分离曝光，参数更新,取消反陀螺,数据增强。
This commit is contained in:
xinyang
@@ -8,8 +8,10 @@ import forward
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import cv2
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import numpy as np
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import mvsdk
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print("Finish!")
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def save_kernal(fp, val):
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print(val.shape[2], file=fp)
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print(val.shape[3], file=fp)
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@@ -49,24 +51,24 @@ def save_para(folder, paras, names, info):
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fp.close()
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STEPS = 50000
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BATCH = 30
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LEARNING_RATE_BASE = 0.0005
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STEPS = 100000
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BATCH = 40
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LEARNING_RATE_BASE = 0.0002
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LEARNING_RATE_DECAY = 0.99
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MOVING_AVERAGE_DECAY = 0.99
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def train(dataset, show_bar=False):
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x = tf.placeholder(tf.float32, [None, generate.SRC_ROWS, generate.SRC_COLS, generate.SRC_CHANNELS])
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y_= tf.placeholder(tf.float32, [None, forward.OUTPUT_NODES])
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y_ = tf.placeholder(tf.float32, [None, forward.OUTPUT_NODES])
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keep_rate = tf.placeholder(tf.float32)
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nodes, vars, vars_name = forward.forward(x, 0.01)
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nodes, vars, vars_name = forward.forward(x, 0.01, keep_rate)
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y = nodes[-1]
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ce = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1))
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# ce = tf.nn.weighted_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1), pos_weight=1)
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ce = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1))
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# ce = tf.nn.weighted_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1), pos_weight=1)
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cem = tf.reduce_mean(ce)
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loss= cem + tf.add_n(tf.get_collection("losses"))
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loss = cem + tf.add_n(tf.get_collection("losses"))
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global_step = tf.Variable(0, trainable=False)
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learning_rate = tf.train.exponential_decay(
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@@ -92,31 +94,33 @@ def train(dataset, show_bar=False):
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bar = tqdm(range(STEPS), ascii=True, dynamic_ncols=True)
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for i in bar:
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images_samples, labels_samples = dataset.sample_train_sets(BATCH)
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images_samples, labels_samples = dataset.sample_train_sets(BATCH, 0.03)
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_, loss_value, step = sess.run(
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[train_op, loss, global_step],
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feed_dict={x: images_samples, y_: labels_samples, keep_rate:0.3}
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feed_dict={x: images_samples, y_: labels_samples, keep_rate: 0.3}
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)
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if step % 500 == 0:
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test_images, test_labels = dataset.sample_test_sets(6000)
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test_acc, output = sess.run([accuracy, y], feed_dict={x: test_images, y_: test_labels, keep_rate:1.0})
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test_images, test_labels = dataset.sample_test_sets(10000)
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test_acc, output = sess.run([accuracy, y],
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feed_dict={x: test_images, y_: test_labels, keep_rate: 1.0})
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output = np.argmax(output, axis=1)
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real = np.argmax(test_labels, axis=1)
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print("=============test-set===============")
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for n in range(forward.OUTPUT_NODES):
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print("label: %d, precise: %f, recall: %f" %
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(n, np.mean(real[output==n]==n), np.mean(output[real==n]==n)))
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(n, np.mean(real[output == n] == n), np.mean(output[real == n] == n)))
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train_images, train_labels = dataset.sample_train_sets(6000)
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train_acc, output = sess.run([accuracy, y], feed_dict={x: train_images, y_: train_labels, keep_rate:1.0})
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train_images, train_labels = dataset.sample_train_sets(10000)
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train_acc, output = sess.run([accuracy, y],
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feed_dict={x: train_images, y_: train_labels, keep_rate: 1.0})
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output = np.argmax(output, axis=1)
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real = np.argmax(train_labels, axis=1)
