MASK-RCNNのspmallickさん実装コードを検出物体を透明度ゼロで塗りつぶすよう１行書き加えた


__[前回の記事](https://qiita.com/electronics_diy721/items/8ccc842ea1c7816f4edb)__で、以下のMASK-RCNNモデルの実装コードを動かしました。

####動かしたMask-RCNN実装コード

https://github.com/spmallick/learnopencv/tree/master/Mask-RCNN

####（ 前回の記事 ）

- [MASK-RCNNの学習済みモデルをCPU環境で動かしてみた](https://qiita.com/electronics_diy721/items/8ccc842ea1c7816f4edb)

今回、出力される画像が、次のように変更されるように、コードに次の一行を追加しました。

>```Python3:追加した行
>    # 追記
>    cv.fillPoly(frame[top:bottom+1, left:right+1], contours, color)
>```

検出されたオブジェクトを透明度のある色で塗りつぶすのではなく、透明度ゼロに塗りつぶすように変更しました。

####入力ファイル

![](https://user-images.githubusercontent.com/87643752/129008591-7cbbd34e-1e5a-46fe-b5a8-8bf667473ac5.jpg)

###変更前の出力結果

![](https://user-images.githubusercontent.com/87643752/129007043-d2655486-e8a6-4c6f-8c6f-993c8f91af6f.jpg)

__（ 実行方法 ）__

```bash:Terminal
% python3 mask_rcnn_deep_color.py --image=cars.jpg
```

###変更後の出力結果

- カレント・ディレクトリ：__*learnopencv/Mask-RCNN*直下__

![](https://user-images.githubusercontent.com/87643752/129006288-be41a233-45b0-4b1c-bcd4-a0a0f249a608.jpg)

