def changeName(pasta):
# changing files name to <number>_garamond
path1 = "/Users/fegvilela/Documents/Unb/TCC/baseDados/" + str(pasta) + "Random/letrasSeparadas/"
os.chdir(path1)
#transforma todos jpg em png
jpgFiles = glob.glob("*.jpeg")
#print(jpgFiles)
for file in jpgFiles:
#print(file[:-4])
im = Image.open(file) #abre imagem com nome do arquivo
im.save("%s.png" %file[:-4])
os.remove(file) #remove jpg
#transforma todos jpg em png
jpgFiles = glob.glob("*.jpg")
#print(jpgFiles)
for file in jpgFiles:
#print(file[:-4])
im = Image.open(file) #abre imagem com nome do arquivo
im.save("%s.png" %file[:-4])
os.remove(file) #remove jpg
#transforma todos gif em png
gifFiles = glob.glob("*.gif")
#print(jpgFiles)
for file in gifFiles:
#print(file[:-4])
im = Image.open(file) #abre imagem com nome do arquivo
im.save("%s.png" %file[:-4])
os.remove(file) #remove gif
#todos arquivos png do diretório (ou seja, todas imagens do diretório)
pngFiles = glob.glob("*.png")
#teste para verificar se os arquivos daquele diretório foram nomeados com _ (p/ posteriormente evitar a nomeação repetida, apagando arquivos)
tem = 0
if '_' in pngFiles[0]:
tem = 1
i = 1
for filename in pngFiles:
#se os arquivos já foram nomeados com _, nomeia diferente, para não sobreescrever os arquivos
if tem == 1:
os.rename(filename, (str(i)+ str(pasta) + ".png"))
else:
os.rename(filename, (str(i)+ "_" + str(pasta) + ".png"))
i = i + 1
for filename in pngFiles:
#se os arquivos já foram nomeados com _, nomeia diferente, para não sobreescrever os arquivos
if tem == 1:
os.rename(filename, (str(i)+ "_" + str(pasta) + ".png"))
i = i + 1
imagePaths2 = list(paths.list_images(path1))
print(len(imagePaths2))
filename='run_ML_classify_run_model.log',
filemode='w')
# write run parameters to log file
logging.info("log level : %s", loglevel)
logging.info("test files : %s", args.test)
logging.info("model : %s", args.model)
# -------------------------------------------
# Classify the images using the input model
# -------------------------------------------
logging.info("Classifying images ...")
# grab set of test images
imagePaths2 = sorted(paths.list_images(args.test))
#------------------------------------
# Open up the results output file
#------------------------------------
# add date & time to results filename
# get current date/time and convert to a string
now = datetime.datetime.now()
nowStr = now.strftime("%Y-%m-%d_%H-%M")
logging.info("nowStr = %s", nowStr)
# ----------------------------------------------
# Set the results output filename from the args
# ----------------------------------------------
#outFileName = r'C:\Users\lrmayer\Documents\Mayer\CatalogImages\classify_output\ML_results_' + nowStr + '.csv'
#!/usr/bin/env python
# coding: utf-8
import paths
import face_recognition
import pickle
import cv2
import os
dataset_path = './dataset'
detection_method = 'hog'
imagePaths = list(paths.list_images(dataset_path))
data = []
#imagePaths
# loop over the image paths
for (i, imagePath) in enumerate(imagePaths):
# load the input image and convert it from RGB (OpenCV ordering)
# to dlib ordering (RGB)
print("[INFO] processing image {}/{}".format(i + 1, len(imagePaths)))
print(imagePath)
image = cv2.imread(imagePath)
rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# detect the (x, y)-coordinates of the bounding boxes
# corresponding to each face in the input image
boxes = face_recognition.face_locations(rgb, model=detection_method)
# compute the facial embedding for the face
encodings = face_recognition.face_encodings(rgb, boxes)
# lrm -------------------------------------------------------------------------------
# assuming loglevel is bound to the string value obtained from the
# command line argument. Convert to upper case to allow the user to
# specify --log=DEBUG or --log=debug
loglevel = args["log"]
numeric_level = getattr(logging, loglevel.upper(), None)
if not isinstance(numeric_level, int):
raise ValueError('Invalid log level: %s' % loglevel)
logging.basicConfig(level=numeric_level, filename='example.log', filemode='w')
# lrm -------------------------------------------------------------------------------
# grab the set of training image paths and initialize the list of training labels and matrix of features
print("[INFO] extracting features...")
