mirror of https://github.com/opencv/opencv.git
Open Source Computer Vision Library
https://opencv.org/
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84 lines
3.7 KiB
84 lines
3.7 KiB
import cv2 as cv |
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import argparse |
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import numpy as np |
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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) |
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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('--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: VPU' % targets) |
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args, _ = parser.parse_known_args() |
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add_preproc_args(args.zoo, parser, 'classification') |
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parser = argparse.ArgumentParser(parents=[parser], |
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description='Use this script to run classification 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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# 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 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 image classification 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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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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# Create a 4D blob from a frame. |
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inpWidth = args.width if args.width else frame.shape[1] |
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inpHeight = args.height if args.height else frame.shape[0] |
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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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out = net.forward() |
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# Get a class with a highest score. |
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out = out.flatten() |
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classId = np.argmax(out) |
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confidence = out[classId] |
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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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# Print predicted class. |
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label = '%s: %.4f' % (classes[classId] if classes else 'Class #%d' % classId, confidence) |
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cv.putText(frame, label, (0, 40), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0)) |
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cv.imshow(winName, frame)
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