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更新CNN训练代码。

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xinyang
2019-07-02 20:56:46 +08:00
parent cb6cf4b2fc
commit 14d1544113
3 changed files with 20 additions and 20 deletions
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32
tools/TrainCNN/generate.py
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@@ -24,15 +24,11 @@ class DataSet:
self.test_labels = []
self.generate_data_sets(folder)
def file2nparray(self, name):
try:
image = cv2.imread(name)
image = cv2.resize(image, (SRC_COLS, SRC_ROWS))
image = image.astype(np.float32)
return image / 255.0
except:
print(name)
sys.exit(-1)
def file2nparray(self, name, random=False):
image = cv2.imread(name)
image = cv2.resize(image, (SRC_COLS, SRC_ROWS))
image = image.astype(np.float32)
return image / 255.0
def id2label(self, id):
a = np.zeros([OUTPUT_NODES])
@@ -42,16 +38,20 @@ class DataSet:
def generate_data_sets(self, folder):
sets = []
for i in range(OUTPUT_NODES):
dir = "%s/%d" % (folder, i)
dir = "%s/id%d" % (folder, i)
files = os.listdir(dir)
for file in tqdm(files, postfix={"loading id": i}, dynamic_ncols=True):
if file[-3:] == "jpg":
if random.random() > 0.2:
self.train_samples.append(self.file2nparray("%s/%s" % (dir, file)))
self.train_labels.append(self.id2label(i))
else:
self.test_samples.append(self.file2nparray("%s/%s" % (dir, file)))
self.test_labels.append(self.id2label(i))
try:
if random.random() > 0.2:
self.train_samples.append(self.file2nparray("%s/%s" % (dir, file)))
self.train_labels.append(self.id2label(i))
else:
self.test_samples.append(self.file2nparray("%s/%s" % (dir, file)))
self.test_labels.append(self.id2label(i))
except:
print("%s/%s" % (dir, file))
continue
self.train_samples = np.array(self.train_samples)
self.train_labels = np.array(self.train_labels)
self.test_samples = np.array(self.test_samples)