SUStech_EdgeCrowdCountingSystem

arc_design_contest/2019/SUStech_EdgeCrowdCountingSystem/data_collection_pi/main.py

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+import time
+import cv2
+import os
+import sys
+from itertools import cycle
+import tensorflow as tf
+import numpy as np
+import socket
+
+
+def conv2d(x, w):
+    return tf.nn.conv2d(x, w, strides=[1, 1, 1, 1], padding='SAME')
+
+
+def max_pool_2x2(x):
+    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
+                          strides=[1, 2, 2, 1], padding='SAME')
+
+
+def inf(x):
+    # tf.reset_default_graph()
+    # s net ###########################################################
+    w_conv1_1 = tf.get_variable('w_conv1_1', [5, 5, 1, 24])
+    b_conv1_1 = tf.get_variable('b_conv1_1', [24])
+    h_conv1_1 = tf.nn.relu(conv2d(x, w_conv1_1) + b_conv1_1)
+
+    h_pool1_1 = max_pool_2x2(h_conv1_1)
+
+    w_conv2_1 = tf.get_variable('w_conv2_1', [3, 3, 24, 48])
+    b_conv2_1 = tf.get_variable('b_conv2_1', [48])
+    h_conv2_1 = tf.nn.relu(conv2d(h_pool1_1, w_conv2_1) + b_conv2_1)
+
+    h_pool2_1 = max_pool_2x2(h_conv2_1)
+
+    w_conv3_1 = tf.get_variable('w_conv3_1', [3, 3, 48, 24])
+    b_conv3_1 = tf.get_variable('b_conv3_1', [24])
+    h_conv3_1 = tf.nn.relu(conv2d(h_pool2_1, w_conv3_1) + b_conv3_1)
+
+    w_conv4_1 = tf.get_variable('w_conv4_1', [3, 3, 24, 12])
+    b_conv4_1 = tf.get_variable('b_conv4_1', [12])
+    h_conv4_1 = tf.nn.relu(conv2d(h_conv3_1, w_conv4_1) + b_conv4_1)
+
+    # m net ###########################################################
+    w_conv1_2 = tf.get_variable('w_conv1_2', [7, 7, 1, 20])
+    b_conv1_2 = tf.get_variable('b_conv1_2', [20])
+    h_conv1_2 = tf.nn.relu(conv2d(x, w_conv1_2) + b_conv1_2)
+
+    h_pool1_2 = max_pool_2x2(h_conv1_2)
+
+    w_conv2_2 = tf.get_variable('w_conv2_2', [5, 5, 20, 40])
+    b_conv2_2 = tf.get_variable('b_conv2_2', [40])
+    h_conv2_2 = tf.nn.relu(conv2d(h_pool1_2, w_conv2_2) + b_conv2_2)
+
+    h_pool2_2 = max_pool_2x2(h_conv2_2)
+
+    w_conv3_2 = tf.get_variable('w_conv3_2', [5, 5, 40, 20])
+    b_conv3_2 = tf.get_variable('b_conv3_2', [20])
+    h_conv3_2 = tf.nn.relu(conv2d(h_pool2_2, w_conv3_2) + b_conv3_2)
+
+    w_conv4_2 = tf.get_variable('w_conv4_2', [5, 5, 20, 10])
+    b_conv4_2 = tf.get_variable('b_conv4_2', [10])
+    h_conv4_2 = tf.nn.relu(conv2d(h_conv3_2, w_conv4_2) + b_conv4_2)
+
+    # l net ###########################################################
+    w_conv1_3 = tf.get_variable('w_conv1_3', [9, 9, 1, 16])
+    b_conv1_3 = tf.get_variable('b_conv1_3', [16])
+    h_conv1_3 = tf.nn.relu(conv2d(x, w_conv1_3) + b_conv1_3)
+
+    h_pool1_3 = max_pool_2x2(h_conv1_3)
+
+    w_conv2_3 = tf.get_variable('w_conv2_3', [7, 7, 16, 32])
+    b_conv2_3 = tf.get_variable('b_conv2_3', [32])
+    h_conv2_3 = tf.nn.relu(conv2d(h_pool1_3, w_conv2_3) + b_conv2_3)
+
+    h_pool2_3 = max_pool_2x2(h_conv2_3)
+
+    w_conv3_3 = tf.get_variable('w_conv3_3', [7, 7, 32, 16])
+    b_conv3_3 = tf.get_variable('b_conv3_3', [16])
+    h_conv3_3 = tf.nn.relu(conv2d(h_pool2_3, w_conv3_3) + b_conv3_3)
+
+    w_conv4_3 = tf.get_variable('w_conv4_3', [7, 7, 16, 8])
+    b_conv4_3 = tf.get_variable('b_conv4_3', [8])
+    h_conv4_3 = tf.nn.relu(conv2d(h_conv3_3, w_conv4_3) + b_conv4_3)
+
+    # merge ###########################################################
+    h_conv4_merge = tf.concat([h_conv4_1, h_conv4_2, h_conv4_3], 3)
+
+    w_conv5 = tf.get_variable('w_conv5', [1, 1, 30, 1])
+    b_conv5 = tf.get_variable('b_conv5', [1])
+    h_conv5 = conv2d(h_conv4_merge, w_conv5) + b_conv5
+
+    y_pre = h_conv5
+
+    return y_pre
+
+
+graph1 = tf.Graph()
+with graph1.as_default():
+    x = tf.placeholder(tf.float32, [None, None, None, 1], name="input")
+    y_act = tf.placeholder(tf.float32, [None, None, None, 1])
+    y_pre = inf(x)
+
+frame_path = './data/'
+filenames = os.listdir(frame_path)
+img_iter = cycle([cv2.imread(os.sep.join([frame_path, name]), 0)
+                 for name in filenames])
+
+key = 0
+cv2.namedWindow('image', cv2.WINDOW_NORMAL)
+cv2.resizeWindow('image', 640, 480)
+
+SEVER_ADDR = ('192.168.31.67', 80)
+
+client_socket = socket(socket.AF_INET, socket.SOCK_STREAM)
+client_socket.connect(SEVER_ADDR)
+print('connect sever(%s) success' % str(SEVER_ADDR))
+
+with tf.Session(graph=graph1) as sess:
+    saver = tf.train.Saver()
+    saver.restore(sess, 'modelA/model.ckpt')
+
+    while key & 0xFF != 28:
+        img = next(img_iter)
+        img = np.array(img)
+        img = (img-127.5)/128
+        data = []
+        data.append([img])
+        d = data[0]
+        x_in = d[0]
+        x_in = np.reshape(d[0], (1, d[0].shape[0], d[0].shape[1], 1))
+        timea = time.time()
+        y_p_den = sess.run(y_pre, feed_dict={"input:0": x_in})
+        timeb = time.time()
+        cv2.imshow('image', img)
+
+        tm = time.strftime("%Y%m%d%H%M%S", time.localtime())
+        real_num = "{:0>4d}".format(np.sum(y_p_den))
+        send_data = 'A'+real_num+tm
+        client_socket.send(send_data.encode())
+
+        print("prediction result:%f" % real_num)
+        print('send message %s' % send_data)
+        print('=============================================================')
+        print('cost time: %.2f s' % (timea - timeb))
+
+        sys.stdout.flush()
+        key = cv2.waitKey(1000)

