chalky classifier

arielcatli · Mar 16, 2018 · 7080af7 · 7080af7
1 parent 3a48e30
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diff --git a/chalky_range.csv b/chalky_range.csv
@@ -0,0 +1,16 @@
+206,213,195
+225,226,212
+235,234,216
+220,223,204
+236,233,216
+235,236,228
+235,230,215
+217,222,202
+226,227,209
+215,216,198
+229,228,208
+220,216,205
+216,213,204
+235,236,222
+227,226,206
+238,237,220
diff --git a/img-src/38/extracted/p/d/p99.jpg b/img-src/38/extracted/p/d/p99.jpg
diff --git a/img-src/52/bkn/p0.jpg b/img-src/52/bkn/p0.jpg
diff --git a/img-src/52/bkn/p12.jpg b/img-src/52/bkn/p12.jpg
diff --git a/img-src/52/extracted/p/p37.jpg → img-src/52/bkn/p15.jpg b/img-src/52/extracted/p/p37.jpg → img-src/52/bkn/p15.jpg
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diff --git a/img-src/52/extracted/p/p69.jpg → img-src/52/bkn/p3.jpg b/img-src/52/extracted/p/p69.jpg → img-src/52/bkn/p3.jpg
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diff --git a/img-src/52/chalky/p0.jpg b/img-src/52/chalky/p0.jpg
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diff --git a/img-src/52/extracted/p/p98.jpg b/img-src/52/extracted/p/p98.jpg
diff --git a/img-src/52/extracted/p/p99.jpg b/img-src/52/extracted/p/p99.jpg
diff --git a/img-src/52/nbkn/p1.jpg b/img-src/52/nbkn/p1.jpg
diff --git a/img-src/52/nbkn/p11.jpg b/img-src/52/nbkn/p11.jpg
diff --git a/img-src/52/nbkn/p13.jpg b/img-src/52/nbkn/p13.jpg
diff --git a/img-src/52/nbkn/p14.jpg b/img-src/52/nbkn/p14.jpg
diff --git a/img-src/52/nbkn/p16.jpg b/img-src/52/nbkn/p16.jpg
diff --git a/img-src/52/nbkn/p17.jpg b/img-src/52/nbkn/p17.jpg
diff --git a/img-src/52/nbkn/p18.jpg b/img-src/52/nbkn/p18.jpg
diff --git a/img-src/52/nbkn/p20.jpg b/img-src/52/nbkn/p20.jpg
diff --git a/img-src/52/nbkn/p21.jpg b/img-src/52/nbkn/p21.jpg
diff --git a/img-src/52/nbkn/p4.jpg b/img-src/52/nbkn/p4.jpg
diff --git a/img-src/52/nbkn/p6.jpg b/img-src/52/nbkn/p6.jpg
diff --git a/img-src/52/nbkn/p7.jpg b/img-src/52/nbkn/p7.jpg
diff --git a/img-src/52/nbkn/p9.jpg b/img-src/52/nbkn/p9.jpg
diff --git a/img-src/52/nchalky/p12.jpg b/img-src/52/nchalky/p12.jpg
diff --git a/img-src/52/nchalky/p14.jpg b/img-src/52/nchalky/p14.jpg
diff --git a/img-src/52/nchalky/p15.jpg b/img-src/52/nchalky/p15.jpg
diff --git a/img-src/52/nchalky/p16.jpg b/img-src/52/nchalky/p16.jpg
diff --git a/img-src/52/nchalky/p21.jpg b/img-src/52/nchalky/p21.jpg
diff --git a/img-src/52/nchalky/p6.jpg b/img-src/52/nchalky/p6.jpg
diff --git a/img-src/52/nchalky/p9.jpg b/img-src/52/nchalky/p9.jpg
diff --git a/python2/capture.py b/python2/capture.py
@@ -121,6 +121,16 @@ def __create_sample_directory(self):
             os.mkdir(self.sample_directory_extracted)
             self.sample_directory_extracted_e = self.sample_directory_extracted + "/e/"
             self.sample_directory_extracted_p = self.sample_directory_extracted + "/p/"
+            self.sample_directory_bkn = self.sample_directory + "/bkn/"
+            self.sample_directory_nbkn = self.sample_directory + "/nbkn/"
+            self.sample_directory_nylw = self.sample_directory + "/nylw/"
+            self.sample_directory_ylw = self.sample_directory + "/ylw/"
+            self.sample_directory_grn = self.sample_directory + "/grn/"
+            self.sample_directory_ngrn = self.sample_directory + "/ngrn/"
+            self.sample_directory_npaddy = self.sample_directory + "/npaddy/"
+            self.sample_directory_paddy = self.sample_directory + "/paddy/"
+            self.sample_directory_nchalky = self.sample_directory + "/nchalky/"
+            self.sample_directory_chalky = self.sample_directory + "/chalky/"
             os.mkdir(self.sample_directory_extracted_e)
             os.mkdir(self.sample_directory_extracted_p)
             print "Created: " + self.sample_directory_extracted

