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https://github.com/opencv/opencv.git
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a316b11aaa
* added HDDL VPU support * changed to return True in one line if any device connected * dnn: use releaseHDDLPlugin() * dnn(hddl): fix conditions
129 lines
5.2 KiB
Python
129 lines
5.2 KiB
Python
import cv2 as cv
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import argparse
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import numpy as np
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import sys
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from common import *
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backends = (cv.dnn.DNN_BACKEND_DEFAULT, cv.dnn.DNN_BACKEND_HALIDE, cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_BACKEND_OPENCV)
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targets = (cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_OPENCL, cv.dnn.DNN_TARGET_OPENCL_FP16, cv.dnn.DNN_TARGET_MYRIAD, cv.dnn.DNN_TARGET_HDDL)
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parser = argparse.ArgumentParser(add_help=False)
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parser.add_argument('--zoo', default=os.path.join(os.path.dirname(os.path.abspath(__file__)), 'models.yml'),
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help='An optional path to file with preprocessing parameters.')
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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('--framework', choices=['caffe', 'tensorflow', 'torch', 'darknet'],
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help='Optional name of an origin framework of the model. '
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'Detect it automatically if it does not set.')
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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('--backend', choices=backends, default=cv.dnn.DNN_BACKEND_DEFAULT, type=int,
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help="Choose one of computation backends: "
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"%d: automatically (by default), "
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"%d: Halide language (http://halide-lang.org/), "
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"%d: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
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"%d: OpenCV implementation" % backends)
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parser.add_argument('--target', choices=targets, default=cv.dnn.DNN_TARGET_CPU, type=int,
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help='Choose one of target computation devices: '
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'%d: CPU target (by default), '
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'%d: OpenCL, '
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'%d: OpenCL fp16 (half-float precision), '
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'%d: NCS2 VPU, '
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'%d: HDDL VPU' % targets)
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args, _ = parser.parse_known_args()
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add_preproc_args(args.zoo, parser, 'segmentation')
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parser = argparse.ArgumentParser(parents=[parser],
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description='Use this script to run semantic segmentation deep learning networks using OpenCV.',
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formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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args = parser.parse_args()
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args.model = findFile(args.model)
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args.config = findFile(args.config)
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args.classes = findFile(args.classes)
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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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# Load a network
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net = cv.dnn.readNet(args.model, args.config, args.framework)
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net.setPreferableBackend(args.backend)
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net.setPreferableTarget(args.target)
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winName = 'Deep learning semantic segmentation 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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frameHeight = frame.shape[0]
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frameWidth = frame.shape[1]
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# Create a 4D blob from a frame.
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inpWidth = args.width if args.width else frameWidth
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inpHeight = args.height if args.height else frameHeight
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blob = cv.dnn.blobFromImage(frame, args.scale, (inpWidth, inpHeight), args.mean, args.rgb, crop=False)
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# Run a model
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net.setInput(blob)
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score = net.forward()
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numClasses = score.shape[1]
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height = score.shape[2]
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width = score.shape[3]
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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):
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colors.append((colors[i - 1] + np.random.randint(0, 256, [3], np.uint8)) / 2)
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classIds = np.argmax(score[0], axis=0)
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segm = np.stack([colors[idx] for idx in classIds.flatten()])
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segm = segm.reshape(height, width, 3)
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segm = cv.resize(segm, (frameWidth, frameHeight), interpolation=cv.INTER_NEAREST)
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frame = (0.1 * frame + 0.9 * segm).astype(np.uint8)
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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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