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分离曝光，参数更新,取消反陀螺,数据增强。

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xinyang
2019-08-06 11:54:48 +08:00
parent 7337f10123
commit 9392c201db
20 changed files with 41259 additions and 17856 deletions
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51
tools/TrainCNN/generate.py
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@@ -4,6 +4,7 @@ import cv2
import random
from tqdm import tqdm
from forward import OUTPUT_NODES
# 原图像行数
SRC_ROWS = 36
@@ -40,38 +41,64 @@ class DataSet:
files = os.listdir(dir)
for file in tqdm(files, postfix={"loading id": i}, dynamic_ncols=True):
if file[-3:] == "jpg":
sample = self.file2nparray("%s/%s" % (dir, file))
label = self.id2label(i)
if random.random() > 0.7:
self.train_samples.append(self.file2nparray("%s/%s" % (dir, file)))
self.train_labels.append(self.id2label(i))
self.train_samples.append(sample)
self.train_labels.append(label)
if i == 0:
tmp = sample.copy()
tmp = tmp[:, :, ::-1]
self.train_samples.append(tmp)
self.train_labels.append(label)
else:
tmp = sample.copy()
tmp = 1.2 * tmp
tmp = np.where(tmp > 1, 1, tmp)
tmp = np.where(tmp < 0, 0, tmp)
self.train_samples.append(tmp)
self.train_labels.append(label)
tmp = sample.copy()
tmp = 0.8 * tmp
tmp = np.where(tmp > 1, 1, tmp)
tmp = np.where(tmp < 0, 0, tmp)
self.train_samples.append(tmp)
self.train_labels.append(label)
else:
self.test_samples.append(self.file2nparray("%s/%s" % (dir, file)))
self.test_labels.append(self.id2label(i))
self.test_samples.append(sample)
self.test_labels.append(label)
self.train_samples = np.array(self.train_samples)
self.train_labels = np.array(self.train_labels)
self.test_samples = np.array(self.test_samples)
self.test_labels = np.array(self.test_labels)
return sets
def sample_train_sets(self, length):
def sample_train_sets(self, length, std=0.0):
samples = []
labels = []
for i in range(length):
id = random.randint(0, len(self.train_samples)-1)
id = random.randint(0, len(self.train_samples) - 1)
samples.append(self.train_samples[id])
labels.append(self.train_labels[id])
return np.array(samples), np.array(labels)
samples = np.array(samples).copy()
samples += np.random.normal(0, std, samples.shape)
labels = np.array(labels)
return samples, labels
def sample_test_sets(self, length):
def sample_test_sets(self, length, std=0.0):
samples = []
labels = []
for i in range(length):
id = random.randint(0, len(self.test_samples)-1)
id = random.randint(0, len(self.test_samples) - 1)
samples.append(self.test_samples[id])
labels.append(self.test_labels[id])
return np.array(samples), np.array(labels)
samples = np.array(samples).copy()
samples += np.random.normal(0, std, samples.shape)
labels = np.array(labels)
return samples, labels
def all_train_sets(self):
def all_train_sets(self, std=0.0):
return self.train_samples[:], self.train_labels[:]
def all_test_sets(self):
def all_test_sets(self, std=0.0):
return self.test_samples[:], self.test_labels[:]