opencv/samples/dnn/classification.py

import cv2 as cv
import argparse
import numpy as np
import sys

backends = (cv.dnn.DNN_BACKEND_DEFAULT, cv.dnn.DNN_BACKEND_HALIDE, cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_BACKEND_OPENCV)
targets = (cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_OPENCL, cv.dnn.DNN_TARGET_OPENCL_FP16, cv.dnn.DNN_TARGET_MYRIAD)

parser = argparse.ArgumentParser(description='Use this script to run classification deep learning networks using OpenCV.')
parser.add_argument('--input', help='Path to input image or video file. Skip this argument to capture frames from a camera.')
parser.add_argument('--model', required=True,
                    help='Path to a binary file of model contains trained weights. '
                         'It could be a file with extensions .caffemodel (Caffe), '
                         '.pb (TensorFlow), .t7 or .net (Torch), .weights (Darknet)')
parser.add_argument('--config',
                    help='Path to a text file of model contains network configuration. '
                         'It could be a file with extensions .prototxt (Caffe), .pbtxt (TensorFlow), .cfg (Darknet)')
parser.add_argument('--framework', choices=['caffe', 'tensorflow', 'torch', 'darknet'],
                    help='Optional name of an origin framework of the model. '
                         'Detect it automatically if it does not set.')
parser.add_argument('--classes', help='Optional path to a text file with names of classes.')
parser.add_argument('--mean', nargs='+', type=float, default=[0, 0, 0],
                    help='Preprocess input image by subtracting mean values. '
                         'Mean values should be in BGR order.')
parser.add_argument('--scale', type=float, default=1.0,
                    help='Preprocess input image by multiplying on a scale factor.')
parser.add_argument('--width', type=int, required=True,
                    help='Preprocess input image by resizing to a specific width.')
parser.add_argument('--height', type=int, required=True,
                    help='Preprocess input image by resizing to a specific height.')
parser.add_argument('--rgb', action='store_true',
                    help='Indicate that model works with RGB input images instead BGR ones.')
parser.add_argument('--backend', choices=backends, default=cv.dnn.DNN_BACKEND_DEFAULT, type=int,
                    help="Choose one of computation backends: "
                         "%d: automatically (by default), "
                         "%d: Halide language (http://halide-lang.org/), "
                         "%d: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
                         "%d: OpenCV implementation" % backends)
parser.add_argument('--target', choices=targets, default=cv.dnn.DNN_TARGET_CPU, type=int,
                    help='Choose one of target computation devices: '
                         '%d: CPU target (by default), '
                         '%d: OpenCL, '
                         '%d: OpenCL fp16 (half-float precision), '
                         '%d: VPU' % targets)
args = parser.parse_args()

# Load names of classes
classes = None
if args.classes:
    with open(args.classes, 'rt') as f:
        classes = f.read().rstrip('\n').split('\n')

# Load a network
net = cv.dnn.readNet(args.model, args.config, args.framework)
net.setPreferableBackend(args.backend)
net.setPreferableTarget(args.target)

winName = 'Deep learning image classification in OpenCV'
cv.namedWindow(winName, cv.WINDOW_NORMAL)

cap = cv.VideoCapture(args.input if args.input else 0)
while cv.waitKey(1) < 0:
    hasFrame, frame = cap.read()
    if not hasFrame:
        cv.waitKey()
        break

    # Create a 4D blob from a frame.
    blob = cv.dnn.blobFromImage(frame, args.scale, (args.width, args.height), args.mean, args.rgb, crop=False)

    # Run a model
    net.setInput(blob)
    out = net.forward()

    # Get a class with a highest score.
    out = out.flatten()
    classId = np.argmax(out)
    confidence = out[classId]

    # Put efficiency information.
    t, _ = net.getPerfProfile()
    label = 'Inference time: %.2f ms' % (t * 1000.0 / cv.getTickFrequency())
    cv.putText(frame, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0))

    # Print predicted class.
    label = '%s: %.4f' % (classes[classId] if classes else 'Class #%d' % classId, confidence)
    cv.putText(frame, label, (0, 40), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0))

    cv.imshow(winName, frame)