arc_design_contest/2019/SUStech_EdgeCrowdCountingSystem/data_collection_pi/modelA/checkpoint

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+model_checkpoint_path: "model.ckpt"
+all_model_checkpoint_paths: "model.ckpt"

arc_design_contest/2019/SUStech_EdgeCrowdCountingSystem/data_collection_pi/modelA/modelA.txt

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+

arc_design_contest/2019/SUStech_EdgeCrowdCountingSystem/data_collection_pi/network.py

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+import numpy as np
+import cv2
+import tensorflow as tf
+
+
+class MCNN:
+    def __init__(self, dataset=None):
+        self.dataset = dataset
+        self.LEARNING_RATE = 1e-4
+
+        self.x = tf.placeholder(tf.float32, [None, None, None, 1])
+        self.y_act = tf.placeholder(tf.float32, [None, None, None, 1])
+        self.y_pre = self.inf(self.x)
+
+        # Loss - Euclidean Distance
+        self.loss = tf.sqrt(tf.reduce_mean(tf.square(self.y_act - self.y_pre)))
+        self.act_sum = tf.reduce_sum(self.y_act)
+        self.pre_sum = tf.reduce_sum(self.y_pre)
+
+        # Mean Absolute Error
+        self.MAE = tf.abs(self.act_sum - self.pre_sum)
+
+        self.train_step = tf.train.AdamOptimizer(
+            self.LEARNING_RATE).minimize(self.loss)
+
+    def conv2d(self, x, w):
+        return tf.nn.conv2d(x, w, strides=[1, 1, 1, 1], padding='SAME')
+
+    def max_pool_2x2(self, x):
+        return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
+                              strides=[1, 2, 2, 1], padding='SAME')
+
+    def inf(self, x):
+        # tf.reset_default_graph()
+        # s net ###########################################################
+        w_conv1_1 = tf.get_variable('w_conv1_1', [5, 5, 1, 24])
+        b_conv1_1 = tf.get_variable('b_conv1_1', [24])
+        h_conv1_1 = tf.nn.relu(self.conv2d(x, w_conv1_1) + b_conv1_1)
+
+        h_pool1_1 = self.max_pool_2x2(h_conv1_1)
+
+        w_conv2_1 = tf.get_variable('w_conv2_1', [3, 3, 24, 48])
+        b_conv2_1 = tf.get_variable('b_conv2_1', [48])
+        h_conv2_1 = tf.nn.relu(self.conv2d(h_pool1_1, w_conv2_1) + b_conv2_1)
+
+        h_pool2_1 = self.max_pool_2x2(h_conv2_1)
+
+        w_conv3_1 = tf.get_variable('w_conv3_1', [3, 3, 48, 24])
+        b_conv3_1 = tf.get_variable('b_conv3_1', [24])
+        h_conv3_1 = tf.nn.relu(self.conv2d(h_pool2_1, w_conv3_1) + b_conv3_1)
+
+        w_conv4_1 = tf.get_variable('w_conv4_1', [3, 3, 24, 12])
+        b_conv4_1 = tf.get_variable('b_conv4_1', [12])
+        h_conv4_1 = tf.nn.relu(self.conv2d(h_conv3_1, w_conv4_1) + b_conv4_1)
+
+        # m net ###########################################################
+        w_conv1_2 = tf.get_variable('w_conv1_2', [7, 7, 1, 20])
+        b_conv1_2 = tf.get_variable('b_conv1_2', [20])
+        h_conv1_2 = tf.nn.relu(self.conv2d(x, w_conv1_2) + b_conv1_2)
+
+        h_pool1_2 = self.max_pool_2x2(h_conv1_2)
