更新CNN参数，使用CNN判断tracking是否跟丢。

tools/TrainCNN/backward.py

@@ -1,10 +1,12 @@
 #!/usr/bin/python3
+print("Preparing...")
 import tensorflow as tf
 from tqdm import tqdm
 import generate
 import forward
 import cv2
 import numpy as np
+print("Finish!")
 
 def save_kernal(fp, val):
     print(val.shape[2], file=fp)
@@ -91,7 +93,7 @@ def train(dataset, show_bar=False):
         init_op = tf.global_variables_initializer()
         sess.run(init_op)
 
-        bar = tqdm(range(STEPS), ascii=True, dynamic_ncols=True)
+        bar = tqdm(range(STEPS), dynamic_ncols=True)
         for i in bar:
             images_samples, labels_samples = dataset.sample_train_sets(BATCH)
 
@@ -111,41 +113,41 @@ def train(dataset, show_bar=False):
             #     bar.cursor.restore()
             #     bar.draw(value=i+1)
 
-        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))
+        # 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))
 
         vars_val = sess.run(vars)
         save_para("/home/xinyang/Desktop/AutoAim/tools/para", vars_val)
@@ -154,5 +156,7 @@ def train(dataset, show_bar=False):
 
 
 if __name__ == "__main__":
+    print("Loading data sets...")
     dataset = generate.DataSet("/home/xinyang/Desktop/DataSets/box")
+    print("Finish!")
     train(dataset, show_bar=True)

tools/TrainCNN/generate.py

@@ -3,6 +3,9 @@ import os
 import cv2
 import random
 from forward import OUTPUT_NODES
+import sys
+import os
+from tqdm import tqdm
 # 原图像行数
 SRC_ROWS = 36
 
@@ -22,10 +25,14 @@ class DataSet:
         self.generate_data_sets(folder)
 
     def file2nparray(self, name):
-        image = cv2.imread(name)
-        image = cv2.resize(image, (SRC_COLS, SRC_ROWS))
-        image = image.astype(np.float32)
-        return image / 255.0
+        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])
@@ -37,7 +44,7 @@ class DataSet:
         for i in range(OUTPUT_NODES):
             dir = "%s/%d" % (folder, i)
             files = os.listdir(dir)
-            for file in files:
+            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)))

tools/para/conv1_b

@@ -1,5 +1,7 @@
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+6
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+1.1128483
+0.92715627
+2.6803706
+-2.4037633
+1.6914424

tools/para/conv1_w

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