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print("=============train-set===============")
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for n in range(forward.OUTPUT_NODES):
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print("label: %d, precise: %f, recall: %f" %
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(n, np.mean(real[output==n]==n), np.mean(output[real==n]==n)))
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(n, np.mean(real[output == n] == n), np.mean(output[real == n] == n)))
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print("\n")
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if train_acc >= 0.99 and test_acc >= 0.99:
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vars_val = sess.run(vars)
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@@ -131,8 +135,8 @@ def train(dataset, show_bar=False):
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# pred = sess.run(y, feed_dict={x: test_images, keep_rate:1.0})
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# nodes_val = sess.run(nodes, feed_dict={x:test_images})
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# return vars_val, nodes_val
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# nodes_val = sess.run(nodes, feed_dict={x:test_images})
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# return vars_val, nodes_val
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DevList = mvsdk.CameraEnumerateDevice()
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nDev = len(DevList)
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if nDev < 1:
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@@ -150,7 +154,7 @@ def train(dataset, show_bar=False):
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try:
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hCamera = mvsdk.CameraInit(DevInfo, -1, -1)
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except mvsdk.CameraException as e:
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print("CameraInit Failed({}): {}".format(e.error_code, e.message) )
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print("CameraInit Failed({}): {}".format(e.error_code, e.message))
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return
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# 获取相机特性描述
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@@ -192,24 +196,25 @@ def train(dataset, show_bar=False):
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# 把pFrameBuffer转换成opencv的图像格式以进行后续算法处理
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frame_data = (mvsdk.c_ubyte * FrameHead.uBytes).from_address(pFrameBuffer)
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frame = np.frombuffer(frame_data, dtype=np.uint8)
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frame = frame.reshape((FrameHead.iHeight, FrameHead.iWidth, 1 if FrameHead.uiMediaType == mvsdk.CAMERA_MEDIA_TYPE_MONO8 else 3) )
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frame = frame.reshape((FrameHead.iHeight, FrameHead.iWidth,
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1 if FrameHead.uiMediaType == mvsdk.CAMERA_MEDIA_TYPE_MONO8 else 3))
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frame = cv2.resize(frame, (640,480), interpolation = cv2.INTER_LINEAR)
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frame = cv2.resize(frame, (640, 480), interpolation=cv2.INTER_LINEAR)
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cv2.imshow("Press q to end", frame)
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if (cv2.waitKey(1)&0xFF) == ord(' '):
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if (cv2.waitKey(1) & 0xFF) == ord(' '):
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roi = cv2.selectROI("roi", frame)
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roi = frame[roi[1]:roi[1]+roi[3], roi[0]:roi[0]+roi[2]]
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roi = frame[roi[1]:roi[1] + roi[3], roi[0]:roi[0] + roi[2]]
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print(roi)
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cv2.imshow("box", roi)
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image = cv2.resize(roi, (48, 36))
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image = image.astype(np.float32) / 255.0
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out = sess.run(y, feed_dict={x:[image]})
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out = sess.run(y, feed_dict={x: [image]})
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print(out)
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print(np.argmax(out))
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except mvsdk.CameraException as e:
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if e.error_code != mvsdk.CAMERA_STATUS_TIME_OUT:
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print("CameraGetImageBuffer failed({}): {}".format(e.error_code, e.message) )
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print("CameraGetImageBuffer failed({}): {}".format(e.error_code, e.message))
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# 关闭相机
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mvsdk.CameraUnInit(hCamera)
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@@ -219,9 +224,9 @@ def train(dataset, show_bar=False):
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if __name__ == "__main__":
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# import os
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# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
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# os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
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# import os
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# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
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# os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
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dataset = generate.DataSet("/home/xinyang/Workspace/box_resize")
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train(dataset, show_bar=True)
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input("press enter to continue...")