diff --git a/python2/chalky-classifier.py b/python2/chalky-classifier.py
@@ -9,12 +9,17 @@
 import numpy as np
 import pickle
 import os
+import shutil
 
 
 class Chalky_classifier():
     def __init__(self):
         self.__isCalibrated = False
         self.__chalky_range = []
+        self.test_files = []
+        self.data_X = []
+        self.data_Y = []
+        self.files = []
 
 
     def calibrate(self, chalky_range):
@@ -26,13 +31,29 @@ def calibrate(self, chalky_range):
                print("Range is invalid")
                self.__isCalibrated = False
                break
+
+    def add_grains(self, grain_directory):
+        files = os.listdir(grain_directory)
+        self.files_name = files
+        for file in files:
+            self.files.append(grain_directory+file)
+
+    def add_test_grains(self, grain_directory, data_class):
+        files = os.listdir(grain_directory)
+        for file in files:
+            self.test_files.append(grain_directory+file)
+            self.data_Y.append(data_class)
 
-    def add_test_dataset(self, grain_directory):
-        images = os.listdir(grain_directory)
+    def test_classify(self):
+        images = self.test_files
+        chalky = 0
+        TP = 0
+        FP = 0
+        TN = 0
+        FN = 0
         if self.__isCalibrated:
-            for image in images:
-                file_name = grain_directory + image
-                image_file = cv2.imread(file_name)
+            for i,image in enumerate(images):
+                image_file = cv2.imread(image)
 #                image_ = cv2.imread(file_name)
 #                mask_image = cv2.threshold(cv2.GaussianBlur(image_file, (7,7), 0),127,255, cv2.THRESH_OTSU + cv2.THRESH_BINARY)
 #                b = cv2.calcHist([image_], [0], mask_image[1], [256], [0,256])
@@ -42,32 +63,95 @@ def add_test_dataset(self, grain_directory):
 #                r = cv2.calcHist([image_], [2], mask_image[1], [256], [0,256])
 #                #r_perc = (sum(r[self.__chalky_range[0][0]:self.__chalky_range[0][1]])/sum(r))[0]
                 chalky_pixels = 0
+                total_pixels = 0
+
                 for row in image_file:
-                    for column in row:                        
-                        if(self.__within_range(column[0], self.__chalky_range[2]) and self.__within_range(column[1], self.__chalky_range[1]) and
-                                           self.__within_range(column[2], self.__chalky_range[0])):
-                            chalky_pixels += 1
+                    for column in row: 
+                        if(not column[0] == 0 and not column[1] == 0 and not column[2] == 0):
+                            total_pixels += 1 
+                            if(self.__within_range(column[0], self.__chalky_range[2]) and self.__within_range(column[1], self.__chalky_range[1]) and
+                                               self.__within_range(column[2], self.__chalky_range[0])):
+                                chalky_pixels += 1
 
