GUI (Image Identification for Self-Driving Cars)

GUI Image/Sign/GUI/M/M.txt

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+FollowSign

GUI Image/Sign/GUI/P/P.txt

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+Be Aware

GUI Image/Sign/GUI/W/W.txt

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+Caution

SignAlgo_DL_final.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Sat Jul 24 12:43:27 2021
+
+@author: user
+"""
+
+"""
+File name: 演算法對接範例程式_v01.py
+Date: 2021/07/19
+Version: v01
+Usage: 演算法對接主程式
+"""
+
+
+#region import package
+
+import os
+import numpy as np
+
+# def load_image_files
+import cv2
+import glob
+
+# def loadModel
+from tensorflow.keras.models import load_model
+
+# predict
+from tensorflow.keras.models import Model
+
+#endregion
+
+import tensorflow as tf
+if tf.test.is_gpu_available():
+      print('有啟用GPU')
+else:
+      print('尚未啟用GPU')  
+
+class AI_Algo():
+      """
+      A class used to ...
+
+      ...
+
+      Attributes
+      ----------
+      data : ndarray (n, w, h, 3)
+          the input image of training model
+      targets : ndarray (n, label)
+          the target label of training model
+      model : 
+          Trained model
+
+      Methods
+      -------
+      __init__(...)
+          Class initialization
+      fit(X, y)
+          Train model
+      saveModel(path):
+          Save model
+      loadModel(path):
+          Load model
+      predict(X):
+          Predict model
+      """
+
+      classLabel= {
+                  "0":{"name":"CarOnly","info":"FollowSign","color":"blue", 'Group': 'M'},
+                  "1":{"name":"DirectForMotor","info":"FollowSign","color":"blue", 'Group': 'M'},
+                  "2":{"name":"KeepLeft","info":"FollowSign","color":"blue", 'Group': 'M'},
+                  "3":{"name":"KeepRight","info":"FollowSign","color":"blue", 'Group': 'M'},
+                  "4":{"name":"LeftTurn","info":"FollowSign","color":"blue", 'Group': 'M'},
+                  "5":{"name":"RightTurn","info":"FollowSign","color":"blue", 'Group': 'M'},
+                  "6":{"name":"Roundabout","info":"FollowSign","color":"blue", 'Group': 'M'},
+                  "7":{"name":"StraightOnly","info":"FollowSign","color":"blue", 'Group': 'M'},
+                  "8":{"name":"StraightOrLeft","info":"FollowSign","color":"blue", 'Group': 'M'},
+                  "9":{"name":"StraightOrRight","info":"FollowSign","color":"blue", 'Group': 'M'},
+                  "10":{"name":"Other_SignC","info":"Look UP","color":"green", 'Group': 'P'},
+                  "11":{"name":"Other_SignD","info":"Look UP","color":"green", 'Group': 'W'},
+                  "12":{"name":"Other_SignT","info":"Look UP","color":"green", 'Group': 'W'},
+                  "13":{"name":"Other_SignT2","info":"Look UP","color":"green", 'Group': 'W'},
+                  "14":{"name":"NoEntry","info":"Be Aware","color":"red", 'Group': 'P'},
+                  "15":{"name":"NoLeftTurn","info":"Be Aware","color":"red", 'Group': 'P'},
+                  "16":{"name":"NoRandLTurn","info":"Be Aware","color":"red", 'Group': 'P'},
+                  "17":{"name":"NoRightTurn","info":"Be Aware","color":"red", 'Group': 'P'},
+                  "18":{"name":"NoStopping","info":"Be Aware","color":"red", 'Group': 'P'},
+                  "19":{"name":"NoUTurn","info":"Be Aware","color":"red", 'Group': 'P'},
+                  "20":{"name":"ALTRightTurn","info":"Be Aware","color":"red", 'Group': 'P'},
+                  "21":{"name":"SpdLimit100","info":"Be Aware","color":"red", 'Group': 'P'},
+                  "22":{"name":"SpdLimit120","info":"Be Aware","color":"red", 'Group': 'P'},
+                  "23":{"name":"SpdLimit20","info":"Be Aware","color":"red", 'Group': 'P'},
+                  "24":{"name":"SpdLimit30","info":"Be Aware","color":"red", 'Group': 'P'},
+                  "25":{"name":"SpdLimit40","info":"Be Aware","color":"red", 'Group': 'P'},
