Mask R-CNN can automatically predict both the bounding box and the pixel-wise segmentation mask of each object in an input image. The downside is that masks produced by Mask R-CNN aren’t always “clean” — there is typically a bit of background that “bleeds” into the foreground segmentation
recognized a horse, mask around it
(venv) C:\Users\zchen\PycharmProjects\opencv>python mask_rcnn.py --mask-rcnn mask-rcnn-coco --image assets/mask_rcnn_image.jpg
[INFO] loading Mask R-CNN from disk...
[INFO] showing output for 'horse'...
[INFO] showing output for 'person'...
[INFO] showing output for 'dog'...
[INFO] showing output for 'person'...
[INFO] showing output for 'truck'...
#mask_rcnn.py
import numpy as np
import argparse
import imutils
import cv2
import os
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-m", "--mask-rcnn", required=True,
help="base path to mask-rcnn directory")
ap.add_argument("-i", "--image", required=True,
help="path to input image")
ap.add_argument("-c", "--confidence", type=float, default=0.5,
help="minimum probability to filter weak detections")
ap.add_argument("-t", "--threshold", type=float, default=0.3,
help="minimum threshold for pixel-wise mask segmentation")
ap.add_argument("-u", "--use-gpu", type=bool, default=0,
help="boolean indicating if CUDA GPU should be used")
ap.add_argument("-e", "--iter", type=int, default=10,
help="# of GrabCut iterations (larger value => slower runtime)")
args = vars(ap.parse_args())
# load the COCO class labels our Mask R-CNN was trained on
labelsPath = os.path.sep.join([args["mask_rcnn"],
"object_detection_classes_coco.txt"])
LABELS = open(labelsPath).read().strip().split("\n")
# initialize a list of colors to represent each possible class label
np.random.seed(42)
COLORS = np.random.randint(0, 255, size=(len(LABELS), 3),
dtype="uint8")
# derive the paths to the Mask R-CNN weights and model configuration
weightsPath = os.path.sep.join([args["mask_rcnn"],
"frozen_inference_graph.pb"])
configPath = os.path.sep.join([args["mask_rcnn"],
"mask_rcnn_inception_v2_coco_2018_01_28.pbtxt"])
# load our Mask R-CNN trained on the COCO dataset (90 classes)
# from disk
print("[INFO] loading Mask R-CNN from disk...")
net = cv2.dnn.readNetFromTensorflow(weightsPath, configPath)
# check if we are going to use GPU
if args["use_gpu"]:
# set CUDA as the preferable backend and target
print("[INFO] setting preferable backend and target to CUDA...")
net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)
net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)
# load our input image from disk and display it to our screen
image = cv2.imread(args["image"])
image = imutils.resize(image, width=600)
cv2.imshow("Input", image)
# construct a blob from the input image and then perform a
# forward pass of the Mask R-CNN, giving us (1) the bounding box
# coordinates of the objects in the image along with (2) the
# pixel-wise segmentation for each specific object
blob = cv2.dnn.blobFromImage(image, swapRB=True, crop=False)
net.setInput(blob)
(boxes, masks) = net.forward(["detection_out_final",
"detection_masks"])
# loop over the number of detected objects
for i in range(0, boxes.shape[2]):
# extract the class ID of the detection along with the
# confidence (i.e., probability) associated with the
# prediction
classID = int(boxes[0, 0, i, 1])
confidence = boxes[0, 0, i, 2]
# filter out weak predictions by ensuring the detected
# probability is greater than the minimum probability
if confidence > args["confidence"]:
# show the class label
print("[INFO] showing output for '{}'...".format(
LABELS[classID]))
# scale the bounding box coordinates back relative to the
# size of the image and then compute the width and the
# height of the bounding box
(H, W) = image.shape[:2]
box = boxes[0, 0, i, 3:7] * np.array([W, H, W, H])
(startX, startY, endX, endY) = box.astype("int")
boxW = endX - startX
boxH = endY - startY
# extract the pixel-wise segmentation for the object, resize
# the mask such that it's the same dimensions as the bounding
# box, and then finally threshold to create a *binary* mask
mask = masks[i, classID]
mask = cv2.resize(mask, (boxW, boxH),
interpolation=cv2.INTER_CUBIC)
mask = (mask > args["threshold"]).astype("uint8") * 255
# allocate a memory for our output Mask R-CNN mask and store
# the predicted Mask R-CNN mask in the GrabCut mask
rcnnMask = np.zeros(image.shape[:2], dtype="uint8")
rcnnMask[startY:endY, startX:endX] = mask
# apply a bitwise AND to the input image to show the output
# of applying the Mask R-CNN mask to the image
rcnnOutput = cv2.bitwise_and(image, image, mask=rcnnMask)
# show the output of the Mask R-CNN and bitwise AND operation
cv2.imshow("R-CNN Mask", rcnnMask)
cv2.imshow("R-CNN Output", rcnnOutput)
cv2.waitKey(0)
reference:
mask-rcnn-coco directory
frozen_inference_graph.pb (trained model)
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