-                total_pixels = image_file.shape[0] * image_file.shape[1]
-                print(chalky_pixels, total_pixels)
+                chalky_percentage = ((chalky_pixels/total_pixels)*100)
+                self.data_X.append(chalky_percentage)
+
+                if chalky_percentage >= 50:
+                    chalky += 1
+
+                if chalky_percentage >= 50 and self.data_Y[i] == 1:
+                    TP += 1
+
+                if chalky_percentage >= 50 and self.data_Y[i] == 0:
+                    FP += 1         
+
+                if chalky_percentage < 50 and self.data_Y[i] == 1:
+                    FN += 1
+
+                if chalky_percentage < 50 and self.data_Y[i] == 0:
+                    TN += 1
+
+        return [chalky, len(images), [TP, TN, FP, FN]]
 
     def __within_range(self, number, range_):
         if number > range_[0] and number < range_[1]:
             return True
         else:
             return False
-
+
+    def classify(self, class0_dir, class1_dir):
+        chalky = 0
+        if self.__isCalibrated:
+            images = self.files
+            for i,image in enumerate(images):
+                image_file = cv2.imread(image)
+                chalky_pixels = 0
+                total_pixels = 0
+
+                for row in image_file:
+                    for column in row: 
+                        if(not column[0] == 0 and not column[1] == 0 and not column[2] == 0):
+                            total_pixels += 1 
+                            if(self.__within_range(column[0], self.__chalky_range[2]) and self.__within_range(column[1], self.__chalky_range[1]) and
+                                               self.__within_range(column[2], self.__chalky_range[0])):
+                                chalky_pixels += 1
+
+                chalky_percentage = ((chalky_pixels/total_pixels)*100)
+
+                if chalky_percentage >= 50:
+                    chalky += 1
+                    shutil.copy(self.files[i], class1_dir+self.files_name[i])
+                else:
+                    shutil.copy(self.files[i], class0_dir+self.files_name[i])
+
+            return [chalky, len(images)]
+
+        else:
+            print("Range is not calibrated")
+
+
 
 
 
 if __name__ == "__main__":
     chalk = Chalky_classifier()
-    chalk.calibrate([[150,255],[150,255],[150,255]])
-    chalk.add_test_dataset("../img-src/40/extracted/d/")
-    img = cv2.imread("../img-src/40/extracted/d/p57.jpg")
-    cv2.imshow("h", img)
-    print(img[85,29])
-    cv2.waitKey(0)
-    cv2.destroyAllWindows()
+    chalk.calibrate([[190, 255],[190, 255],[180, 255]])
+    chalk.add_test_grains("../testing/chalky/data/chalky/", 1)
+    chalk.add_test_grains("../testing/chalky/data/nchalky/", 0)
+    chalk_results = chalk.test_classify()
+    print((chalk_results[2][0]+chalk_results[2][1])/sum(chalk_results[2]))
+#    print(chalk.data_X)
+#    print(chalk.data_Y)
+    chalk.add_grains("../img-src/52/extracted/p/")
+    chalk.classify("../img-src/52/nchalky/", "../img-src/52/chalky/")
+
+##    
+#    images = os.listdir("../img-src/52/extracted/p/j")
+#    for i,image in enumerate(images):
+#        img = cv2.imread("../img-src/52/extracted/p/j/"+image)
+#        cv2.imshow(str(i), img)
+#   
+#    cv2.waitKey(0)
+#    cv2.destroyAllWindows()
 
