大版本更新。

tools/TrainCNN/backward.py

@@ -5,8 +5,8 @@ from tqdm import tqdm
 import generate
 import forward
 import cv2
-import sys
 import numpy as np
+import mvsdk
 print("Finish!")
 
 def save_kernal(fp, val):
@@ -54,7 +54,7 @@ def save_para(folder, paras):
         save_bias(fp, paras[7])
 
 
-STEPS = 100000
+STEPS = 5000
 BATCH = 30
 LEARNING_RATE_BASE  = 0.01
 LEARNING_RATE_DECAY = 0.99
@@ -62,12 +62,16 @@ 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)
+    keep_rate = tf.placeholder(tf.float32)
+    nodes, vars = forward.forward(x, 0.01)
     y = nodes[-1]
 
-    ce  = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1))
+#    ce  = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1))
+    ce  = tf.nn.weighted_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1), pos_weight=1)
     cem = tf.reduce_mean(ce)
     loss= cem + tf.add_n(tf.get_collection("losses"))
 
@@ -87,72 +91,118 @@ def train(dataset, show_bar=False):
 
     correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
     accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
+
     config = tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True))
     with tf.Session(config=config) as sess:
         init_op = tf.global_variables_initializer()
         sess.run(init_op)
 
-        bar = tqdm(range(STEPS), dynamic_ncols=True)
+        bar = tqdm(range(STEPS), ascii=True, dynamic_ncols=True)
         for i in bar:
             images_samples, labels_samples = dataset.sample_train_sets(BATCH)
 
             _, loss_value, step = sess.run(
                 [train_op, loss, global_step],
-                feed_dict={x: images_samples, y_: labels_samples}
+                feed_dict={x: images_samples, y_: labels_samples, keep_rate:0.7}
             )
 
             if i % 100 == 0:
                 if i % 1000 == 0:
-                    test_samples, test_labels = dataset.sample_test_sets(1000)
-                    acc = sess.run(accuracy, feed_dict={x: test_samples, y_: test_labels})
+                    acc = sess.run(accuracy, feed_dict={x: test_images, y_: test_labels, keep_rate:1.0})
                 bar.set_postfix({"loss": loss_value, "acc": acc})
 
