Visualization

[endeavorman]

Hi
Congratulations on your Oral Presentation being accepted.
Do you have the codes to visualize the corresponding segmentation results？I want to see the segmentation effect of the training model。Do I have access to the codes?

[chunbolang]

Hi,
thanks for your interest!

You can refer to the code below for visual analysis.

def plot_seg_result(img, mask, type=None, size=500, alpha=0.5, anns='mask'):
    assert type in ['red', 'blue', 'yellow']
    if type == 'red':
        color = (255, 50, 50)     # red  (255, 50, 50) (255, 90, 90) (252, 60, 60)
    elif type == 'blue':
        color = (90, 90, 218)   # blue (102, 140, 255) (90, 90, 218) (90, 154, 218)
    elif type == 'yellow':
        color = (255, 218, 90)  # yellow
    color_scribble = (255, 218, 90) # (255, 218, 90) (0, 0, 255)

    img_pre = img.copy()

    if anns == 'mask':
        for c in range(3):
            img_pre[:, :, c] = np.where(mask[:,:,0] == 1,
                                        img[:, :, c] * (1 - alpha) + alpha * color[c],
                                        img[:, :, c])            
    elif anns == 'scribble':
        mask[mask==255]=0
        mask = mask[:,:,0]
        dilated_size = 5
        Scribble_Expert = ScribblesRobot()
        scribble_mask = Scribble_Expert.generate_scribbles(mask)
        scribble_mask = ndimage.maximum_filter(scribble_mask, size=dilated_size) # 
        for c in range(3):
            img_pre[:, :, c] = np.where(scribble_mask == 1,
                                        color_scribble[c],
                                        img[:, :, c])                    
    elif anns == 'bbox':
        mask[mask==255]=0
        mask = mask[:,:,0]        
        bboxs = find_bbox(mask)
        for j in bboxs: 
            cv2.rectangle(img_pre, (j[0], j[1]), (j[0] + j[2], j[1] + j[3]), (255, 0, 0), 4) # -1->fill; 2->draw_rec

    img_pre = cv2.cvtColor(img_pre, cv2.COLOR_RGB2BGR)  
    
    if size is not None:
        img_pre = cv2.resize(img_pre, dsize=(size, size), interpolation=cv2.INTER_LINEAR)

    return img_pre

[endeavorman]

Thanks for your reply, sincerely!

[chunbolang]

You are welcome. Feel free to contact~

[endeavorman]

Trouble you again very sorry.
How to use model to generate image segmentation results? For example, use the model to generate PNG files for segmentation results. Is there code for that? You only tested the relevant IOU.

[chunbolang]

Hi,
You can generate a demo file based on test.py. Specifically, changing the mode of dataloader to 'demo' and adding the following code here to get the visual result:

que_img = ori_img_list[-1][0].numpy()
que_mask = ori_label.cpu().numpy().transpose((1, 2, 0))
pre_mask_final = output[:, :ori_label.size(1), :ori_label.size(2)].cpu().numpy().transpose((1, 2, 0))
que_gt = plot_seg_result(que_img, que_mask, type='blue')
que_pre_f = plot_seg_result(que_img, pre_mask_final, type='red')
cv2.imwrite(viz_path + 'que_gt.png', que_gt)                 # please specify the path
cv2.imwrite(viz_path + 'que_pre_f.png', que_pre_f)           # same here

Note that ori_img_list corresponds to this list, and plot_seg_result denotes the function given above.

[endeavorman]

Thank you for your detailed answer. I've got the segmentation results of the model.