 
diff --git a/python3/utils/__pycache__/led.cpython-36.pyc b/python3/utils/__pycache__/led.cpython-36.pyc
diff --git a/python3/utils/modeler.py b/python3/utils/modeler.py
@@ -26,6 +26,42 @@ def __init__(self, isHOG):
         self.dataset_filenames_nodir_test = []
         self.dataset_test = []
         self.isHOG = isHOG
+        self.dataset_classification = []
+        self.dataset_files = []
+
+    def add_dataset(self, data_folder):
+        if os.path.exists(data_folder):
+            images = os.listdir(data_folder)
+
+            if self.isHOG:
+                HOG = cv2.HOGDescriptor()
+                for image in images:
+                    file_name = data_folder + image
+                    self.dataset_files.append(file_name)
+                    image = cv2.imread(file_name)
+                    image_hog = HOG.compute(image)
+                    self.dataset_classification.append(image_hog)
+
+            elif self.isHOG == False:
+
+                for image in images:
+                    img_hist = np.zeros([])
+                    file_name = data_folder + image
+                    self.dataset_files.append(file_name)
+                    image = cv2.imread(file_name)
+                    mask_image = cv2.threshold(cv2.GaussianBlur(cv2.imread(file_name, 0), (7,7), 0),127,255, cv2.THRESH_OTSU + cv2.THRESH_BINARY)
+                    r = cv2.calcHist([image], [0], mask_image[1], [256], [0,256])
+                    r = r/max(r)
+                    g = cv2.calcHist([image], [1], mask_image[1], [256], [0,256])
+                    g = g/max(g)
+                    b = cv2.calcHist([image], [2], mask_image[1], [256], [0,256])
+                    b = b/max(b)
+
+                    img_hist = np.concatenate((r,g,b))
+
+                    self.dataset_classification(img_hist)
+
+
 
     def add_model_dataset(self, data_folder, data_class):
         if os.path.exists(data_folder):
@@ -141,8 +177,11 @@ def load_dataset(self, dataset_folder):
             return pickle.load(dataset)
 
     #def test(class1_dest, class0_dest, dataset=self.dataset_test, model=self.__model):
-
-    def classify(self, class0_dest, class1_dest):
+    def set_model(self, model_dir):
+        with open(model_dir) as model:
+            self.__model = pickle.load(model)
+
+    def test_classify(self, class0_dest, class1_dest):
         count = 0
         correct = 0
 
@@ -175,6 +214,21 @@ def classify(self, class0_dest, class1_dest):
                 FP += 1
 
         return [results, self.dataset_filenames_test, [correct, count], [TP, TN, FP, FN]]
+
+    def classify(self, class0_dir, class1_dir):
+        if self.__model != None:
+            if self.isHOG:
+                self.dataset_classification = np.array(self.dataset_classification).reshape(-1,3780)
+
+            predictions = self.__model.predict(self.dataset_classification)
+            print(predictions)
+
+            for i,prediction in enumerate(predictions):
+                if prediction == 1:
+                    shutil.copy(self.dataset_files[i], class1_dir)
+                else:
+                    shutil.copy(self.dataset_files[i], class0_dir)
+
 
 
     #def add_test_dataset(self): 
@@ -188,44 +242,49 @@ def classify(self, class0_dest, class1_dest):
     bkn.add_test_dataset("../../testing/bkn/data/bkn/", 0)
     bkn.add_test_dataset("../../testing/bkn/data/unbkn/", 1)
     bkn.build_test_dataset("../../testing/bkn/bins/", "bkn_test_dataset")
-    results = bkn.classify("../../testing/bkn/results/bkn/", "../../testing/bkn/results/unbkn/")
+    results = bkn.test_classify("../../testing/bkn/results/bkn/", "../../testing/bkn/results/unbkn/")
     print(results[2][0]/results[2][1])
     print(results[3])
 