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@@ -35,16 +35,16 @@ CONV1_OUTPUT_CHANNELS = 4
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CONV2_KERNAL_SIZE = 3
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# 第二层卷积输出通道数
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CONV2_OUTPUT_CHANNELS = 6
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CONV2_OUTPUT_CHANNELS = 8
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# 第三层卷积核大小
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CONV3_KERNAL_SIZE = 3
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# 第三层卷积输出通道数
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CONV3_OUTPUT_CHANNELS = 8
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CONV3_OUTPUT_CHANNELS = 16
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# 第一层全连接宽度
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FC1_OUTPUT_NODES = 50
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FC1_OUTPUT_NODES = 60
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# 第二层全连接宽度（输出标签类型数）
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FC2_OUTPUT_NODES = 15
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@@ -62,8 +62,8 @@ def forward(x, regularizer=None, keep_rate=tf.constant(1.0)):
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[CONV1_KERNAL_SIZE, CONV1_KERNAL_SIZE, int(x.shape[3]), CONV1_OUTPUT_CHANNELS]
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)
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conv1_b = get_bias([CONV1_OUTPUT_CHANNELS])
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conv1 = tf.nn.relu(tf.nn.bias_add(conv2d(x, conv1_w), conv1_b))
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pool1 = avg_pool_2x2(conv1)
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conv1 = tf.nn.relu(tf.nn.bias_add(conv2d(x, conv1_w), conv1_b))
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pool1 = avg_pool_2x2(conv1)
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print("conv1: ", conv1.shape)
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print("pool1: ", pool1.shape)
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vars.extend([conv1_w, conv1_b])
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@@ -100,7 +100,7 @@ def forward(x, regularizer=None, keep_rate=tf.constant(1.0)):
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fc1_w = get_weight([node, FC1_OUTPUT_NODES], regularizer)
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fc1_b = get_bias([FC1_OUTPUT_NODES])
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fc1 = tf.nn.relu(tf.matmul(reshaped, fc1_w) + fc1_b)
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fc1 = tf.nn.relu(tf.matmul(reshaped, fc1_w) + fc1_b)
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vars.extend([fc1_w, fc1_b])
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vars_name.extend(["fc1_w", "fc1_b"])
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nodes.extend([fc1])
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@@ -113,4 +113,3 @@ def forward(x, regularizer=None, keep_rate=tf.constant(1.0)):
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nodes.extend([fc2])
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return nodes, vars, vars_name
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@@ -4,6 +4,7 @@ import cv2
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import random
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from tqdm import tqdm
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from forward import OUTPUT_NODES
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# 原图像行数
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SRC_ROWS = 36
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@@ -40,38 +41,64 @@ class DataSet:
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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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sample = self.file2nparray("%s/%s" % (dir, file))
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label = self.id2label(i)
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if random.random() > 0.7:
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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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self.train_samples.append(sample)
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self.train_labels.append(label)
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if i == 0:
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tmp = sample.copy()
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tmp = tmp[:, :, ::-1]
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self.train_samples.append(tmp)
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self.train_labels.append(label)
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else:
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tmp = sample.copy()
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tmp = 1.2 * tmp
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tmp = np.where(tmp > 1, 1, tmp)
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tmp = np.where(tmp < 0, 0, tmp)
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self.train_samples.append(tmp)
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self.train_labels.append(label)
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tmp = sample.copy()
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tmp = 0.8 * tmp
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tmp = np.where(tmp > 1, 1, tmp)
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tmp = np.where(tmp < 0, 0, tmp)
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self.train_samples.append(tmp)
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self.train_labels.append(label)
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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.test_samples.append(sample)
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self.test_labels.append(label)
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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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def sample_train_sets(self, length, std=0.0):
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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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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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samples = np.array(samples).copy()
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samples += np.random.normal(0, std, samples.shape)
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labels = np.array(labels)
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return samples, labels
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def sample_test_sets(self, length):
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def sample_test_sets(self, length, std=0.0):
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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.test_samples)-1)
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id = random.randint(0, len(self.test_samples) - 1)
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samples.append(self.test_samples[id])
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labels.append(self.test_labels[id])
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return np.array(samples), np.array(labels)
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samples = np.array(samples).copy()
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samples += np.random.normal(0, std, samples.shape)
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labels = np.array(labels)
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return samples, labels
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def all_train_sets(self):
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def all_train_sets(self, std=0.0):
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return self.train_samples[:], self.train_labels[:]
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def all_test_sets(self):
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def all_test_sets(self, std=0.0):
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return self.test_samples[:], self.test_labels[:]
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@@ -1,5 +1,5 @@
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@@ -2,303 +2,303 @@
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1586
tools/para/conv3_w
1586
tools/para/conv3_w
File diff suppressed because it is too large
Load Diff
112
tools/para/fc1_b
112
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
||||
54396
tools/para/fc1_w
54396
tools/para/fc1_w
File diff suppressed because it is too large
Load Diff
@@ -1,16 +1,16 @@
|
||||
15
|
||||
0.6056967
|
||||
-0.13792518
|
||||
-0.21883932
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
0.07883086
|
||||
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|
||||
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|
||||
0.19498166
|
||||
-0.13619435
|
||||
0.37006247
|
||||
0.41003358
|
||||
|
||||
1652
tools/para/fc2_w
1652
tools/para/fc2_w
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user