def makeHandsHuman():
files = getFiles()
total_file = len(files)
Console.info("Image total:", total_file)
num_processes = multiprocessing.cpu_count()
if platform.system() == "Linux" and num_processes > 1:
processes = []
lot_size = int(total_file / num_processes)
for x in range(1, num_processes + 1):
if x < num_processes:
lot_img = files[(x - 1) * lot_size:((x - 1) * lot_size) +
lot_size]
else:
lot_img = files[(x - 1) * lot_size:]
processes.append(Process(target=mpStart, args=(lot_img, output)))
if len(processes) > 0:
Console.info("Get histogram of the images...")
for p in processes:
p.start()
result = []
for x in range(num_processes):
result.append(output.get(True))
for p in processes:
p.join()
updateProgress(1, total_file, total_file, "")
else:
Console.info("No podemos dividir la cargan en distintos procesadores")
exit(0)
def loadDataSet(files=[]):
total_file = len(files)
# defined path
path = os.path.join(__location__, "dataset")
path_original = os.path.join(path, "original")
path_hands = os.path.join(path, "hands")
X_img = []
y_img = []
for i in range(total_file):
img_file = files[i]
# Update the progress bar
progress = float(i / total_file), (i + 1)
updateProgress(progress[0], progress[1], total_file, img_file)
# Get image's path
x_path = os.path.join(path_original, img_file)
y_path = os.path.join(path_hands, img_file)
x_img = cv2.imread(x_path) # Read a image
y_img = cv2.imread(y_path) # Read a image
for x_img, y_img in dataAugmentation(x_img, y_img):
X_img.append(processeImg(x_img))
y_img.append(processeImg(y_img))
return X_img, y_img
def processeImg(files, y_lower_upper):
total_file = len(files)
Console.log("Process", total_file, "images")
x_files = []
for i in range(total_file):
min_color = int(y_lower_upper[0][i])
max_color = int(y_lower_upper[1][i])
min_color = min_color if min_color > 0 else 0
max_color = max_color if max_color < 255 else 255
x_files.append((files[i], min_color, max_color))
# Usado en caso de usar multiples core
output = multiprocessing.Queue()
num_processes = multiprocessing.cpu_count()
if platform.system() == "Linux" and num_processes > 1:
processes = []
lot_size = int(total_file / num_processes)
for x in range(1, num_processes + 1):
if x < num_processes:
lote = x_files[(x - 1) * lot_size: ((x - 1) * lot_size) + lot_size]
else:
lote = x_files[(x - 1) * lot_size:]
processes.append(Process(target=mpProcessImg, args=(lote, output)))
if len(processes) > 0:
Console.info("Fix colors of the images...")
for p in processes:
p.start()
result = []
for x in range(num_processes):
result.append(output.get(True))
for p in processes:
p.join()
X_values = []
for x in result:
X_values = X_values + x
updateProgress(1, total_file, total_file, "")
Console.log("Image processed:", len(X_values))
else:
Console.info("We can not divide the load into different processors")
# X_values = mpGetHistogramFormFiles(files)
exit(0)
return X_values
def progressImg(files=[]):
global img_file
total_file = len(files)
for i in range(total_file):
(img_file, lower, upper) = files[i]
# Update the progress bar
progress = float(i / total_file), (i + 1)
updateProgress(progress[0], progress[1], total_file, img_file)
# Get image's path
img_path = os.path.join(train_dir, img_file)
# Read a image
img = cv2.imread(img_path, 0)
img = histogramsLevelFix(img, lower, upper)
writeImage("hands", img)
def progressFiles(path_input, files, hands_valid):
path = os.path.join(__location__)
for p in path_input:
path = os.path.join(path, p)
total_file = len(files)
Console.info("Image total:", total_file)
num_processes = multiprocessing.cpu_count()
if platform.system() == "Linux" and num_processes > 1:
processes = []
lot_size = int(total_file / num_processes)
for x in range(1, num_processes + 1):
if x < num_processes:
lot_img = files[(x - 1) * lot_size:((x - 1) * lot_size) +
lot_size]
else:
lot_img = files[(x - 1) * lot_size:]
processes.append(
Process(target=mpStart,
args=(path, lot_img, hands_valid, output, x)))
if len(processes) > 0:
Console.info("Get histogram of the images...")
for p in processes:
p.start()
result = []
for x in range(num_processes):
result.append(output.get(True))
for p in processes:
p.join()
X_train = []
y_train = []
for mp_X_train, mp_y_train in result:
X_train = X_train + mp_X_train
y_train = y_train + mp_y_train
updateProgress(1, total_file, total_file, "")
return X_train, y_train
else:
Console.info("No podemos dividir la cargan en distintos procesadores")
exit(0)
def mpProcessImg(files, output):
global img_file
rta = []
total_file = len(files)
for i in range(total_file):
(img_file, lower, upper) = files[i]
# Update the progress bar
progress = float(i / total_file), (i + 1)
updateProgress(progress[0], progress[1], total_file, img_file)
# Get image's path
img_path = os.path.join(train_dir, img_file)
# Read a image
img = cv2.imread(img_path, 0)
img = histogramsLevelFix(img, lower, upper)
rta.append((img_file, img, getHistogram(img)))
output.put(rta)
def mpGetHistogramFormFiles(files, output):
global img_file
total_file = len(files)
rta = []
for i in range(total_file):
img_file = files[i]
# Update the progress bar
progress = float(i / total_file), (i + 1)
updateProgress(progress[0], progress[1], total_file, img_file)
# Get image's path
img_path = os.path.join(train_dir, img_file)
# Read a image
img = cv2.imread(img_path, 0)
# Get histogram of hands
rta.append((img_file, getHistogram(img)))
output.put(rta)
def getHistogramFormFiles(files=[]):
total_file = len(files)
Console.log("Process", total_file, "images")
# Usado en caso de usar multiples core
output = multiprocessing.Queue()
num_processes = multiprocessing.cpu_count()
if platform.system() == "Linux" and num_processes > 1:
processes = []
lot_size = int(total_file / num_processes)
for x in range(1, num_processes + 1):
if x < num_processes:
lote = files[(x - 1) * lot_size: ((x - 1) * lot_size) + lot_size]
else:
lote = files[(x - 1) * lot_size:]
processes.append(Process(target=mpGetHistogramFormFiles, args=(lote, output)))
if len(processes) > 0:
Console.info("Get histogram of the images...")
for p in processes:
p.start()
result = []
for x in range(num_processes):
result.append(output.get(True))
for p in processes:
p.join()
X_values = []
for x in result:
X_values = X_values + x
updateProgress(1, total_file, total_file, "")
Console.log("Image processed:", len(X_values))
else:
Console.info("We can not divide the load into different processors")
# X_values = mpGetHistogramFormFiles(files)
exit(0)
return X_values
def loadDataSet(path, files=[], hands_valid=1):
X_train = []
y_train = []
total_file = len(files)
for i in range(total_file):
img_file = files[i]
# Update the progress bar
progress = float(i / total_file), (i + 1)
updateProgress(progress[0], progress[1], total_file, img_file)
# Get image's path
img_path = os.path.join(path, img_file)
# Read a image
img = cv2.imread(img_path, 0)
X_train.append(getHistogram(img))
y_train.append(hands_valid)
return X_train, y_train