maskrcnn opencv inference demo

maskrcnn_cv_demo.py

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

# Initialize the parameters
confThreshold = 0.3  # Confidence threshold
maskThreshold = 0.5  # 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.')
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.
    needle_box=frame[top:bottom,left:right]
    cv.rectangle(frame, (left, top), (right, bottom), (255, 178, 50), 3)
    print(left,top,left,right)
    # Print a label of class.
    label = '%.2f' % conf
    if classes:
        assert(classId < len(classes))
        label = '%s:%s' % (classes[classId], label)
        print("label",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]
    cv.imwrite("roi.jpg",frame[top:bottom+1, left:right+1][mask])
    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)
    rectimg = cv.minAreaRect(contours[0])
    box = cv.boxPoints(rectimg)
    cv.drawContours(frame[top:bottom+1, left:right+1], contours, -1, color, 3, cv.LINE_8, hierarchy, 100)

# 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]

    print("numClasses %d" % numClasses)
    print("numDetections %d" % numDetections )
    for i in range(numDetections):
        box = boxes[0, 0, i]

        mask = masks[i]
        score = box[2]
        if score > confThreshold:
            classId = int(box[1])
            print("classId %d" % classId)
            print("score %d" % score)
            # 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 = "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";
textGraph = "./inference_680/graph.pbtxt";
#textGraph="./mask_rcnn_inception_v2_coco_2018_11_28/graph.pbtxt"
#modelWeights = "./mask_rcnn_inception_v2_coco_2018_11_28/saved_model/saved_model.pb";
#modelWeights = "./mask_rcnn_inception_v2_coco_2018_01_28/frozen_inference_graph.pb";
modelWeights ="./inference_680/frozen_inference_graph.pb"
# Load the network
net = cv.dnn.readNetFromTensorflow(modelWeights, textGraph);
net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU)

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

    # Put efficiency information.
    t, _ = net.getPerfProfile()
    cv.putText(frame,'box', (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)