train_advanced.py.bak

# coding: utf-8

# EXECUÇÃO
# python train_advanced.py --samples output/examples --char-classifier output/adv_char.cpickle --digit-classifier output/adv_digit.cpickle

# import the necessary packages

from .include.license_plate import LicensePlateDetector
from .include.descriptors import BlockBinaryPixelSum
from sklearn.svm import LinearSVC
from imutils import paths
import argparse
import pickle
import random
import glob
import cv2
import numpy as np
import os

# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-s", "--samples", required=True, help="path to the training samples directory")
ap.add_argument("-c", "--char-classifier", required=True, help="path to the output character classifier")
ap.add_argument("-d", "--digit-classifier", required=True, help="path to the output digit classifier")
ap.add_argument("-m", "--min-samples", type=int, default=20, help="minimum # of samples per character")
args = vars(ap.parse_args())

# initialize the descriptor
blockSizes = ((5, 5), (5, 10), (10, 5), (10, 10))
desc = BlockBinaryPixelSum(targetSize=(30, 15), blockSizes=blockSizes)

# initialize the data and labels for the alphabet and digits
alphabetData = []
digitsData = []
alphabetLabels = []
digitsLabels = []

# loop over the sample character paths
for samplePath in sorted(glob.glob(args["samples"] + "/*")):

    # extract the sample name, grab all images in the sample path, and sample them
    samplePath = samplePath.replace('\\', '/')
    sampleName = os.path.basename(samplePath)  # samplePath[samplePath.rfind("/") + 1:]
    imagePaths = list(paths.list_images(samplePath))
    imagePaths = random.sample(imagePaths, min(len(imagePaths), args["min_samples"]))

    # loop over all images in the sample path
    for imagePath in imagePaths:

        # load the character, convert it to grayscale, preprocess it, and describe it
        char = cv2.imread(imagePath)
        char = cv2.cvtColor(char, cv2.COLOR_BGR2GRAY)
        char = LicensePlateDetector.preprocessChar(char)
        features = desc.describe(char)

        # check to see if we are examining a digit
        if sampleName.isdigit():
            digitsData.append(features)
            digitsLabels.append(sampleName)

        # otherwise, we are examining an alphabetical character
        else:
            alphabetData.append(features)
            alphabetLabels.append(sampleName)

# train the character classifier
print("[INFO] fitting character model...")
charModel = LinearSVC(C=1.0, random_state=42)
charModel.fit(alphabetData, alphabetLabels)

# train the digit classifier
print("[INFO] fitting digit model...")
digitModel = LinearSVC(C=1.0, random_state=42)

# print(digitModel)

#digitsData = np.array(digitsData).reshape(-1, 1)
digitModel.fit(digitsData, digitsLabels)
# digitModel.fit(digitsData, digitsLabels)
# digitModel.fit(digitsData.reshape(-1,1), digitsLabels)

# dump the character classifier to file
print("[INFO] dumping character model...")
f = open(args["char_classifier"], "wb")
f.write(pickle.dumps(charModel))
f.close()

# dump the digit classifier to file
print("[INFO] dumping digit model...")
f = open(args["digit_classifier"], "wb")
f.write(pickle.dumps(digitModel))
f.close()