roi.txt

def convert(img, target_type_min, target_type_max, target_type):
    imin = img.min()
    imax = img.max()

    a = (target_type_max - target_type_min) / (imax - imin)
    b = target_type_max - a * imax
    new_img = (a * img + b).astype(target_type)
    return new_img

aaaa = convert(pixel_array, pixel_array.min(), pixel_array.max(), np.uint8)

#img = cv2.imread('images/mountain.jpg' , 0) # import image as grayscale array
img = aaaa.copy()
# threshold image
img_b = cv2.GaussianBlur(img, (13, 13), 2)
ret, img_th = cv2.threshold(img_b, 40, 255, cv2.THRESH_BINARY)
# find contours
(cnts,_) = cv2.findContours(img_th.copy(), cv2.RETR_TREE, 
cv2.CHAIN_APPROX_SIMPLE)
print(str(len(cnts))+' contours detected')

# find maximum area contour
area = np.array([cv2.contourArea(cnts[i]) for i in range(len(cnts))]) #  list of all areas
maxa_ind = np.argmax(area) # index of maximum area contour

plt.figure(figsize=(10,4))
plt.subplot(1,3,1)
plt.imshow(img_b, cmap='gray')
plt.title('GaussianBlurr')
plt.subplot(1,3,2)
plt.imshow(img_th, cmap='gray')
plt.title('threshold')
plt.subplot(1,3,3)
xx = [cnts[maxa_ind][i][0][0] for i in range(len(cnts[maxa_ind]))]
yy = [cnts[maxa_ind][i][0][1] for i in range(len(cnts[maxa_ind]))]
#ROI.append([min(xx),max(xx),min(yy),max(yy)])
plt.imshow(img, cmap='gray')
plt.plot(xx,yy,'r',linewidth=3)
plt.title('largest contour')