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144 lines
5.1 KiB
Python
144 lines
5.1 KiB
Python
import cv2 as cv
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import argparse
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import numpy as np
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parser = argparse.ArgumentParser(description=
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'Use this script to run Mask-RCNN object detection and semantic '
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'segmentation network from TensorFlow Object Detection API.')
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parser.add_argument('--input', help='Path to input image or video file. Skip this argument to capture frames from a camera.')
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parser.add_argument('--model', required=True, help='Path to a .pb file with weights.')
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parser.add_argument('--config', required=True, help='Path to a .pxtxt file contains network configuration.')
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parser.add_argument('--classes', help='Optional path to a text file with names of classes.')
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parser.add_argument('--colors', help='Optional path to a text file with colors for an every class. '
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'An every color is represented with three values from 0 to 255 in BGR channels order.')
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parser.add_argument('--width', type=int, default=800,
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help='Preprocess input image by resizing to a specific width.')
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parser.add_argument('--height', type=int, default=800,
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help='Preprocess input image by resizing to a specific height.')
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parser.add_argument('--thr', type=float, default=0.5, help='Confidence threshold')
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args = parser.parse_args()
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np.random.seed(324)
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# Load names of classes
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classes = None
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if args.classes:
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with open(args.classes, 'rt') as f:
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classes = f.read().rstrip('\n').split('\n')
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# Load colors
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colors = None
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if args.colors:
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with open(args.colors, 'rt') as f:
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colors = [np.array(color.split(' '), np.uint8) for color in f.read().rstrip('\n').split('\n')]
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legend = None
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def showLegend(classes):
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global legend
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if not classes is None and legend is None:
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blockHeight = 30
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assert(len(classes) == len(colors))
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legend = np.zeros((blockHeight * len(colors), 200, 3), np.uint8)
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for i in range(len(classes)):
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block = legend[i * blockHeight:(i + 1) * blockHeight]
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block[:,:] = colors[i]
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cv.putText(block, classes[i], (0, blockHeight/2), cv.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255))
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cv.namedWindow('Legend', cv.WINDOW_NORMAL)
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cv.imshow('Legend', legend)
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classes = None
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def drawBox(frame, classId, conf, left, top, right, bottom):
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# Draw a bounding box.
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cv.rectangle(frame, (left, top), (right, bottom), (0, 255, 0))
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label = '%.2f' % conf
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# Print a label of class.
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if classes:
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assert(classId < len(classes))
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label = '%s: %s' % (classes[classId], label)
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labelSize, baseLine = cv.getTextSize(label, cv.FONT_HERSHEY_SIMPLEX, 0.5, 1)
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top = max(top, labelSize[1])
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cv.rectangle(frame, (left, top - labelSize[1]), (left + labelSize[0], top + baseLine), (255, 255, 255), cv.FILLED)
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cv.putText(frame, label, (left, top), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0))
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# Load a network
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net = cv.dnn.readNet(args.model, args.config)
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net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
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winName = 'Mask-RCNN in OpenCV'
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cv.namedWindow(winName, cv.WINDOW_NORMAL)
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cap = cv.VideoCapture(args.input if args.input else 0)
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legend = None
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while cv.waitKey(1) < 0:
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hasFrame, frame = cap.read()
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if not hasFrame:
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cv.waitKey()
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break
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frameH = frame.shape[0]
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frameW = frame.shape[1]
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# Create a 4D blob from a frame.
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blob = cv.dnn.blobFromImage(frame, size=(args.width, args.height), swapRB=True, crop=False)
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# Run a model
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net.setInput(blob)
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boxes, masks = net.forward(['detection_out_final', 'detection_masks'])
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numClasses = masks.shape[1]
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numDetections = boxes.shape[2]
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# Draw segmentation
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if not colors:
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# Generate colors
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colors = [np.array([0, 0, 0], np.uint8)]
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for i in range(1, numClasses + 1):
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colors.append((colors[i - 1] + np.random.randint(0, 256, [3], np.uint8)) / 2)
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del colors[0]
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boxesToDraw = []
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for i in range(numDetections):
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box = boxes[0, 0, i]
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mask = masks[i]
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score = box[2]
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if score > args.thr:
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classId = int(box[1])
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left = int(frameW * box[3])
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top = int(frameH * box[4])
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right = int(frameW * box[5])
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bottom = int(frameH * box[6])
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left = max(0, min(left, frameW - 1))
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top = max(0, min(top, frameH - 1))
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right = max(0, min(right, frameW - 1))
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bottom = max(0, min(bottom, frameH - 1))
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boxesToDraw.append([frame, classId, score, left, top, right, bottom])
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classMask = mask[classId]
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classMask = cv.resize(classMask, (right - left + 1, bottom - top + 1))
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mask = (classMask > 0.5)
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roi = frame[top:bottom+1, left:right+1][mask]
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frame[top:bottom+1, left:right+1][mask] = (0.7 * colors[classId] + 0.3 * roi).astype(np.uint8)
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for box in boxesToDraw:
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drawBox(*box)
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# Put efficiency information.
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t, _ = net.getPerfProfile()
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label = 'Inference time: %.2f ms' % (t * 1000.0 / cv.getTickFrequency())
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cv.putText(frame, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0))
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showLegend(classes)
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cv.imshow(winName, frame)
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