-    ylw = Classifier(False)
-    ylw.add_model_dataset("../../training/ylw/data/ylw/", 1)
-    ylw.add_model_dataset("../../training/ylw/data/nylw/", 0)
-    ylw.build_model_dataset("../../training/ylw/bins/", "ylw_dataset")
-    ylw.build_model("../../training/ylw/bins/", "model_ylw")
-    ylw.add_test_dataset("../../testing/ylw/data/ylw/", 1)
-    ylw.add_test_dataset("../../testing/ylw/data/nylw/", 0)
-    ylw.build_test_dataset("../../testing/ylw/bins/", "ylw_test_dataset")
-    results = ylw.classify("../../testing/ylw/results/ylw/", "../../testing/ylw/results/nylw/")
-    print(results[2][0]/results[2][1])
-    print(results[3])
+    bkn.add_dataset("../../img-src/52/extracted/p/")
+    bkn.classify("../../img-src/52/bkn/", "../../img-src/52/nbkn")
+
+#    ylw = Classifier(False)
+#    ylw.add_model_dataset("../../training/ylw/data/ylw/", 1)
+#    ylw.add_model_dataset("../../training/ylw/data/nylw/", 0)
+#    ylw.build_model_dataset("../../training/ylw/bins/", "ylw_dataset")
+#    ylw.build_model("../../training/ylw/bins/", "model_ylw")
+#    ylw.add_test_dataset("../../testing/ylw/data/ylw/", 1)
+#    ylw.add_test_dataset("../../testing/ylw/data/nylw/", 0)
+#    ylw.build_test_dataset("../../testing/ylw/bins/", "ylw_test_dataset")
+#    results = ylw.test_classify("../../testing/ylw/results/ylw/", "../../testing/ylw/results/nylw/")
+#    print(results[2][0]/results[2][1])
+#    print(results[3])
+#    
+#    grn = Classifier(False)
+#    grn.add_model_dataset("../../training/grn/data/grn/", 1)
+#    grn.add_model_dataset("../../training/grn/data/ngrn/", 0)
+#    grn.build_model_dataset("../../training/grn/bins/", "grn_dataset")
+#    grn.build_model("../../training/grn/bins/", "model_grn")
+#    grn.add_test_dataset("../../testing/grn/data/grn/", 1)
+#    grn.add_test_dataset("../../testing/grn/data/ngrn/", 0)
+#    grn.build_test_dataset("../../testing/grn/bins/", "grn_test_dataset")
+#    results = grn.test_classify("../../testing/grn/results/grn/", "../../testing/grn/results/ngrn/")
+#    print(results[2][0]/results[2][1])
+#    print(results[3])
+##   
+#    paddy = Classifier(False)
+#    paddy.add_model_dataset("../../training/paddy/data/paddy/", 1)
+#    paddy.add_model_dataset("../../training/paddy/data/npaddy/", 0)
+#    paddy.build_model_dataset("../../training/paddy/bins/", "paddy_dataset")
+#    paddy.build_model("../../training/paddy/bins/", "model_paddy")
+#    paddy.add_test_dataset("../../testing/paddy/data/paddy/", 1)
+#    paddy.add_test_dataset("../../testing/paddy/data/npaddy/", 0)
+#    paddy.build_test_dataset("../../testing/paddy/bins/", "paddy_test_dataset")
+#    results = paddy.test_classify("../../testing/paddy/results/paddy/", "../../testing/paddy/results/npaddy/")
+#    print(results[2][0]/results[2][1])
+#    print(results[3])
+
 
-    grn = Classifier(False)
-    grn.add_model_dataset("../../training/grn/data/grn/", 1)
-    grn.add_model_dataset("../../training/grn/data/ngrn/", 0)
-    grn.build_model_dataset("../../training/grn/bins/", "grn_dataset")
-    grn.build_model("../../training/grn/bins/", "model_grn")
-    grn.add_test_dataset("../../testing/grn/data/grn/", 1)
-    grn.add_test_dataset("../../testing/grn/data/ngrn/", 0)
-    grn.build_test_dataset("../../testing/grn/bins/", "grn_test_dataset")
-    results = grn.classify("../../testing/grn/results/grn/", "../../testing/grn/results/ngrn/")
-    print(results[2][0]/results[2][1])
-    print(results[3])
-#   
-    paddy = Classifier(False)
-    paddy.add_model_dataset("../../training/paddy/data/paddy/", 1)
-    paddy.add_model_dataset("../../training/paddy/data/npaddy/", 0)
-    paddy.build_model_dataset("../../training/paddy/bins/", "paddy_dataset")
-    paddy.build_model("../../training/paddy/bins/", "model_paddy")
-    paddy.add_test_dataset("../../testing/paddy/data/paddy/", 1)
-    paddy.add_test_dataset("../../testing/paddy/data/npaddy/", 0)
-    paddy.build_test_dataset("../../testing/paddy/bins/", "paddy_test_dataset")
-    results = paddy.classify("../../testing/paddy/results/paddy/", "../../testing/paddy/results/npaddy/")
-    print(results[2][0]/results[2][1])
-    print(results[3])
 
 
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