def newimg(img): #Image will be taken for Deskewing
newim = Deskew.deskew(img) #Deskew the image
#print(newim)
data = MyClust.get_all_points(
newim) #Gives all points according to threshold
print("Shape:", data.shape[0])
if (data.shape[0] < 300): print("Continue")
else:
print("Nice try !!!!")
return
############# CLUSTERING #################
lm = MyClust.Get_Clusters(
data,
num_clusters) #Data of all points will be clustered into some points
lm2 = np.array(Euclid.fun1(lm,
num_clusters)) #Distance array of all clusters
#print(lm2)
lm2 = Ham.Shortest_path_way(
lm2, num_clusters) #Order of the path it needed to travel
#print(lm2)
lm2 = np.array(lm2[0][:-1], dtype=np.int64)
#print(lm2)
mm = Ham.path_order(
lm2, lm,
num_clusters) #After sorting of the landmarks,path has been defined
#print(mm)
return (mm) #Returning path to comp2
def newimg(img_no):
newim = Deskew.deskew(test_images[img_no])
#print(newim)
data= MyClust.get_all_points(newim)
############# CLUSTERING #################
lm= MyClust.Get_Cluster(data,num_clusters)
lm2= np.array(Euclid.fun1(lm,num_clusters))
#print(lm2)
lm2= Ham.fun3(lm2,num_clusters)
lm2= np.array(lm2[0],dtype= np.int64)
order= lm2[:-1]
mm= Ham.path_order(order,lm,num_clusters)
#print(mm)
return(mm)