+
+        w_conv2_2 = tf.get_variable('w_conv2_2', [5, 5, 20, 40])
+        b_conv2_2 = tf.get_variable('b_conv2_2', [40])
+        h_conv2_2 = tf.nn.relu(self.conv2d(h_pool1_2, w_conv2_2) + b_conv2_2)
+
+        h_pool2_2 = self.max_pool_2x2(h_conv2_2)
+
+        w_conv3_2 = tf.get_variable('w_conv3_2', [5, 5, 40, 20])
+        b_conv3_2 = tf.get_variable('b_conv3_2', [20])
+        h_conv3_2 = tf.nn.relu(self.conv2d(h_pool2_2, w_conv3_2) + b_conv3_2)
+
+        w_conv4_2 = tf.get_variable('w_conv4_2', [5, 5, 20, 10])
+        b_conv4_2 = tf.get_variable('b_conv4_2', [10])
+        h_conv4_2 = tf.nn.relu(self.conv2d(h_conv3_2, w_conv4_2) + b_conv4_2)
+
+        # l net ###########################################################
+        w_conv1_3 = tf.get_variable('w_conv1_3', [9, 9, 1, 16])
+        b_conv1_3 = tf.get_variable('b_conv1_3', [16])
+        h_conv1_3 = tf.nn.relu(self.conv2d(x, w_conv1_3) + b_conv1_3)
+
+        h_pool1_3 = self.max_pool_2x2(h_conv1_3)
+
+        w_conv2_3 = tf.get_variable('w_conv2_3', [7, 7, 16, 32])
+        b_conv2_3 = tf.get_variable('b_conv2_3', [32])
+        h_conv2_3 = tf.nn.relu(self.conv2d(h_pool1_3, w_conv2_3) + b_conv2_3)
+
+        h_pool2_3 = self.max_pool_2x2(h_conv2_3)
+
+        w_conv3_3 = tf.get_variable('w_conv3_3', [7, 7, 32, 16])
+        b_conv3_3 = tf.get_variable('b_conv3_3', [16])
+        h_conv3_3 = tf.nn.relu(self.conv2d(h_pool2_3, w_conv3_3) + b_conv3_3)
+
+        w_conv4_3 = tf.get_variable('w_conv4_3', [7, 7, 16, 8])
+        b_conv4_3 = tf.get_variable('b_conv4_3', [8])
+        h_conv4_3 = tf.nn.relu(self.conv2d(h_conv3_3, w_conv4_3) + b_conv4_3)
+
+        # merge ###########################################################
+        h_conv4_merge = tf.concat([h_conv4_1, h_conv4_2, h_conv4_3], 3)
+
+        w_conv5 = tf.get_variable('w_conv5', [1, 1, 30, 1])
+        b_conv5 = tf.get_variable('b_conv5', [1])
+        h_conv5 = self.conv2d(h_conv4_merge, w_conv5) + b_conv5
+
+        y_pre = h_conv5
+
+        return y_pre
+
+    def predict(self, img_path='./data/IMG_3.jpg'):
+        with tf.Session() as sess:
+            saver = tf.train.Saver()
+            saver.restore(sess, 'model' + self.dataset + '/model.ckpt')
+
+            # img_path = '.\\data\\original\\
+            # shanghaitech\\part_'+ self.dataset +'\\test_data\\images\\'
+
+            print('Image Loading!')
+
+            data = []
+            img = cv2.imread(img_path, 0)
+            img = np.array(img)
+            img = (img - 127.5) / 128
+            data.append([img])
+
+            print('Image loaded!')
+
+            d = data[0]
+            x_in = d[0]
+
+            x_in = np.reshape(d[0], (1, d[0].shape[0], d[0].shape[1], 1))
+            y_p_den = sess.run(self.y_pre, feed_dict={self.x: x_in})
+            y_p = np.sum(y_p_den)
+            print('y_p : ', y_p)
+            return y_p_den, y_p