+                  "26":{"name":"SpdLimit50","info":"Be Aware","color":"red", 'Group': 'P'},
+                  "27":{"name":"SpdLimit60","info":"Be Aware","color":"red", 'Group': 'P'},
+                  "28":{"name":"Spdimit70","info":"Be Aware","color":"red", 'Group': 'P'},
+                  "29":{"name":"Spdimit80","info":"Be Aware","color":"red", 'Group': 'P'},
+                  "30":{"name":"Children","info":"Caution","color":"orange", 'Group': 'W'},
+                  "31":{"name":"CurveToLeft","info":"Caution","color":"orange", 'Group': 'W'},
+                  "32":{"name":"CurveToRight","info":"Caution","color":"orange", 'Group': 'W'},
+                  "33":{"name":"DoubleCurve1","info":"Caution","color":"orange", 'Group': 'W'},
+                  "34":{"name":"DoubleCurve2","info":"Caution","color":"orange", 'Group': 'W'},
+                  "35":{"name":"FallingRocks","info":"Caution","color":"orange", 'Group': 'W'},
+                  "36":{"name":"OtherDanger","info":"Caution","color":"orange", 'Group': 'W'},
+                  "37":{"name":"Pedestrians","info":"Caution","color":"orange", 'Group': 'W'},
+                  "38":{"name":"RoadNarrows","info":"Caution","color":"orange", 'Group': 'W'},
+                  "39":{"name":"RoadLights","info":"Caution","color":"orange", 'Group': 'W'},
+                  "40":{"name":"SeparateIsland","info":"Caution","color":"orange", 'Group': 'W'},
+                  "41":{"name":"SlipperyRoad","info":"Caution","color":"orange", 'Group': 'W'},
+                  "42":{"name":"Slow","info":"Caution","color":"orange", 'Group': 'W'},
+                  "43":{"name":"Stop","info":"Caution","color":"orange", 'Group': 'W'},
+                  "44":{"name":"UnevenRoad","info":"Caution","color":"orange", 'Group': 'W'},
+                  }
+
+      DictImage_stan_Sign = {}
+      Dict_name_2_Group = {}
+
+      def __init__(self): # 參數
+          """
+          Constructor: Class initialization
+          """
+          print('__init__')
+          self.initial_DictImage_stan_Sign()
+          self.initial_Dict_name_2_Group()
+          return
+
+      def initial_DictImage_stan_Sign(self):
+          dir_image = 'GUI Image/Sign/Standard_Sample/'
+          for i in range(len(self.classLabel)):
+              path = dir_image + self.classLabel[str(i)]['Group'] + '/' + self.classLabel[str(i)]['name'] + '.jpg'
+              self.DictImage_stan_Sign[self.classLabel[str(i)]['name']] = cv2.imdecode(np.fromfile(path, dtype=np.uint8), 1)
+          return
+
+      def initial_Dict_name_2_Group(self):
+          for i in range(len(self.classLabel)):
+              self.Dict_name_2_Group[self.classLabel[str(i)]['name']] = self.classLabel[str(i)]['Group']
+          return
+
+      def loadModel(self, path):
+          """
+          Load model
+          '''
+          Parameters
+          ----------
+          path : string
+              Loading model path
+          """
+          with tf.device('/cpu:0'):
+            self.model = load_model(path)
+          return  
+
+      def predict(self, X_test):
+          with tf.device('/cpu:0'):
+            y_pred = self.model.predict(X_test)
+          # 整理y_pred的格式
+          for i in range(len(y_pred)):
+            temp_list = []
+
+            temp_list.append(y_pred[i])
+            arr_total = np.sum(temp_list)
+            y_pred[i] = y_pred[i]/arr_total
+
+          # 得到y_pred的機率
+          y_pred_probability = []
+          for i in range(len(y_pred)):
+
+            probability = y_pred[i].max()
+            y_pred_probability.append(probability)
+
+          # 得到y_pred的label
+          predictresult = [] 
+          for i in range(X_test.shape[0]): 
+            predictresult.append(y_pred[i].argmax())
+
+          # 最終輸出格式: [('M_CarOnly', 0.35248378, 'M', "red")]
+          output = []
+          for i in range(len(y_pred)):
+            int_dict = str(predictresult[i])
