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使用旧版候选区寻找方式。

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xinyang
2019-05-02 20:19:17 +08:00
parent ee83a373d3
commit 2f20367677
20 changed files with 12502 additions and 12395 deletions
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12
tools/TrainCNN/backward.py
View File

@@ -53,7 +53,7 @@ def save_para(folder, paras):
save_bias(fp, paras[7])
STEPS = 20000
STEPS = 10000
BATCH = 30
LEARNING_RATE_BASE = 0.01
LEARNING_RATE_DECAY = 0.99
@@ -61,8 +61,6 @@ MOVING_AVERAGE_DECAY = 0.99
def train(dataset, show_bar=False):
test_images, test_labels = dataset.all_test_sets()
x = tf.placeholder(tf.float32, [None, generate.SRC_ROWS, generate.SRC_COLS, generate.SRC_CHANNELS])
y_= tf.placeholder(tf.float32, [None, forward.OUTPUT_NODES])
nodes, vars = forward.forward(x, 0.001)
@@ -104,7 +102,8 @@ def train(dataset, show_bar=False):
if i % 100 == 0:
if i % 1000 == 0:
acc = sess.run(accuracy, feed_dict={x: test_images, y_: test_labels})
test_samples, test_labels = dataset.sample_train_sets(5000)
acc = sess.run(accuracy, feed_dict={x: test_samples, y_: test_labels})
bar.set_postfix({"loss": loss_value, "acc": acc})
@@ -151,8 +150,8 @@ def train(dataset, show_bar=False):
vars_val = sess.run(vars)
save_para("/home/xinyang/Desktop/AutoAim/tools/para", vars_val)
nodes_val = sess.run(nodes, feed_dict={x:test_images})
return vars_val, nodes_val
nodes_val = sess.run(nodes, feed_dict={x:test_samples})
return vars_val, nodes_val, test_samples
if __name__ == "__main__":
@@ -160,3 +159,4 @@ if __name__ == "__main__":
dataset = generate.DataSet("/home/xinyang/Desktop/DataSets/box")
print("Finish!")
train(dataset, show_bar=True)
input("Press any key to continue...")

4
tools/TrainCNN/forward.py
View File

@@ -64,8 +64,8 @@ def forward(x, regularizer=None):
[CONV2_KERNAL_SIZE, CONV2_KERNAL_SIZE, CONV1_OUTPUT_CHANNELS, CONV2_OUTPUT_CHANNELS]
)
conv2_b = get_bias([CONV2_OUTPUT_CHANNELS])
conv2 = tf.nn.relu(tf.nn.bias_add(conv2d(pool1, conv2_w), conv2_b))
pool2 = avg_pool_2x2(conv2)
conv2 = tf.nn.relu(tf.nn.bias_add(conv2d(pool1, conv2_w), conv2_b))
pool2 = avg_pool_2x2(conv2)
vars.extend([conv2_w, conv2_b])
nodes.extend([conv2, pool2])

57
tools/TrainCNN/generate.py
View File

@@ -24,34 +24,40 @@ 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 id2label(self, id):
a = np.zeros([OUTPUT_NODES])
a[id] = 1
return a[:]
def generate_data_sets(self, folder):
def id2label(id):
a = np.zeros([OUTPUT_NODES])
a[id] = 1
return a[:]
def file2nparray(name):
try:
image = cv2.imread(name)
if image.shape[0] < 15:
return None
elif image.shape[1] < 10:
return None
image = cv2.resize(image, (SRC_COLS, SRC_ROWS))
image = image.astype(np.float32)
return image / 255.0, id2label(int(name.split("/")[-2]))
except TypeError:
print(name)
sys.exit(-1)
sets = []
for i in range(OUTPUT_NODES):
dir = "%s/%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))
x = file2nparray("%s/%s" % (dir, file))
if x is not None:
if random.random() > 0.2:
self.train_samples.append(x[0])
self.train_labels.append(x[1])
else:
self.test_samples.append(x[0])
self.test_labels.append(x[1])
self.train_samples = np.array(self.train_samples)
self.train_labels = np.array(self.train_labels)
self.test_samples = np.array(self.test_samples)
@@ -67,6 +73,15 @@ class DataSet:
labels.append(self.train_labels[id])
return np.array(samples), np.array(labels)
def sample_test_sets(self, length):
samples = []
labels = []
for i in range(length):
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)
def all_train_sets(self):
return self.train_samples[:], self.train_labels[:]