-        # video = cv2.VideoCapture("/home/xinyang/Desktop/Video.mp4")
-        # _ = True
-        # while _:
-        #     _, frame = video.read()
-        #     cv2.imshow("Video", frame)
-        #     k = cv2.waitKey(10)
-        #     if k == ord(" "):
-        #         bbox = cv2.selectROI("frame", frame, False)
-        #         print(bbox)
-        #         roi = frame[bbox[1]:bbox[1]+bbox[3], bbox[0]:bbox[0]+bbox[2]]
-        #         roi = cv2.resize(roi, (48, 36))
-        #         cv2.imshow("roi", roi)
-        #         cv2.waitKey(0)
-        #         roi = roi.astype(np.float32)
-        #         roi /= 255.0
-        #         roi = roi.reshape([1, 36, 48, 3])
-        #         res = sess.run(y, feed_dict={x: roi})
-        #         res = res.reshape([forward.OUTPUT_NODES])
-        #         print(np.argmax(res))
-        #     elif k==ord("q"):
-        #         break
-        # keep = True
-        # while keep:
-        #     n = input()
-        #     im = cv2.imread(n)
-        #     im = cv2.resize(im, (48, 36))
-        #     cv2.imshow("im", im)
-        #     if cv2.waitKey(0) == ord("q"):
-        #         keep = False
-        #     im = im.astype(np.float32)
-        #     im /= 255.0
-        #     im = im.reshape([1, 36, 48, 3])
-        #     res = sess.run(y, feed_dict={x: im})
-        #     res = res.reshape([forward.OUTPUT_NODES])
-        #     print(np.argmax(res))
-    
-        test_samples, test_labels = dataset.sample_test_sets(100)
+
+
         vars_val = sess.run(vars)
-        save_para("/home/xinyang/Desktop/AutoAim/tools/para", vars_val)
-        nodes_val = sess.run(nodes, feed_dict={x:test_samples})
-        return vars_val, nodes_val, test_samples
+        save_para("/home/xinyang/Desktop/RM_auto-aim/tools/para", vars_val)
+        print("save done!")
+#        nodes_val = sess.run(nodes, feed_dict={x:test_images})
+#        return vars_val, nodes_val
+        DevList = mvsdk.CameraEnumerateDevice()
+        nDev = len(DevList)
+        if nDev < 1:
+            print("No camera was found!")
+            return
+
+        for i, DevInfo in enumerate(DevList):
+            print("{}: {} {}".format(i, DevInfo.GetFriendlyName(), DevInfo.GetPortType()))
+        i = 0 if nDev == 1 else int(input("Select camera: "))
+        DevInfo = DevList[i]
+        print(DevInfo)
+
+        # 打开相机
+        hCamera = 0
+        try:
+            hCamera = mvsdk.CameraInit(DevInfo, -1, -1)
+        except mvsdk.CameraException as e:
+            print("CameraInit Failed({}): {}".format(e.error_code, e.message) )
+            return
+
+        # 获取相机特性描述
+        cap = mvsdk.CameraGetCapability(hCamera)
+
+        # 判断是黑白相机还是彩色相机
+        monoCamera = (cap.sIspCapacity.bMonoSensor != 0)
+
+        # 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
+        if monoCamera:
+            mvsdk.CameraSetIspOutFormat(hCamera, mvsdk.CAMERA_MEDIA_TYPE_MONO8)
+        else:
+            mvsdk.CameraSetIspOutFormat(hCamera, mvsdk.CAMERA_MEDIA_TYPE_BGR8)
+
+        # 相机模式切换成连续采集
+        mvsdk.CameraSetTriggerMode(hCamera, 0)
+
+        # 手动曝光，曝光时间30ms
+        mvsdk.CameraSetAeState(hCamera, 0)
+        mvsdk.CameraSetExposureTime(hCamera, 30 * 1000)
+
+        # 让SDK内部取图线程开始工作
+        mvsdk.CameraPlay(hCamera)
+
+        # 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
+        FrameBufferSize = cap.sResolutionRange.iWidthMax * cap.sResolutionRange.iHeightMax * (1 if monoCamera else 3)
+
+        # 分配RGB buffer，用来存放ISP输出的图像
+        # 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）
+        pFrameBuffer = mvsdk.CameraAlignMalloc(FrameBufferSize, 16)
+
+        while (cv2.waitKey(1) & 0xFF) != ord('q'):
+            # 从相机取一帧图片
+            try:
+                pRawData, FrameHead = mvsdk.CameraGetImageBuffer(hCamera, 200)
+                mvsdk.CameraImageProcess(hCamera, pRawData, pFrameBuffer, FrameHead)
+                mvsdk.CameraReleaseImageBuffer(hCamera, pRawData)
+                # 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
+                # 把pFrameBuffer转换成opencv的图像格式以进行后续算法处理
+                frame_data = (mvsdk.c_ubyte * FrameHead.uBytes).from_address(pFrameBuffer)
+                frame = np.frombuffer(frame_data, dtype=np.uint8)
+                frame = frame.reshape((FrameHead.iHeight, FrameHead.iWidth, 1 if FrameHead.uiMediaType == mvsdk.CAMERA_MEDIA_TYPE_MONO8 else 3) )
+
+                frame = cv2.resize(frame, (640,480), interpolation = cv2.INTER_LINEAR)
+                cv2.imshow("Press q to end", frame)
+                if (cv2.waitKey(1)&0xFF) == ord(' '):
+                    roi = cv2.selectROI("roi", frame)
+                    roi = frame[roi[1]:roi[1]+roi[3], roi[0]:roi[0]+roi[2]]
+                    print(roi)
+                    cv2.imshow("box", roi)
+                    image = cv2.resize(roi, (48, 36))
+                    image = image.astype(np.float32) / 255.0
+                    out = sess.run(y, feed_dict={x:[image]})
+                    print(out)
+                    print(np.argmax(out))
+                
+            except mvsdk.CameraException as e:
+                if e.error_code != mvsdk.CAMERA_STATUS_TIME_OUT:
+                    print("CameraGetImageBuffer failed({}): {}".format(e.error_code, e.message) )
+
+        # 关闭相机
+        mvsdk.CameraUnInit(hCamera)
+
+        # 释放帧缓存
+        mvsdk.CameraAlignFree(pFrameBuffer)
 
 
 if __name__ == "__main__":
-    print("Loading data sets...")
-    dataset = generate.DataSet("/home/xinyang/Desktop/dataset/box")
-    print("Finish!")
+    dataset = generate.DataSet("/home/xinyang/Desktop/box_cut")
     train(dataset, show_bar=True)
-    input("Press any key to end...")