Pytorch Mask R-CNN 实例分割

Mask R-CNN通过在 Faster-RCNN 的基础上添加一个分支网络，在实现目标检测的同时，把目标像素分割出来.

图像分割是深度学习和神经网络的一个重要应用.

使用Pytorch自带的Mask R-CNN模型.

在实例分割中每个实例根据不同的类别划分颜色.如何实现呢? 可以这么说，我们可以检测出一张图片中的每个物体的存在，得到它的锚框，进行分类，然后用不同的颜色mask it 。所以说，实例分割是目标检测和图像分割的结合。

 

 

import cv2
import numpy as np
import random
import torch
import torchvision
import argparse
from PIL import Image
from torchvision.transforms import transforms as transforms
coco_names= [
    '__background__', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
    'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'N/A', 'stop sign',
    'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
    'elephant', 'bear', 'zebra', 'giraffe', 'N/A', 'backpack', 'umbrella', 'N/A', 'N/A',
    'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
    'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket',
    'bottle', 'N/A', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl',
    'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
    'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'N/A', 'dining table',
    'N/A', 'N/A', 'toilet', 'N/A', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
    'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'N/A', 'book',
    'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush'
]
COLORS = np.random.uniform(0, 255, size=(len(coco_names), 3))

def get_outputs(image, model, threshold):
    with torch.no_grad():
        # forward pass of the image through the modle
        outputs = model(image)
    
    # get all the scores
    scores = list(outputs[0]['scores'].detach().cpu().numpy())
    # index of those scores which are above a certain threshold
    thresholded_preds_inidices = [scores.index(i) for i in scores if i > threshold]
    thresholded_preds_count = len(thresholded_preds_inidices)
    # get the masks
    masks = (outputs[0]['masks']>0.5).squeeze().detach().cpu().numpy()
    # discard masks for objects which are below threshold
    masks = masks[:thresholded_preds_count]
    # get the bounding boxes, in (x1, y1), (x2, y2) format
    boxes = [[(int(i[0]), int(i[1])), (int(i[2]), int(i[3]))]  for i in outputs[0]['boxes'].detach().cpu()]
    # discard bounding boxes below threshold value
    boxes = boxes[:thresholded_preds_count]
    # get the classes labels
    labels = [coco_names[i] for i in outputs[0]['labels']]
    return masks, boxes, labels

def draw_segmentation_map(image, masks, boxes, labels):
    alpha = 1 
    beta = 0.6 # transparency for the segmentation map
    gamma = 0 # scalar added to each sum
    for i in range(len(masks)):
        red_map = np.zeros_like(masks[i]).astype(np.uint8)
        green_map = np.zeros_like(masks[i]).astype(np.uint8)
        blue_map = np.zeros_like(masks[i]).astype(np.uint8)
        # apply a randon color mask to each object
        color = COLORS[random.randrange(0, len(COLORS))]
        red_map[masks[i] == 1], green_map[masks[i] == 1], blue_map[masks[i] == 1]  = color
        # combine all the masks into a single image
        segmentation_map = np.stack([red_map, green_map, blue_map], axis=2)
        #convert the original PIL image into NumPy format
        image = np.array(image)
        # convert from RGN to OpenCV BGR format
        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
        # apply mask on the image
        cv2.addWeighted(image, alpha, segmentation_map, beta, gamma, image)
        # draw the bounding boxes around the objects
        cv2.rectangle(image, boxes[i][0], boxes[i][1], color=color, 
                      thickness=2)
        # put the label text above the objects
        cv2.putText(image , labels[i], (boxes[i][0][0], boxes[i][0][1]-10), 
                    cv2.FONT_HERSHEY_SIMPLEX, 1, color, 
                    thickness=2, lineType=cv2.LINE_AA)
    
    return image

parser = argparse.ArgumentParser()
parser.add_argument('-i', '--input', required=True, 
                    help='path to the input data')
parser.add_argument('-t', '--threshold', default=0.965, type=float,
                    help='score threshold for discarding detection')
args = vars(parser.parse_args(args=['-i','image1.jpg']))

# initialize the model
model = torchvision.models.detection.maskrcnn_resnet50_fpn(pretrained=True, progress=True, 
                                                           num_classes=91)
# set the computation device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# load the modle on to the computation device and set to eval mode
model.to(device).eval()
# transform to convert the image to tensor
transform = transforms.Compose([
    transforms.ToTensor()
])
image_path = args['input']
image = Image.open(image_path).convert('RGB')
# keep a copy of the original image for OpenCV functions and applying masks
orig_image = image.copy()
# transform the image
image = transform(image)
# add a batch dimension
image = image.unsqueeze(0).to(device)
masks, boxes, labels = get_outputs(image, model, args['threshold'])
result = draw_segmentation_map(orig_image, masks, boxes, labels)
# visualize the image
cv2.imshow('Segmented image', result)
cv2.waitKey(0)
# set the save path
save_path = f"../outputs/{args['input'].split('/')[-1].split('.')[0]}.jpg"
cv2.imwrite(save_path, result)