Drawing bounding rectangle around the tumor cv2 - python-3.x

Question

I am working on a project which predicts that the MRI has tumor or not, now the next step is to draw a bounding rectangle around the tumor. I was able to extract the tumor from the MRI, now I want to get the opposite corners of the rectangle to bound the tumor in original figure.
EDIT:
For some of the MRI images the I cannot separate the the tumor from MRI, calculated the threshold using OTSU method seperately but its not working properly.
Thank you !
Computing threshold:
path=r"ImageProc\Y54.jpg"
img = cv.imread(path,0)
blur = cv.GaussianBlur(img,(5,5),0)
# find normalized_histogram, and its cumulative distribution function
hist = cv.calcHist([blur],[0],None,[256],[0,256])
hist_norm = hist.ravel()/hist.sum()
Q = hist_norm.cumsum()
bins = np.arange(256)
fn_min = np.inf
thresh = -1
for i in range(1,256):
p1,p2 = np.hsplit(hist_norm,[i]) # probabilities
q1,q2 = Q[i],Q[255]-Q[i] # cum sum of classes
if q1 < 1.e-6 or q2 < 1.e-6:
continue
b1,b2 = np.hsplit(bins,[i]) # weights
# finding means and variances
m1,m2 = np.sum(p1*b1)/q1, np.sum(p2*b2)/q2
v1,v2 = np.sum(((b1-m1)**2)*p1)/q1,np.sum(((b2-m2)**2)*p2)/q2
# calculates the minimization function
fn = v1*q1 + v2*q2
if fn < fn_min:
fn_min = fn
thresh = i
# find otsu's threshold value with OpenCV function
ret, otsu = cv.threshold(blur,0,255,cv.THRESH_BINARY+cv.THRESH_OTSU)
print( "{} {}".format(thresh,ret) )
My progress so for through the code is:
import cv2
import matplotlib.pyplot as plt
def show_image(title, image):
cv2.imshow(title, image)
cv2.waitKey(0)
cv2.destroyAllWindows()
def show_image_plt(title, image, cmap = None):
plt.figure(title)
plt.imshow(image,cmap=cmap)
plt.axis('off')
plt.show()
def cvt_image_colorspace(image, colorspace = cv2.COLOR_BGR2GRAY):
return cv2.cvtColor(image, colorspace)
def median_filtering(image, kernel_size=3):
'''
:param image: grayscale image
:param kernel_size: kernel size should be odd number
:return: blurred image
'''
return cv2.medianBlur(image, kernel_size)
def apply_threshold(image, **kwargs):
'''
:param image: image object
:param kwargs: threshold parameters - dictionary
:return:
'''
threshold_method = kwargs['threshold_method']
max_value = kwargs['pixel_value']
threshold_flag = kwargs.get('threshold_flag', None)
if threshold_flag is not None:
ret, thresh1 = cv2.adaptiveThreshold(image, max_value, threshold_method,cv2.THRESH_BINARY,
kwargs['block_size'], kwargs['const'])
else:
ret, thresh1 = cv2.threshold(image, kwargs['threshold'], max_value, threshold_method)
return thresh1
def sobel_filter(img,x,y,kernel_size = 3):
return cv2.Sobel(img, cv2.CV_8U, x,y, ksize=kernel_size)
path=r"Imageproc\Y54.jpg"
image = cv2.imread(path, 1)
show_image('Original image', image)
#Step one - grayscale the image
grayscale_img = cvt_image_colorspace(image)
#show_image('Grayscaled image', grayscale_img)
#Step two - filter out image
median_filtered = median_filtering(grayscale_img,5)
#show_image('Median filtered', median_filtered)
#testing threshold function
bin_image = apply_threshold(median_filtered, **{"threshold" : 93,
"pixel_value" : 255,
"threshold_method" : cv2.THRESH_BINARY})
otsu_image = apply_threshold(median_filtered, **{"threshold" : 93,
"pixel_value" : 255,
"threshold_method" : cv2.THRESH_BINARY +
cv2.THRESH_OTSU})
#Step 3a - apply Sobel filter
img_sobelx = sobel_filter(median_filtered, 1, 0)
img_sobely = sobel_filter(median_filtered, 0, 1)
# Adding mask to the image
img_sobel = img_sobelx + img_sobely+grayscale_img
#show_image('Sobel filter applied', img_sobel)
#Step 4 - apply threshold
# Set threshold and maxValue
threshold = 160
maxValue = 255
# Threshold the pixel values
thresh = apply_threshold(img_sobel, **{"threshold" : 93,
"pixel_value" : 255,
"threshold_method" : cv2.THRESH_BINARY})
#show_image("Thresholded", thresh)
#Step 3b - apply erosion + dilation
#apply erosion and dilation to show only the part of the image having more intensity - tumor region
#that we want to extract
kernel=cv2.getStructuringElement(cv2.MORPH_RECT,(9,9))
erosion = cv2.morphologyEx(median_filtered, cv2.MORPH_ERODE, kernel)
#show_image('Eroded image', erosion)
dilation = cv2.morphologyEx(erosion, cv2.MORPH_DILATE, kernel)
#show_image('Dilatated image', dilation)
#Step 4 - apply thresholding
threshold = 160
maxValue = 255
# apply thresholding
new_thresholding = apply_threshold(dilation, **{"threshold" : 93,
"pixel_value" : 255,
"threshold_method" : cv2.THRESH_BINARY})
show_image('Threshold image after erosion + dilation', new_thresholding)
The output image for given MRI is:

Answer 1

I think the best way is to know where pixels are not black
pts = np.argwhere(new_thresholding>0)
y1,x1 = pts.min(axis=0)
y2,x2 = pts.max(axis=0)
new_thresholding_rect= cv2.rectangle(new_thresholding,(x1,y1),(x2,y2),(255,0,0),2)
show_image('Threshold image after erosion + dilation + Rectangle',new_thresholding_rect)