+            name_dict = self.classLabel[int_dict]['name']
+            shorthand = self.classLabel[int_dict]['info']
+            color = self.classLabel[int_dict]['color']
+
+            output.append(tuple([name_dict, y_pred_probability[i], shorthand, color]))
+          return output
+
+      def predict2(self, listImg: list, dimension=(32, 32), BGR=False): # Jeff Revised!
+          # Preprocess
+          raw_data_test = []
+          for img in listImg:
+              img = img[:, :, ::(-1 if BGR else 1)]
+              resized = cv2.resize(img, dimension, interpolation = cv2.INTER_AREA)
+              raw_data_test.append(resized)
+          X_test = np.array(raw_data_test)
+
+          with tf.device('/cpu:0'):
+            y_pred = self.model.predict(X_test)
+          # 整理y_pred的格式
+          for i in range(len(y_pred)):
+            temp_list = []
+
+            temp_list.append(y_pred[i])
+            arr_total = np.sum(temp_list)
+            y_pred[i] = y_pred[i]/arr_total
+
+          # 得到y_pred的機率
+          y_pred_probability = []
+          for i in range(len(y_pred)):
+
+            probability = y_pred[i].max()
+            y_pred_probability.append(probability)
+
+          # 得到y_pred的label
+          predictresult = [] 
+          for i in range(X_test.shape[0]): 
+            predictresult.append(y_pred[i].argmax())
+
+          # 最終輸出格式: [('M_CarOnly', 0.35248378, 'M', "red")]
+          output = []
+          for i in range(len(y_pred)):
+            int_dict = str(predictresult[i])
+            name_dict = self.classLabel[int_dict]['name']
+            shorthand = self.classLabel[int_dict]['info']
+            color = self.classLabel[int_dict]['color']
+
+            output.append(tuple([name_dict, y_pred_probability[i], shorthand, color]))
+          return output
+
+      def load_image_files(self, container_path, dimension=(32, 32)):
+          """
+          Load image files with categories as subfolder names 
+          which performs like scikit-learn sample dataset
+          Parameters
+          ----------
+          container_path : string or unicode
+              Path to the main folder holding one subfolder per category
+          dimension : tuple
+              size to which image are adjusted to
+          Returns
+          -------
+          Bunch
+          """
+
+          raw_data_test = []
+          test_len = []
+          size = dimension
+
+
+          # link = container_path + "*.jpg"
+          link = os.path.join(container_path, '*.jpg')
+          path = glob.glob(link)
+
+          pre_size = len(raw_data_test)
+          #print(pre_size)
+
+
+          for img in path:
+            #n = cv2.imread(img)
+            n = cv2.imdecode(np.fromfile(img, dtype=np.uint8), 1) # Jeff Revised!
+            n = cv2.cvtColor(n, cv2.COLOR_BGR2RGB)
+            resized = cv2.resize(n, size, interpolation = cv2.INTER_AREA)
+            raw_data_test.append(resized)
+
+          real_size = len(raw_data_test) - pre_size
+
+          test_len.append(real_size)
+
+          test_len = np.array(raw_data_test)
+
+          return test_len
+
+if __name__ == "__main__": # 演算法測試
+    ai_Algo = AI_Algo()
+
+    ai_Algo.loadModel(r"D:\Machine Learning\工研院產業新尖兵\專題\交通號誌辨識\Model\sign_batch.h5")
+
+    #region Preprocess
+
+    #讀取圖片需要Monica的奧援
+    data = ai_Algo.load_image_files(r"D:\Machine Learning\工研院產業新尖兵\專題\交通號誌辨識\UNclear")
+    #endregion
+    x = ai_Algo.predict(data)
+    print(x)
+
+    #region Train model
+
+    #endregion
+
+    #region Test model
+
+    print('-' * 30)
+
+    # import glob
+    # listImg = []
+    # dir_img = 'D:/Machine Learning/工研院產業新尖兵/專題/Code/yolov5/yolov5/runs/detect/exp6/crops/sign/'
+    # for path in glob.glob(dir_img + '*.jpg'):
+    #     print(path)
+    #     listImg.append(cv2.imdecode(np.fromfile(path, dtype=np.uint8), 1))
+
+    # print(ai_Algo.predict2(listImg, BGR=True))
+
+    #endregion