arc_design_contest/2019/SUStech_EdgeCrowdCountingSystem/src/emsk_sdcard/emsk_sdcard.c

@@ -0,0 +1,100 @@
+/* ------------------------------------------
+ * Copyright (c) 2017, Synopsys, Inc. All rights reserved.
+
+ * Redistribution and use in source and binary forms, with or without modification,
+ * are permitted provided that the following conditions are met:
+
+ * 1) Redistributions of source code must retain the above copyright notice, this
+ * list of conditions and the following disclaimer.
+
+ * 2) Redistributions in binary form must reproduce the above copyright notice,
+ * this list of conditions and the following disclaimer in the documentation and/or
+ * other materials provided with the distribution.
+
+ * 3) Neither the name of the Synopsys, Inc., nor the names of its contributors may
+ * be used to endorse or promote products derived from this software without
+ * specific prior written permission.
+
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
+ * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ * ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *
+--------------------------------------------- */
+
+/**
+ * \defgroup	BOARD_EMSK_DRV_MID_FS_SDCARD	EMSK Fatfs Middleware SDCard Driver
+ * \ingroup	BOARD_EMSK_DRIVER
+ * \brief	EMSK Fatfs Middleware SDCard Interface Driver
+ * \details
+ *		Realize the sdcard driver for fatfs based on the middleware fatfs diskio abstract
+ *	layer, sdcard interface can be spi or sdio and so on.
+ */
+
+/**
+ * \file
+ * \ingroup	BOARD_EMSK_DRV_MID_FS_SDCARD
+ * \brief	sdcard driver for fatfs of emsk board
+ */
+
+/**
+ * \addtogroup	BOARD_EMSK_DRV_MID_FS_SDCARD
+ * @{
+ */
+#ifdef MID_FATFS /* only available when enable fatfs middleware */
+
+#include "embARC_toolchain.h"
+#include "ff_diskio.h"
+#include "emsk_sdcard.h"
+#include <time.h>
+
+uint32_t diskio_get_fattime(void)
+{
+	struct tm *p_tm;
+	time_t cur_time;
+	uint32_t fattime;
+
+	cur_time = time(NULL);
+	p_tm = localtime(&cur_time);
+	fattime = ((p_tm->tm_year+1900-1980)&0x7f) << 25;
+	fattime |= ((p_tm->tm_mon+1)&0xf) << 21;
+	fattime |= ((p_tm->tm_mday)&0x1f) << 16;
+	fattime |= ((p_tm->tm_hour)&0x1f) << 11;
+	fattime |= ((p_tm->tm_min)&0x3f) << 5;
+	fattime |= ((p_tm->tm_sec>>1)&0x1f);
+
+	return fattime;
+}
+
+FATFS_DISKIO *get_fatfs_diskio(uint32_t drvid)
+{
+	switch (drvid) {
+#if (USE_EMSK_SDCARD_SPI_0)
+		case EMSK_SDCARD_0_DRVID:
+			return &sdcard_spi_0_diskio;
+			break;
+#endif
+		default:
+			break;
+	}
+	return NULL;
+}
+
+/** update in 1ms period interrupt for disk status update */
+void emsk_sdcard_1ms_update(void)
+{
+#if (USE_EMSK_SDCARD_SPI_0)
+	sdcard_spi_0_diskio.diskio_timerproc();
+#endif
+}
+
+
+#endif /* MID_FATFS */
+
+/** @} end of group BOARD_EMSK_DRV_MID_FS_SDCARD */