Increasing the speed of working with large data numpy array in Python

I am working on image processing and want to colorize a gray level image with my own algorithm. I work on the primary data in numpy array and based on my algorithm, I color the image in space HSV and convert it to space RGB. But the speed of my algorithm is low. Is there a way to implement my own algorithm with numpy functions instead of working with loops and conditions on the array? for example use of: np.where my code:

row, col = num1.shape    
for i in range(row):
for j in range(col):
    if num12[i][j]>=250:
        im[i][j][0] = 0
        im[i][j][1] = 0
        im[i][j][2] = num12[i][j]
    else:
        if num1[i][j] <=0:
            color[i][j]=0
            im[i][j][0] = 0
            im[i][j][1] = 0
            im[i][j][2] = 0
        else:                
            if num1[i][j]< num2[i][j]:
                if(num1[i][j]-num2[i][j]<-0.5):
                    im[i][j][0] = 107
                else:
                    im[i][j][0] = 33
            else:
                if num2[i][j] >= np.multiply(num1[i][j] , 1.1):
                    im[i][j][0] = 33
                elif num2[i][j] <= np.multiply(num1[i][j] , 1.1) and num2[i][j] >= np.multiply(num1[i][j] , 1):
                    im[i][j][0] = 107 
                elif num2[i][j] <= np.multiply(num1[i][j] , 1):
                    im[i][j][0] = 209
            im[i][j][1] = num12[i][j]
            im[i][j][2] = 255
    im[i][j][0], im[i][j][1], im[i][j][2] = HSV(im[i][j][0], im[i][j][1], im[i][j][2], max_sva=255).rgb()