233 lines
9.3 KiB
Python
Executable File
233 lines
9.3 KiB
Python
Executable File
#!/usr/bin/python3
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print("Preparing...")
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import tensorflow as tf
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import os
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from tqdm import tqdm
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import generate
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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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print(val.shape[1], file=fp)
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print(val.shape[0], file=fp)
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for in_channel in range(val.shape[2]):
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for out_channel in range(val.shape[3]):
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for row in range(val.shape[0]):
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for col in range(val.shape[1]):
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print(val[row][col][in_channel][out_channel], file=fp)
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def save_weight_mat(fp, val):
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print(val.shape[0], file=fp)
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print(val.shape[1], file=fp)
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for row in range(val.shape[0]):
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for col in range(val.shape[1]):
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print(val[row][col], file=fp)
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def save_bias(fp, val):
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print(val.shape[0], file=fp)
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for i in range(val.shape[0]):
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print(val[i], file=fp)
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def save_para(folder, paras, names, info):
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os.system("mkdir %s/%s" % (folder, info))
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for para, name in zip(paras, names):
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fp = open("%s/%s/%s" % (folder, info, name), "w")
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if name[-1:] == "b":
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save_bias(fp, para)
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elif name[:2] == "fc":
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save_weight_mat(fp, para)
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elif name[:4] == "conv":
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save_kernal(fp, para)
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fp.close()
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STEPS = 100000
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BATCH = 40
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LEARNING_RATE_BASE = 0.0003
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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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keep_rate = tf.placeholder(tf.float32)
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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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cem = tf.reduce_mean(ce)
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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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LEARNING_RATE_BASE,
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global_step,
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len(dataset.train_samples) / BATCH,
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LEARNING_RATE_DECAY,
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staircase=False)
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train_step = tf.train.AdamOptimizer(learning_rate).minimize(loss, global_step=global_step)
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ema = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step)
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ema_op = ema.apply(tf.trainable_variables())
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with tf.control_dependencies([train_step, ema_op]):
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train_op = tf.no_op(name='train')
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correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
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accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
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config = tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True))
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with tf.Session(config=config) as sess:
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init_op = tf.global_variables_initializer()
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sess.run(init_op)
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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, 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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)
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if step % 500 == 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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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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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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save_para(
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"model",
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vars_val,
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vars_name,
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"steps:%d-train_acc:%f-test_acc:%f" % (step, train_acc, test_acc)
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)
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bar.set_postfix({"loss": loss_value, "train_acc": train_acc, "test_acc": test_acc})
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# print("save done!")
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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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DevList = mvsdk.CameraEnumerateDevice()
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nDev = len(DevList)
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if nDev < 1:
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print("No camera was found!")
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return
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for i, DevInfo in enumerate(DevList):
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print("{}: {} {}".format(i, DevInfo.GetFriendlyName(), DevInfo.GetPortType()))
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i = 0 if nDev == 1 else int(input("Select camera: "))
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DevInfo = DevList[i]
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print(DevInfo)
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# 打开相机
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hCamera = 0
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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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return
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# 获取相机特性描述
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cap = mvsdk.CameraGetCapability(hCamera)
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# 判断是黑白相机还是彩色相机
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monoCamera = (cap.sIspCapacity.bMonoSensor != 0)
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# 黑白相机让ISP直接输出MONO数据，而不是扩展成R=G=B的24位灰度
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if monoCamera:
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mvsdk.CameraSetIspOutFormat(hCamera, mvsdk.CAMERA_MEDIA_TYPE_MONO8)
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else:
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mvsdk.CameraSetIspOutFormat(hCamera, mvsdk.CAMERA_MEDIA_TYPE_BGR8)
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# 相机模式切换成连续采集
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mvsdk.CameraSetTriggerMode(hCamera, 0)
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# 手动曝光，曝光时间30ms
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mvsdk.CameraSetAeState(hCamera, 0)
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mvsdk.CameraSetExposureTime(hCamera, 30 * 1000)
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# 让SDK内部取图线程开始工作
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mvsdk.CameraPlay(hCamera)
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# 计算RGB buffer所需的大小，这里直接按照相机的最大分辨率来分配
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FrameBufferSize = cap.sResolutionRange.iWidthMax * cap.sResolutionRange.iHeightMax * (1 if monoCamera else 3)
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# 分配RGB buffer，用来存放ISP输出的图像
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# 备注：从相机传输到PC端的是RAW数据，在PC端通过软件ISP转为RGB数据（如果是黑白相机就不需要转换格式，但是ISP还有其它处理，所以也需要分配这个buffer）
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pFrameBuffer = mvsdk.CameraAlignMalloc(FrameBufferSize, 16)
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while (cv2.waitKey(1) & 0xFF) != ord('q'):
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# 从相机取一帧图片
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try:
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pRawData, FrameHead = mvsdk.CameraGetImageBuffer(hCamera, 200)
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mvsdk.CameraImageProcess(hCamera, pRawData, pFrameBuffer, FrameHead)
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mvsdk.CameraReleaseImageBuffer(hCamera, pRawData)
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# 此时图片已经存储在pFrameBuffer中，对于彩色相机pFrameBuffer=RGB数据，黑白相机pFrameBuffer=8位灰度数据
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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,
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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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cv2.imshow("Press q to end", frame)
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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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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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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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# 关闭相机
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mvsdk.CameraUnInit(hCamera)
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# 释放帧缓存
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mvsdk.CameraAlignFree(pFrameBuffer)
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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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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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