__（ 実行方法 ）__

```bash:Terminal
% python3 mask_rcnn.py --image=cars.jpg
```


###変更後のコード全文

- カレント・ディレクトリ：__*learnopencv/Mask-RCNN*直下__

```Python3:mask_rcnn_deep_color.py
# Copyright (C) 2018-2019, BigVision LLC (LearnOpenCV.com), All Rights Reserved. 
# Author : Sunita Nayak
# Article : https://www.learnopencv.com/deep-learning-based-object-detection-and-instance-segmentation-using-mask-r-cnn-in-opencv-python-c/
# License: BSD-3-Clause-Attribution (Please read the license file.)
# This work is based on OpenCV samples code (https://opencv.org/license.html)    

import cv2 as cv
import argparse
import numpy as np
import os.path
import sys
import random

# Initialize the parameters
confThreshold = 0.5  # Confidence threshold
maskThreshold = 0.3  # Mask threshold

parser = argparse.ArgumentParser(description='Use this script to run Mask-RCNN object detection and segmentation')
parser.add_argument('--image', help='Path to image file')
parser.add_argument('--video', help='Path to video file.')
parser.add_argument("--device", default="cpu", help="Device to inference on")
args = parser.parse_args()

# Draw the predicted bounding box, colorize and show the mask on the image
def drawBox(frame, classId, conf, left, top, right, bottom, classMask):
    # Draw a bounding box.
    cv.rectangle(frame, (left, top), (right, bottom), (255, 178, 50), 3)
    
    # Print a label of class.
    label = '%.2f' % conf
    if classes:
        assert(classId < len(classes))
        label = '%s:%s' % (classes[classId], label)
    
    # Display the label at the top of the bounding box
    labelSize, baseLine = cv.getTextSize(label, cv.FONT_HERSHEY_SIMPLEX, 0.5, 1)
    top = max(top, labelSize[1])
    cv.rectangle(frame, (left, top - round(1.5*labelSize[1])), (left + round(1.5*labelSize[0]), top + baseLine), (255, 255, 255), cv.FILLED)
    cv.putText(frame, label, (left, top), cv.FONT_HERSHEY_SIMPLEX, 0.75, (0,0,0), 1)

    # Resize the mask, threshold, color and apply it on the image
    classMask = cv.resize(classMask, (right - left + 1, bottom - top + 1))
    mask = (classMask > maskThreshold)
    roi = frame[top:bottom+1, left:right+1][mask]

    # color = colors[classId%len(colors)]
    # Comment the above line and uncomment the two lines below to generate different instance colors
    colorIndex = random.randint(0, len(colors)-1)
    color = colors[colorIndex]

    frame[top:bottom+1, left:right+1][mask] = ([0.3*color[0], 0.3*color[1], 0.3*color[2]] + 0.7 * roi).astype(np.uint8)

    # Draw the contours on the image
    mask = mask.astype(np.uint8)
    contours, hierarchy = cv.findContours(mask,cv.RETR_TREE,cv.CHAIN_APPROX_SIMPLE)
    cv.drawContours(frame[top:bottom+1, left:right+1], contours, -1, color, 3, cv.LINE_8, hierarchy, 100)
    
    # 追記
    cv.fillPoly(frame[top:bottom+1, left:right+1], contours, color)
    
# For each frame, extract the bounding box and mask for each detected object
def postprocess(boxes, masks):
    # Output size of masks is NxCxHxW where
    # N - number of detected boxes
    # C - number of classes (excluding background)
    # HxW - segmentation shape
    numClasses = masks.shape[1]
    numDetections = boxes.shape[2]

    frameH = frame.shape[0]
    frameW = frame.shape[1]

    for i in range(numDetections):
        box = boxes[0, 0, i]
        mask = masks[i]
        score = box[2]
        if score > confThreshold:
            classId = int(box[1])
            
            # Extract the bounding box
            left = int(frameW * box[3])
            top = int(frameH * box[4])
            right = int(frameW * box[5])
            bottom = int(frameH * box[6])
            
            left = max(0, min(left, frameW - 1))
            top = max(0, min(top, frameH - 1))
            right = max(0, min(right, frameW - 1))
            bottom = max(0, min(bottom, frameH - 1))
            
            # Extract the mask for the object
            classMask = mask[classId]

            # Draw bounding box, colorize and show the mask on the image
            drawBox(frame, classId, score, left, top, right, bottom, classMask)


# Load names of classes
classesFile = "mscoco_labels.names";
classes = None
with open(classesFile, 'rt') as f:
   classes = f.read().rstrip('\n').split('\n')

# Give the textGraph and weight files for the model
textGraph = "./mask_rcnn_inception_v2_coco_2018_01_28.pbtxt";
modelWeights = "./mask_rcnn_inception_v2_coco_2018_01_28/frozen_inference_graph.pb";

# Load the network
net = cv.dnn.readNetFromTensorflow(modelWeights, textGraph);

if args.device == "cpu":
    net.setPreferableBackend(cv.dnn.DNN_TARGET_CPU)
    print("Using CPU device")
elif args.device == "gpu":
    net.setPreferableBackend(cv.dnn.DNN_BACKEND_CUDA)
    net.setPreferableTarget(cv.dnn.DNN_TARGET_CUDA)
    print("Using GPU device")


# Load the classes
colorsFile = "colors.txt";
with open(colorsFile, 'rt') as f:
    colorsStr = f.read().rstrip('\n').split('\n')
colors = [] #[0,0,0]
for i in range(len(colorsStr)):
    rgb = colorsStr[i].split(' ')
    color = np.array([float(rgb[0]), float(rgb[1]), float(rgb[2])])
    colors.append(color)

winName = 'Mask-RCNN Object detection and Segmentation in OpenCV'
cv.namedWindow(winName, cv.WINDOW_NORMAL)

outputFile = "mask_rcnn_out_py.avi"
if (args.image):
    # Open the image file
    if not os.path.isfile(args.image):
        print("Input image file ", args.image, " doesn't exist")
        sys.exit(1)
    cap = cv.VideoCapture(args.image)
    outputFile = args.image[:-4]+'_mask_rcnn_out_py.jpg'
elif (args.video):
    # Open the video file
    if not os.path.isfile(args.video):
        print("Input video file ", args.video, " doesn't exist")
        sys.exit(1)
    cap = cv.VideoCapture(args.video)
    outputFile = args.video[:-4]+'_mask_rcnn_out_py.avi'
else:
    # Webcam input
    cap = cv.VideoCapture(0)

# Get the video writer initialized to save the output video
if (not args.image):
    vid_writer = cv.VideoWriter(outputFile, cv.VideoWriter_fourcc('M','J','P','G'), 28, (round(cap.get(cv.CAP_PROP_FRAME_WIDTH)),round(cap.get(cv.CAP_PROP_FRAME_HEIGHT))))

while cv.waitKey(1) < 0:
    
    # Get frame from the video
    hasFrame, frame = cap.read()
    
    # Stop the program if reached end of video
    if not hasFrame:
        print("Done processing !!!")
        print("Output file is stored as ", outputFile)
        cv.waitKey(3000)
        break

    # Create a 4D blob from a frame.
    blob = cv.dnn.blobFromImage(frame, swapRB=True, crop=False)

    # Set the input to the network
    net.setInput(blob)

    # Run the forward pass to get output from the output layers
    boxes, masks = net.forward(['detection_out_final', 'detection_masks'])

    # Extract the bounding box and mask for each of the detected objects
    postprocess(boxes, masks)

    # Put efficiency information.
    t, _ = net.getPerfProfile()
    label = 'Mask-RCNN on 2.5 GHz Intel Core i7 CPU, Inference time for a frame : %0.0f ms' % abs(t * 1000.0 / cv.getTickFrequency())
    cv.putText(frame, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0))

    # Write the frame with the detection boxes
    if (args.image):
        cv.imwrite(outputFile, frame.astype(np.uint8));
    else:
        vid_writer.write(frame.astype(np.uint8))

    cv.imshow(winName, frame)

```