75 lines
2.2 KiB
Python
75 lines
2.2 KiB
Python
import numpy as np
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import os
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import cv2
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import random
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from forward import OUTPUT_NODES
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import sys
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import os
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from tqdm import tqdm
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# 原图像行数
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SRC_ROWS = 36
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# 原图像列数
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SRC_COLS = 48
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# 原图像通道数
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SRC_CHANNELS = 3
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class DataSet:
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def __init__(self, folder):
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self.train_samples = []
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self.train_labels = []
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self.test_samples = []
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self.test_labels = []
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self.generate_data_sets(folder)
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def file2nparray(self, name):
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try:
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image = cv2.imread(name)
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image = cv2.resize(image, (SRC_COLS, SRC_ROWS))
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image = image.astype(np.float32)
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return image / 255.0
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except:
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print(name)
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sys.exit(-1)
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def id2label(self, id):
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a = np.zeros([OUTPUT_NODES])
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a[id] = 1
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return a[:]
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def generate_data_sets(self, folder):
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sets = []
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for i in range(OUTPUT_NODES):
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dir = "%s/%d" % (folder, i)
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files = os.listdir(dir)
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for file in tqdm(files, postfix={"loading id": i}, dynamic_ncols=True):
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if file[-3:] == "jpg":
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if random.random() > 0.2:
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self.train_samples.append(self.file2nparray("%s/%s" % (dir, file)))
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self.train_labels.append(self.id2label(i))
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else:
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self.test_samples.append(self.file2nparray("%s/%s" % (dir, file)))
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self.test_labels.append(self.id2label(i))
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self.train_samples = np.array(self.train_samples)
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self.train_labels = np.array(self.train_labels)
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self.test_samples = np.array(self.test_samples)
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self.test_labels = np.array(self.test_labels)
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return sets
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def sample_train_sets(self, length):
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samples = []
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labels = []
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for i in range(length):
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id = random.randint(0, len(self.train_samples)-1)
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samples.append(self.train_samples[id])
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labels.append(self.train_labels[id])
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return np.array(samples), np.array(labels)
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def all_train_sets(self):
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return self.train_samples[:], self.train_labels[:]
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def all_test_sets(self):
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return self.test_samples[:], self.test_labels[:]
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