added corners,edges,counters to FV

edges-corners-counters.py

@@ -1,50 +0,0 @@
-import cv2
-import numpy as np
-from os import walk
-import csv
-
-def detect_corners(gray_image, max_corners=100, quality_level=0.14, min_distance=1):
-    corners = cv2.goodFeaturesToTrack(cv2.blur(gray_image, (5,5)), maxCorners=max_corners, qualityLevel=quality_level, minDistance=min_distance)
-    if corners is not None:
-        corners = np.int0(corners)  
-        return len(corners), corners
-    else:
-        return 0, []
-
-def detect_edges(gray_image):
-    edges = cv2.Canny(cv2.blur(gray_image, (5,5)), threshold1=100, threshold2=200)
-    edge_count = np.count_nonzero(edges)
-    return edge_count, edges
-
-
-
-def detect_contours(edge_image):
-    contours, _ = cv2.findContours(edge_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-    contour_count = len(contours)
-    return contour_count, contours
-
-
-def feature_vector(image_path):
-    resultVector = {}
-    img = cv2.imread(image_path)
-    image_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-
-    corners_count, _ = detect_corners(image_gray)
-    edges_count, edges = detect_edges(image_gray)
-    contours_count, _ = detect_contours(edges)   
-
-    resultVector["corners"] = corners_count % 100
-    resultVector["edges"] = edges_count / 1000
-    resultVector["contours"] = contours_count * 0.1
-
-    return resultVector
-
-
-filenames = next(walk("images"), (None, None, []))[2]  # [] if no file
-
-with open('dict.csv', 'w') as csv_file:  
-    writer = csv.DictWriter(csv_file, fieldnames=['corners', 'edges', 'contours'])
-    writer.writeheader()
-    for filename in filenames: 
-        result = feature_vector(f'images/{filename}')
-        writer.writerow(result)

requirements.txt

@@ -1 +1,2 @@
-opencv-python
+opencv-python
+np

src/create_feature_vectors.py

@@ -1,13 +1,14 @@
 import os
+import csv
 import cv2 as cv
 
-from featue_extraction import get_color_percentage, get_raster_color_percentage
+from featue_extraction import get_color_percentage, get_raster_color_percentage, get_edges, get_corners, get_contours
 from feature_vector import FeatureVector
 from concept import Concept
 from feature import Feature
 
-img_path = "D:/uni/master/machine_learning/StreetsignRecognition/data/processed"
-vector_path = "D:/uni/master/machine_learning/StreetsignRecognition/data/vectors"
+img_path = "/Users/denysseredenko/StreetsignRecognition/data"
+vector_path = "/Users/denysseredenko/StreetsignRecognition/vector"
 
 def get_concept(path) -> Concept:
     if "fahrtrichtung_links" in path:
@@ -36,26 +37,35 @@ if __name__ == "__main__":
 
         for fname in fnames:
             image_path = os.path.join(dirpath, fname)
+            print(image_path)
             try:
-                # extract features
+                # prepare image
                 image = cv.imread(image_path)
+                gray_image = cv.imread(image_path, cv.IMREAD_GRAYSCALE)
+                #extract features
                 color_percentage_overall = get_color_percentage(image)
                 color_percentage_rasters = get_raster_color_percentage(image, 4, 4)
-                # add more
+                edges_amount, edged_image = get_edges(gray_image)
+                edges_amount = round(edges_amount / 1000)
+                corners_amount = get_corners(gray_image)
+                contours_amount = get_contours(edged_image)
 
             except Exception:
                 print("Failed for ", image_path)
                 continue
-            
+                
             concept: Concept = get_concept(image_path)
             if concept == Concept.UNKNOWN:
                 print("no concept found for ", image_path)
                 continue
 
             vec: FeatureVector = FeatureVector(concept)
+            vec.add_feature(Feature.CORNERS, corners_amount)
+            vec.add_feature(Feature.EDGES, edges_amount)
+            vec.add_feature(Feature.COUNTOURS, contours_amount)
             vec.add_feature(Feature.OVERALL_COLOR_PERCENTAGE, color_percentage_overall)
             vec.add_feature(Feature.RASTER_COLOR_PERCENTAGE, color_percentage_rasters)
-            # add more
+
             print(vec.get_vector())
-            vec_path = os.path.join(save_path, fname)
+            vec_path = os.path.join(save_path, fname.split('.')[0])
             vec.save(vec_path)

src/featue_extraction.py

@@ -67,3 +67,23 @@ def get_raster_color_percentage(image: cv.Mat, rows: int, cols: int) -> list[lis
 		ret.append(new)
 	return ret
 
+def get_edges(gray_image: cv.Mat):
+    edges = cv.Canny(cv.blur(gray_image, (5,5)), threshold1=100, threshold2=200)
+    edge_count = np.count_nonzero(edges)
+    return edge_count, edges
+
+def get_corners(gray_image: cv.Mat):
+    max_corners  = 100
+    quality_level = 0.14
+    min_distance = 1
+    corners = cv.goodFeaturesToTrack(cv.blur(gray_image, (5,5)), maxCorners=max_corners, qualityLevel=quality_level, minDistance=min_distance)
+    if corners is not None:
+        corners = np.int0(corners)  
+        return len(corners)
+    else:
+        return 0
+    
+def get_contours(edged_gray_image: cv.Mat):
+    contours, _ = cv.findContours(edged_gray_image, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
+    contour_count = len(contours)
+    return contour_count

src/feature.py

@@ -5,3 +5,4 @@ class Feature(Enum):
 	RASTER_COLOR_PERCENTAGE = auto()
 	CORNERS = auto()
 	EDGES = auto()
+	COUNTOURS = auto()

src/feature_vector.py

@@ -26,7 +26,6 @@ class FeatureVector:
 			feature_data: dict
 			for val in feature_data.values():
 				ret.append(val)
-			print(ret)
 			return ret
 		
 		elif feature == Feature.RASTER_COLOR_PERCENTAGE:
@@ -36,15 +35,17 @@ class FeatureVector:
 				for data in feature_data1d:
 					for val in data.values():
 						ret.append(val)
-			print(ret)
 			return ret
 		
 		elif feature == Feature.CORNERS:
-			#feature_data: int #?
-			return [-1]
+			ret.append(feature_data)
+			return ret
 		elif feature == Feature.EDGES:
-			#feature_data: int #?
-			return [-1]
+			ret.append(feature_data)
+			return ret
+		elif feature == Feature.COUNTOURS:
+			ret.append(feature_data)
+			return ret
 
 
 	def add_feature(self, key: Feature, value: Any) -> None: