I am currently trying to get contours of a collection of images. But CV2 is unable to return the full contours for various images as shown below.
Hence, I am looking for a way to either approximate the moments for the list of contours, a way to make CV2 return a single contour when calling the function "findContours(...)" or merge the list of contours returned into a single contour if possible.
My codes(Updated) currently consists of:
def find_if_close(cnt1,cnt2):
row1,row2 = cnt1.shape[0],cnt2.shape[0]
for i in range(row1):
for j in range(row2):
dist = np.linalg.norm(cnt1[i]-cnt2[j])
if abs(dist) < 50 :
return True
elif i==row1-1 and j==row2-1:
return False
def thresh_callback(thresh, img, gray, blur):
edges = cv2.Canny(blur,thresh,thresh*2)
drawing = np.zeros(img.shape,np.uint8) # Image to draw the contours
image, contours,hierarchy = cv2.findContours(edges,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
LENGTH = len(contours)
status = np.zeros((LENGTH,1))
for i,cnt1 in enumerate(contours):
x = i
if i != LENGTH-1:
for j,cnt2 in enumerate(contours[i+1:]):
x = x+1
dist = find_if_close(cnt1,cnt2)
if dist == True:
val = min(status[i],status[x])
status[x] = status[i] = val
else:
if status[x]==status[i]:
status[x] = i+1
unified = []
maximum = int(status.max())+1
for i in range(maximum):
pos = np.where(status==i)[0]
if pos.size != 0:
cont = np.vstack(contours[i] for i in pos)
unified.append(cont)
cv2.drawContours(img,unified,-1,(0,255,0),2)
cv2.drawContours(drawing,unified,-1,255,-1)
cv2.imshow('output',img)
cv2.imshow('input',drawing)
cv2.waitKey(0)
cv2.destroyAllWindows()
return moments, cx, cy, count
def alter_image(img, blur):
ret, thresh1 = cv2.threshold(blur, 50, 255, cv2.THRESH_BINARY)
bitwise = cv2.bitwise_not(thresh1)
erosion = cv2.erode(bitwise, np.ones((2, 2) ,np.uint8), iterations=1) #15
dilation = cv2.dilate(erosion, np.ones((3, 3) ,np.uint8), iterations=1) #45
return dilation
imgs = cv2.imread('./images/'+str(num)+'.jpg')
img_grey = cv2.cvtColor(imgs,cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(img_grey,(5,5),0)
imgs = alter_image(imgs, blur)
thresh = 255
max_thresh = 255
moments, cx, cy, count = thresh_callback(thresh, imgs, img_grey, blur)
As shown in the codes, I tried to alter the image through changes such as erosion, dilation, bitwise changes but the results were still the same.The original image can be found below.
Image Output (Updated):
Related
I am loading several images will go over my face and I am having difficulty getting the image to go over the square for face created. I have looked at a many resources , but for some reason I am receiving an error when attempting to follow their method.
Every time I do so , I receive an error
ValueError: could not broadcast input array from shape (334,334,3) into shape (234,234,3)
I think the images might be too large, however I tried to resize them to see if they will fit to no avail.
here is my code:
import cv2
import sys
import logging as log
import datetime as dt
from time import sleep
import os
import random
from timeit import default_timer as timer
cascPath = "haarcascade_frontalface_default.xml"
faceCascade = cv2.CascadeClassifier(cascPath)
#log.basicConfig(filename='webcam.log',level=log.INFO)
video_capture = cv2.VideoCapture(0)
anterior = 0
#s_img = cv2.imread("my.jpg")
increment = 0
for filename in os.listdir("Faces/"):
if filename.endswith(".png"):
FullFile = (os.path.join("Faces/", filename))
#ret, frame = video_capture.read()
frame = cv2.imread(FullFile, cv2.IMREAD_UNCHANGED)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = faceCascade.detectMultiScale( gray,scaleFactor=1.1, minNeighbors=5, minSize=(30, 30) )
edges = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 9, 9)
for (x, y, w, h) in faces:
roi_color = frame[y:( y ) + ( h ), x:x + w]
status = cv2.imwrite('export/faces_detected'+ str( increment ) +'.png', roi_color)
increment = increment + 1
else:
continue
masks = []
for filename in os.listdir("export/"):
if filename.endswith(".png"):
FullFile = (os.path.join("export/", filename))
s_img = cv2.imread(FullFile)
masks.append(s_img)
Start = timer()
End = timer()
MasksSize = len(masks)
nrand = random.randint(0, MasksSize -1 )
increment = 0
while True:
if not video_capture.isOpened():
print('Unable to load camera.')
sleep(5)
pass
# Capture frame-by-frame
ret, frame = video_capture.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = faceCascade.detectMultiScale(
gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30)
)
edges = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 9, 9)
# Draw a rectangle around the faces
for (x, y, w, h) in faces:
if (End - Start) > 3:
Start = timer()
End = timer()
nrand = random.randint(0, MasksSize -1 )
# -75 and +20 added to fit my face
cv2.rectangle(frame, (x, y - 75), (x+w, y+h+20), (0, 255, 0), 2)
s_img = masks[nrand]
increment = increment + 1
#maskresize = cv2.resize(s_img, (150, 150))
#frame[y:y+s_img.shape[0] , x:x+s_img.shape[1]] = s_img # problem occurs here with
# ValueError: could not broadcast input array from shape (334,334,3) into shape (234,234,3)
# I assume I am inserting somethign too big?
End = timer()
if anterior != len(faces):
anterior = len(faces)
#log.info("faces: "+str(len(faces))+" at "+str(dt.datetime.now()))
# Display the resulting frame
cv2.imshow('Video', frame)
#cv2.imshow('Video', cartoon)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Display the resulting frame
cv2.imshow('Video', frame)
# When everything is done, release the capture
video_capture.release()
cv2.destroyAllWindows()
In the following line,
frame[y:y+s_img.shape[0] , x:x+s_img.shape[1]] = s_img
you are trying to attempt to assign s_img to frame[y:y+s_img.shape[0] , x:x+s_img.shape[1]] which are of different shapes.
You can check the shapes of the two by printing the shape (it will be the same as the shapes mentioned in the error).
Try reshaping s_img to the same shape and then try to assign.
Refer to this link:https://www.geeksforgeeks.org/image-resizing-using-opencv-python/
I used this function to resize the image to scale.
def image_resize(image, width = None, height = None, inter = cv2.INTER_AREA):
# initialize the dimensions of the image to be resized and
# grab the image size
dim = None
(h, w) = image.shape[:2]
# if both the width and height are None, then return the
# original image
if width is None and height is None:
return image
# check to see if the width is None
if width is None:
# calculate the ratio of the height and construct the
# dimensions
r = height / float(h)
dim = (int(w * r), height)
# otherwise, the height is None
else:
# calculate the ratio of the width and construct the
# dimensions
r = width / float(w)
dim = (width, int(h * r))
# resize the image
resized = cv2.resize(image, dim, interpolation = inter)
# return the resized image
return resized
Then later on called
r= image_resize(s_img, height = h, width=w)
frame[y:y+r.shape[0] , x:x+r.shape[1]] = r
Answer taken from here too:
Resize an image without distortion OpenCV
I'm currently trying to create a detector of aphids (green and rose) on plants but only using "classic" image processing technique (no neural network).
Here are an image I'm working on:
'aphids.jpg'
I'm working on a code (see below). If you apply it on the image you should have the plants alone. My problem is that I want to isolate the aphids that can be seen on the plants. There are a lot of them but I just want to detect the biggest or the more obvious.
On the code there is an "edges_detect" function I'm currently working on. One of the problem I have is that I can detect some of the aphids as contour but it will also take simple lines...
I tried to drop those line using the hierarchy of contour but it seems those line have inner contour so I can't easily delete them.
I also tried the adjust_gamma and contrast, but it doesn't give that much result.
I'm looking for more ideas. What would you try ?
Thank you in advance !
Here is the code:
import cv2
import numpy as np
import matplotlib.pyplot as plt
def adjust_gamma(image, gamma=1.0):
# build a lookup table mapping the pixel values [0, 255] to
# their adjusted gamma values
invGamma = 1.0 / gamma
table = np.array([((i / 255.0) ** invGamma) * 255
for i in np.arange(0, 256)]).astype("uint8")
# apply gamma correction using the lookup table
return cv2.LUT(image, table)
def adjust_contrast(image,alpha=1.0,beta=0):
new = np.zeros(image.shape,image.dtype)
for y in range(image.shape[0]):
for x in range(image.shape[1]):
for c in range(image.shape[2]):
new[y,x,c] = np.clip(alpha*image[y,x,c]+beta,0,255)
return(new)
def img_process(img):
(h1, w1) = img.shape[:2]
center = (w1 / 2, h1 / 2)
blur = cv2.GaussianBlur(img.copy(),(5,5),0)
hsv = cv2.cvtColor(blur,cv2.COLOR_BGR2HSV)
#image = img.copy()
#Boundaries to separate plants from the image
l_bound = np.array([20,0,0])
h_bound = np.array([90,250,170])#green
mask = cv2.inRange(hsv,l_bound,h_bound)
res = cv2.bitwise_and(img,img,mask=mask)
#Find contour plants
cnt,_ = cv2.findContours(mask,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
sort_cnt = sorted(cnt,key=cv2.contourArea,reverse=True)
cnt = [sort_cnt[i] for i in range(len(sort_cnt)) if cv2.contourArea(sort_cnt[i])>300]
cv2.drawContours(res, cnt, -1, (0,255,0), -1)
#Inverse mask to have only the plant in the image
mask2 = cv2.inRange(res,np.array([0,0,0]),np.array([250,250,250]))
mask2 = cv2.bitwise_not(mask2)
res2 = cv2.bitwise_and(img,img,mask=mask2)
#Augment bright/contrast
res2=res2*1.45
res2=res2.astype('uint8')
#Crop
res2 = res2[:-50,int(center[0]-300):int(center[0]+550)]
return res2
def edge_detec(img):
(h1, w1) = img.shape[:2]
center = (w1 / 2, h1 / 2)
blur = cv2.GaussianBlur(img.copy(),(5,5),0)
gray = cv2.cvtColor(blur,cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray,30,70,apertureSize = 3)
edges = edges[:-50,int(center[0]-300):int(center[0]+550)]
#kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3))
#edges = cv2.morphologyEx(edges, cv2.MORPH_GRADIENT, kernel)
cnt,hierarchy = cv2.findContours(edges,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
cnt = sorted(cnt,key=cv2.contourArea,reverse=True)
listArea = list(map(cv2.contourArea,cnt))
sort_cnt = [x for x in cnt if cv2.contourArea(x)>10]
cv2.drawContours(edges, sort_cnt, -1, (0,255,0), -1)
return edges,center,img
### Debut programme
img = cv2.imread('051.jpg')
while True:
##Put processing function here
img_mod = img_process(img)
cv2.imshow('img',img_mod)
if cv2.waitKey(1) & 0xFF == 27:
break
cv2.destroyAllWindows()
I am trying to display an image over another image at a particular co-ordinates. I have detected the aruco markers using the webcam and I want to display another image over the aruco marker. The aruco marker can be moved and the overlaying image should move along with the marker.
There is various draw functions and to input text into the image. I have tried image overlay and image homography.
I can obtain the co-ordinates for the corners.
Is there any function to insert the image at those co-ordinates?
import cv2
import cv2.aruco as aruco
import glob
markerLength = 0.25
cap = cv2.VideoCapture(0)
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
objp = np.zeros((6*7,3), np.float32)
objp[:,:2] = np.mgrid[0:7,0:6].T.reshape(-1,2)
objpoints = []
imgpoints = []
images = glob.glob('calib_images/*.jpg')
for fname in images:
img = cv2.imread(fname)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret, corners = cv2.findChessboardCorners(gray, (7,6),None)
if ret == True:
objpoints.append(objp)
corners2 = cv2.cornerSubPix(gray,corners,(11,11),(-1,-1),criteria)
imgpoints.append(corners2)
img = cv2.drawChessboardCorners(img, (7,6), corners2,ret)
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1],None,None)
calibrationFile = "calibrationFileName.xml"
calibrationParams = cv2.FileStorage(calibrationFile, cv2.FILE_STORAGE_READ)
camera_matrix = calibrationParams.getNode("cameraMatrix").mat()
dist_coeffs = calibrationParams.getNode("distCoeffs").mat()
while(True):
ret, frame = cap.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
aruco_dict = aruco.Dictionary_get(aruco.DICT_6X6_250)
arucoParameters = aruco.DetectorParameters_create()
corners, ids, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict, parameters=arucoParameters)
if np.all(ids != None):
rvec, tvec, _ = aruco.estimatePoseSingleMarkers(corners, markerLength, mtx, dist)
axis = aruco.drawAxis(frame, mtx, dist, rvec, tvec, 0.3)
print(ids)
display = aruco.drawDetectedMarkers(axis, corners)
display = np.array(display)
else:
display = frame
cv2.imshow('Display',display)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()```
To replace a part of image
import cv2
import numpy as np
img1 = cv2.imread('Desert.jpg')
img2 = cv2.imread('Penguins.jpg')
img3 = img1.copy()
# replace values at coordinates (100, 100) to (399, 399) of img3 with region of img2
img3[100:400,100:400,:] = img2[100:400,100:400,:]
cv2.imshow('Result1', img3)
To alpha blend two images
alpha = 0.5
img3 = np.uint8(img1*alpha + img2*(1-alpha))
cv2.imshow('Result2', img3)
#user8190410's answer works fine. Just to give a complete answer, in order to alpha blend two images with different size at a particular position, you can do the following:
alpha= 0.7
img1_mod = img1.copy()
img1_mod[:pos_x,:pos_y,:] = img1[:pos_x,:pos_y,:]*alpha + img2*(1-alpha)
cv2.imshow('Image1Mod', img1_mod)
Actually, I found that image homography can be used to do it.
Here is the updated code.
import numpy as np
import cv2
import cv2.aruco as aruco
cap = cv2.VideoCapture(0)
while(True):
ret, frame = cap.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
aruco_dict = aruco.Dictionary_get(aruco.DICT_6X6_250)
arucoParameters = aruco.DetectorParameters_create()
corners, ids, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict, parameters=arucoParameters)
if np.all(ids != None):
display = aruco.drawDetectedMarkers(frame, corners)
x1 = (corners[0][0][0][0], corners[0][0][0][1])
x2 = (corners[0][0][1][0], corners[0][0][1][1])
x3 = (corners[0][0][2][0], corners[0][0][2][1])
x4 = (corners[0][0][3][0], corners[0][0][3][1])
im_dst = frame
im_src = cv2.imread("mask.jpg")
size = im_src.shape
pts_dst = np.array([x1,x2,x3,x4])
pts_src = np.array(
[
[0,0],
[size[1] - 1, 0],
[size[1] - 1, size[0] -1],
[0, size[0] - 1 ]
],dtype=float
);
h, status = cv2.findHomography(pts_src, pts_dst)
temp = cv2.warpPerspective(im_src, h, (im_dst.shape[1],im_dst.shape[0]))
cv2.fillConvexPoly(im_dst, pts_dst.astype(int), 0, 16);
im_dst = im_dst + temp
cv2.imshow('Display',im_dst)
else:
display = frame
cv2.imshow('Display',display)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
I'm creating a object classifier in opencv python using svm. Training dataset is of 200 positive and 200 negative images. For positive images first took 200 images and cropped target object from images and resized them to (64,128) size for HOG calculation. Then for negative images, First created Pyramid of images then applied sliding window of 64X128 and then calculated HOG for positive as well all windows of negative images with labels 1 and 0. Trained svm model on hog features.
I am getting error "cv2.error: OpenCV(3.4.2) C:\projects\opencv-python\opencv\modules\ml\src\svm.cpp:2010: error: (-215:Assertion failed) samples.cols == var_count && samples.type() == 5 in function 'cv::ml::SVMImpl::predict' " when i called predict function using res = svm.predict(samples[0]).ravel() method.
import cv2
import os
import time
import numpy as np
import imutils
positive_path='C:\\Users\\Admin\\3D Objects\\datqaet with hog and svm\\ROI images'
negative_path='C:\\Users\\Admin\\3D Objects\\datqaet with hog and svm\\Negative images'
def pyramid(img): #Create image Pyramid
minSize=(30, 30)
imgarr = []
while True:
scale = 2
imgarr.append(img)
w = int(img.shape[1] / scale)
img = imutils.resize(img, width=w)
if img.shape[0] < minSize[1] or img.shape[1] < minSize[0]:
break
return imgarr
def sliding_window(image, stepSize, windowSize): #Sliding window for negative images
sliding = []
for y in range(0, image.shape[0], stepSize):
for x in range(0, image.shape[1], stepSize):
sliding.append((x, y, image[y:y + windowSize[1], x:x + windowSize[0]]))
return sliding
def get_hog() :
winSize = (64,128)
blockSize = (16,16)
blockStride = (16,16)
cellSize = (8,8)
nbins = 9
derivAperture = 1
winSigma = 4.
histogramNormType = 0
L2HysThreshold = 0.2
gammaCorrection = 0
nlevels = 64
signedGradient = True
hog = cv2.HOGDescriptor(winSize,blockSize,blockStride,cellSize,nbins,derivAperture,winSigma,histogramNormType,L2HysThreshold,gammaCorrection,nlevels, signedGradient)
return hog
samples = []
labels = []
sam = []
hog = get_hog()
for filename in os.listdir(positive_path):
img = cv2.imread(os.path.join(positive_path,filename),0) #RGB image
img = cv2.resize(img,(64,128))
img = np.array(img)
hist = hog.compute(img)
hist = cv2.normalize(hist,None)
sam.append(img)
samples.append(hist)
labels.append(1)
i=0
for filename in os.listdir(negative_path):
img = cv2.imread(os.path.join(negative_path,filename),0)
(winW, winH) = (64,128)
pyr = pyramid(img)
for resized in pyr:
sliding = sliding_window(resized, stepSize=32, windowSize=(winW, winH))
for (x, y, window) in sliding:
if window.shape[0] != winH or window.shape[1] != winW:
continue
hist = hog.compute(window)
hist = cv2.normalize(hist,None)
sam.append(window)
samples.append(hist)
labels.append(0)
print(i)
i=i+1
samples = np.array(samples,dtype=np.float32)
labels = np.array(labels,dtype=int)
samples = np.squeeze(samples)
print(len(samples))
print(samples.shape)
rand = np.random.RandomState(10)
shuffle = rand.permutation(len(samples))
sam = samples[shuffle]
samples = sam[shuffle]
labels = labels[shuffle]
svm = cv2.ml.SVM_create()
svm.setKernel(cv2.ml.SVM_LINEAR)
svm.setType(cv2.ml.SVM_C_SVC)
svm.setC(2.67)
svm.setGamma(5.383)
svm_params = dict( kernel_type = cv2.ml.SVM_LINEAR,
svm_type = cv2.ml.SVM_C_SVC,
C=2.67, gamma=5.383 )
svm.train(samples,cv2.ml.ROW_SAMPLE,labels)
print("trained")
res = svm.predict(samples[0]).ravel()
print(res)
cap = cv2.VideoCapture(0)
while True:
ret, img = cap.read()
img=cv2.resize(img,(400,400))
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
(winW, winH) = (64,128)
pyr = pyramid(img)
for resized in pyr:
sliding = sliding_window(resized, stepSize=32, windowSize=(winW, winH))
for (x, y, window) in sliding:
if window.shape[0] != winH or window.shape[1] != winW:
continue
hist = hog.compute(window)
hist = cv2.normalize(hist,None)
hist = np.reshape(hist,(1,hist.shape[0]))
res = svm.predict(hist)[1].ravel()
if res == 1:
print("found")
cv2.imshow('img',img)
cv2.waitKey(10)
Image used --
My code:
# multiple programs
import cv2
import numpy as np
img = cv2.imread('Dodo.jpg', 0)
ret, thresh = cv2.threshold(img, 127, 255, 0)
img2, contours, hierarchy = cv2.findContours(thresh, 1, 2)
cnt = contours[0]
M = cv2.moments(cnt)
print(M)
cx = int(M['m10']/ M['m00'])
cy = int(M['m01']/ M['m00'])
print("Cx:", cx, "Cy:", cy)
area = cv2.contourArea(cnt)
print("Area:", area)
perimeter = cv2.arcLength(cnt, True)
print("Perimeter:", perimeter)
epsilon = 0.1*cv2.arcLength(cnt,True)
approx = cv2.approxPolyDP(cnt,epsilon,True)
imgapprox = cv2.drawContours(img,[approx],0,(0,0,255),2)
hull = cv2.convexHull(cnt)
imghull =cv2.drawContours(img,[hull],0,(0,0,255),2)
k = cv2.isContourConvex(cnt)
print(k)
x,y,w,h = cv2.boundingRect(cnt)
rectst = cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2)
rect = cv2.minAreaRect(cnt)
box = cv2.boxPoints(rect)
box = np.int0(box)
rectrt =cv2.drawContours(img,[box],0,(0,0,255),2)
cv2.imshow('StraightRect', rectst)
cv2.imshow('RotatedRect', rectrt)
cv2.imshow('Approx', imgapprox)
cv2.imshow('hull', imghull)
cv2.waitKey()
cv2.destroyAllWindows()
OpenCV-Python version 3.4.1
So I am trying to learn the contour section in OpenCV (Link below)
Link : https://docs.opencv.org/3.4.1/dd/d49/tutorial_py_contour_features.html
Now the output is the same for all the features. i.e. same output for every cv2.imshow here.
Why? What is the error?
If it is overwriting the previous feature, then how do I display every feature?
Please help. Thanks :)
You are making the change in the same image each time.
Use image.copy() in cv2.drawContours(img.copy ,.......) , cv2.rectangle(img.copy(),.....)
.Because of that it seems they are showing the same features but it isn't .
Also since the background is black you are not able to see the rectangles and contour properly
Try this:
import cv2
import numpy as np
img = cv2.imread('Dodo.jpg')
f1 = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
f1 = cv2.threshold(f1, 120,255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)[1]
img2, contours, hierarchy = cv2.findContours(f1, cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
#ret, thresh = cv2.threshold(img, 127, 255, 0)
#img2, contours, hierarchy = cv2.findContours(thresh, 1, 2)
cnt = contours[0]
M = cv2.moments(cnt)
print(M)
cx = int(M['m10']/ M['m00'])
cy = int(M['m01']/ M['m00'])
print("Cx:", cx, "Cy:", cy)
area = cv2.contourArea(cnt)
print("Area:", area)
perimeter = cv2.arcLength(cnt, True)
print("Perimeter:", perimeter)
epsilon = 0.1*cv2.arcLength(cnt,True)
approx = cv2.approxPolyDP(cnt,epsilon,True)
imgapprox = cv2.drawContours(img.copy(),[approx],0,(0,0,255),2)
hull = cv2.convexHull(cnt)
imghull =cv2.drawContours(img.copy(),[hull],0,(0,0,255),2)
k = cv2.isContourConvex(cnt)
print(k)
x,y,w,h = cv2.boundingRect(cnt)
rectst = cv2.rectangle(img.copy(),(x,y),(x+w,y+h),(0,255,0),2)
rect = cv2.minAreaRect(cnt)
box = cv2.boxPoints(rect)
box = np.int0(box)
rectrt =cv2.drawContours(img.copy(),[box],0,(0,0,255),2)
cv2.imshow('StraightRect', rectst)
cv2.imshow('RotatedRect', rectrt)
cv2.imshow('Approx', imgapprox)
cv2.imshow('hull', imghull)
cv2.waitKey()
cv2.destroyAllWindows()
This is the result i get after executing the above code.