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修改了摄像头读取方式

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xinyang
2019-04-27 16:16:53 +08:00
parent 9cfd26cc23
commit 4e47b38d7d
15 changed files with 272 additions and 255 deletions
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60
tools/TrainCNN/generate.py
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@@ -3,47 +3,63 @@ import os
import cv2
import random
from forward import OUTPUT_NODES
# 原图像行数
SRC_ROWS = 36
# 原图像列数
SRC_COLS = 48
# 原图像通道数
SRC_CHANNELS = 3
class DataSet:
def __init__(self, folder):
self.train_sets = []
self.test_sets = []
self.train_samples = []
self.train_labels = []
self.test_samples = []
self.test_labels = []
self.generate_data_sets(folder)
def file2nparray(self, name):
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])
a[id] = 1
return a[:]
def generate_data_sets(self, folder):
def file2nparray(name):
image = cv2.imread(name)
return image[:, :, 0]
def id2label(id):
a = np.zeros([OUTPUT_NODES, 1])
a[id] = 1
return a[:]
sets = []
for i in range(OUTPUT_NODES):
dir = "%s/%d" % (folder, i)
files = os.listdir(dir)
for file in files:
sets.append([file2nparray("%s/%s" % (dir, file)), id2label(i)])
sets = np.array(sets)
np.random.shuffle(sets)
length = len(sets)
self.train_sets = sets[:length*3//4]
self.test_sets = sets[length*3//4:]
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))
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):
samples = []
labels = []
for i in range(length):
id = random.randint(0, length-1)
samples.append(self.train_sets[id][0])
labels.append(self.train_sets[id][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)
def all_train_sets(self):
return self.train_sets[:, 0, :, :], self.train_sets[:, 1, :, :]
return self.train_samples[:], self.train_labels[:]
def all_test_sets(self):
return self.test_sets[:, 0, :, :], self.test_sets[:, 1, :, :]
return self.test_samples[:], self.test_labels[:]