from model import generate_classifier
from keras.preprocessing import image
import numpy as np
import os
import cv2
import json
from filter_with_model import crop_double_check
input_dirs = []
save_dirs = ["col_pad_bo", "col_pad_bu", "col_pad_pu", "col_pad_no"]
crop_dir = "col_pad_crop"
cutoff = 0.8
m = generate_classifier()
m.load_weights("weights/lastClassifier2.h5")
os.makedirs(crop_dir, exist_ok=True)
for save_dir in save_dirs:
if not os.path.exists(save_dir):
print("creating_save_dir")
os.makedirs(save_dir)
def classify(imgname, cutoff=0.9):
try:
if imgname[-3:] != "jpg":
return None, -1
try:
img = image.load_img(imgname)
except:
return None, -1
img = image.img_to_array(img)
DATA_FOLDER = "data2" #C:\programmering\differentiate"
BATCH_SIZE = 128
IMAGE_SIZE = 128
LEARNING_RATE = 0.0005
LOAD_WEIGHTS = False
EPOCHS = 500
classes = len(os.listdir(DATA_FOLDER))
gen = generator.generator(batch_size=BATCH_SIZE, data_folder=DATA_FOLDER)
folders = next(gen)
with open("weights/categories.txt", "w") as f:
f.write(str(folders))
total = next(gen)
modl = model.generate_classifier(IMAGE_SIZE, classes, LEARNING_RATE)
if LOAD_WEIGHTS:
modl.load_weights("weights/accurate.h5")
print(modl.summary())
check1 = ModelCheckpoint(filepath="weights/last.h5",
save_best_only=False,
save_weights_only=True,
verbose=1)
check2 = ModelCheckpoint(filepath="weights/accurate.h5",
monitor="loss",
save_best_only=True,
save_weights_only=True,
verbose=1)
modl.fit(gen,
#save.save("just_bordered/"+ str(index)+ "-" + str(int(use1)) + ".jpg")
batch_img.append(img)
label = np.zeros(classes)
label[current_class] = 1
batch_labels.append(label)
#print(x1,y1,x2,y2)
if (len(batch_img) == batch_size):
batch_img = np.array(batch_img)
#print(batch_img.shape)
batch_labels = np.array(batch_labels)
#print(batch_labels.shape)
yield (batch_img, batch_labels)
batch_img = []
batch_labels = []
current_class += 1
if current_class == classes:
current_class = 0
model = generate_classifier()
if load_weights:
model.load_weights("weights/lastClassifier3.h5")
print(model.summary())
check1 = ModelCheckpoint(filepath="weights/lastClassifier3.h5",
save_best_only=False,
save_weights_only=True,
verbose=1)
model.fit_generator(generator=generator(),
steps_per_epoch=sum // batch_size,
epochs=100,
callbacks=[check1])
import cv2
import numpy as np
from tensorflow.keras.callbacks import ModelCheckpoint
data_folder = "D:\data\\filer\\reddit_sub_pasta"
batch_size = 1
image_size = 128
learning_rate = 0.005
classes = len(os.listdir("data"))
gen = test_generator.generator(batch_size=batch_size,
data_folder=data_folder,
total_classes=classes)
foldesr = next(gen)
total = next(gen)
modl = model.generate_classifier(image_size, classes, learning_rate)
modl.load_weights("weights/accurate.h5")
fold = os.listdir("data")
with open("weights/categories.txt", "r") as f:
folders = f.read()[1:-1]
folders = folders.split(",")
folders = [folder.strip()[1:-1] for folder in folders]
output = "result\\session"
os.makedirs(output, exist_ok=True)
for folds in folders:
os.makedirs(os.path.join(output, folds), exist_ok=True)