imagePaths = sorted(paths.list_images(args["train"]))
Labels = []
Data = []
# loop over images in training directory
for imagePath in imagePaths:
# extract the label and load image from disk
label = imagePath[imagePath.rfind("/") + 1:].split("_")[0]
logging.info("reading the image : %s", imagePath)
image = cv2.imread(imagePath)
# extract features from the image, then update the list of
# labels and features
features = describe(image)
Labels.append(label)
Data.append(features)
args = vars(ap.parse_args())
# load the configuration and grab all image paths in the dataset
conf = Conf(args["conf"])
datagen = ImageDataGenerator(rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
fill_mode='nearest')
for subdir in r_list_dirs(conf["dataset_path"]):
for fname in list_images(conf["dataset_path"], subdir):
fpath = os.path.join(conf["dataset_path"], subdir, fname)
label = os.path.basename(os.path.dirname(fpath))
out_dir = os.path.join('generated', subdir)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
img = cv2.imread(
fpath) # this is a Numpy array with shape (150, 150, 3)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
x = np.swapaxes(img, 0,
2) # this is a Numpy array with shape (3, 150, 150)
x = x.reshape(
(1, ) +
# loglevel =
numeric_level = getattr(logging, args.log.upper(), None)
if not isinstance(numeric_level, int):
raise ValueError('Invalid log level: %s' % loglevel)
logging.basicConfig(level=numeric_level,
filename='run_ML_classify_create_model.log',
filemode='w')
# write run parameters to log file
logging.info("log level : %s", args.log)
logging.info("training files : %s", args.train)
# -------------------------------------------------------------------------------
# grab the set of training image paths and initialize the list of training labels and matrix of features
imagePaths = sorted(paths.list_images(args.train))
Labels = []
Data = []
#-----------------------------
# Set up the image descriptor
#-----------------------------
desc = HSVColorTexture()
# -----------------------------------------------------------
# Loop over images in training directory to create the model
# -----------------------------------------------------------
for imagePath in imagePaths:
# get the basename of the file, use that to get the target name
label = os.path.basename(imagePath).split("_")[0]
eroded = cv2.cvtColor(eroded, cv2.COLOR_HSV2BGR)
cv2.imwrite(os.path.join(out_dir, fname), eroded)
hist = cv2.calcHist([eroded], [0], None, [256], [0, 256])
hist /= hist.max()
fig = plt.figure(figsize=(4.5, 1.8))
ax = fig.add_subplot(111)
ax.set_title("Histogram")
ax.set_xlabel("Bins")
ax.set_ylabel("# of Pixels")
# ax.set_ylim([0, 300])
ax.plot(hist)
ax.set_xlim([0, 256])
# print os.path.join(out_hist_dir, fname)
plt.savefig(os.path.join(out_hist_dir, fname), dpi=(50))
plt.close(fig)
# gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
for subdir in r_list_dirs(in_dir):
for fname in list_images(in_dir, subdir):
# print in_dir, dpath, fname
process_image(fname, os.path.join(in_dir, subdir),
os.path.join(out_dir, subdir),
os.path.join(out_hist_dir, subdir))
print 'Finished'
# region Initializing command argument parser
argsParser = argparse.ArgumentParser()
argsParser.add_argument("-i",
"--dataset",
required=True,
help="path to input directory of faces + images")
argsParser.add_argument("-e",
"--encodings",
required=True,
help="path to serialized db of facial encodings")
args = vars(argsParser.parse_args())
# endregion
# region Prepare images and data
print("[INFO] quantifying faces...")
imagePaths = list(paths.list_images(args["dataset"]))
knownEncodings = []
knownNames = []
# endregion
# region Encoding
for (i, imagePath) in enumerate(imagePaths):
print("[INFO] processing image {}/{}".format(i + 1, len(imagePaths)))
name = imagePath.split(os.path.sep)[-2]
image = cv2.imread(imagePath)
rgbImage = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
boxes = face_recognition.face_locations(rgbImage)
encodings = face_recognition.face_encodings(rgbImage, boxes)