[0][1]), (rejected[2][0],rejected[2][1]), (255, 255, 255), -1)
penta = np.array([[rejected[0],rejected[1],rejected[2],rejected[3]]], np.int32)
area=(cv2.contourArea(penta))
if area>(h*w/1000):
cv2.polylines(im2 ,penta,True, (255, 255, 255), 1)
cv2.fillPoly(im2,penta, 255)
# cv2.imshow('g',cv2.resize(im2,(800,800)))
#cv2.waitKey(0)
#cv2.imwrite('diagonal1.png',im2)
temp=im2.copy()
image2=image.copy()
while True:
quater=int(h/3)
for t in range (quater,h-quater):
if np.sum(temp[t,:])==0:
middle = t
break
top=temp[:middle,:]
btm=temp[middle:,:]
top_img=image2[:middle,:]
btm_img=image2[middle:,:]
# cv2.imshow('g',cv2.resize(btm,(600,600)))
# cv2.waitKey(0)
(_,ct,_) = cv2.findContours(top, cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
(_,cb,_) = cv2.findContours(btm, cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
a1= sorted(ct, key=lambda x: cv2.contourArea(x))
a2= sorted(cb, key=lambda x: cv2.contourArea(x))
if (cv2.contourArea(a1[-1])>cv2.contourArea(a2[-1])):
break
else:
center = (w / 2, h / 2)
M = cv2.getRotationMatrix2D(center, 180, 1.0)
rotatedim2 = cv2.warpAffine(im2, M, (w, h))
rotatedimage = cv2.warpAffine(image2, M, (w, h))
temp=rotatedim2.copy()
image2=rotatedimage.copy()
# cv2.imshow('va',cv2.resize(rotatedimage,(600,600)))
continue
# cv2.imshow('va',cv2.resize(btm_img,(600,600)))
ctSort = sorted(ct, key=lambda x: cv2.contourArea(x))
cbSort,b=sort_contours(cb, "right-to-left")
top_seg=[]
for x2 in reversed(ctSort):
# cv2.drawContours(top_img, x2, -1, (0,0,0), 10)
# cv2.imshow('output', cv2.resize(top_img,(800,800)))
rc = cv2.minAreaRect(x2)
box = cv2.boxPoints(rc)
# box=np.uint32(box)
# for p in cord:
# pt = (p[0],p[1])
# cv2.circle(top_img,pt,5,(0,255,0),15)
warped = four_point_transform(top_img, box)
top_seg.append(warped)
cat=top_seg[4]
center=top_seg[3]
roll=top_seg[2]
mob=top_seg[1]
name=top_seg[0]
btm_seg=[]
for x3 in reversed(cbSort):
# cv2.drawContours(top_img, x2, -1, (0,0,0), 10)
# cv2.imshow('output', cv2.resize(top_img,(800,800)))
rc = cv2.minAreaRect(x3)
box1 = cv2.boxPoints(rc)
# box=np.uint32(box)
# for p1 in cord:
# pt1 = (p1[0],p1[1])
# cv2.circle(top_img,pt,5,(0,255,0),15)
warped1 = four_point_transform(btm_img, box1)
btm_seg.append(warped1)
q5=btm_seg[4]
q4=btm_seg[3]
q3=btm_seg[2]
q2=btm_seg[1]
q1=btm_seg[0]
#
#cv2.imshow('output', q1)
#cv2.waitKey(0)
#cv2.destroyAllWindows()
return q1,q2,q3,q4,q5,name,mob,roll,center,cat