DIC_for_GUI.py 文件源码

def template_match(img_master, img_slave, method = 'cv2.TM_CCOEFF_NORMED', mlx = 1, mly = 1, show=True):    

    # Apply image oversampling 
    img_master = cv2.resize(img_master,None,fx=mlx, fy=mly, interpolation = cv2.INTER_CUBIC)
    img_slave  = cv2.resize(img_slave,None,fx=mlx, fy=mly, interpolation = cv2.INTER_CUBIC)

    res = cv2.matchTemplate(img_slave,img_master,eval(method))

    w, h = img_master.shape[::-1]    
    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)

    # Control if the method is TM_SQDIFF or TM_SQDIFF_NORMED, take minimum value
    if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:
        top_left = min_loc
    else:
        top_left = max_loc 
    bottom_right = (top_left[0] + w, top_left[1] + h)

    # Retrieve center coordinates
    px = (top_left[0]+bottom_right[0])/(2.0*mlx)
    py = (top_left[1]+bottom_right[1])/(2.0*mly)

    # Scale images for visualization
    img_master_scaled = cv2.convertScaleAbs(img_master, alpha=(255.0/500))
    img_slave_scaled = cv2.convertScaleAbs(img_slave, alpha=(255.0/500))

    cv2.rectangle(img_slave_scaled,top_left, bottom_right, 255, 2*mlx) 

    if show == True:
        plt.figure(figsize=(20,10))
        plt.subplot(131),plt.imshow(res,cmap = 'gray')
        plt.title('Matching Result'), plt.xticks([]), plt.yticks([])
        plt.subplot(132),plt.imshow(img_master_scaled,cmap = 'gray')
        plt.title('Detected Point'), plt.xticks([]), plt.yticks([])
        plt.subplot(133),plt.imshow(img_slave_scaled, cmap = 'gray')
        plt.suptitle(method)
        plt.show()

    return px, py, max_val