added color feature and raster_image | adjusted crop and remove_background

.gitignore

@@ -2,4 +2,7 @@
 .vsc
 .venv
 
-data
+data
+
+# ignore all files in src that start with an underscore
+src/**/_* 

src/featue_extraction.py

@@ -1,8 +1,56 @@
-class FeatureExtractor:
+import cv2 as cv
+import numpy as np
 
-	def get_overall_color_percentage():
+blue_lower_hsv = np.array([90, 75, 75])
-		pass
+blue_upper_hsv = np.array([130, 255, 255])
 
-	def get_color_per_raster_percentage():
+red_lower1_hsv = np.array([0, 75, 75])
-		pass
+red_upper1_hsv = np.array([10, 255, 255])
+red_lower2_hsv = np.array([170, 75, 75])
+red_upper2_hsv = np.array([179, 255, 255])
+
+yellow_lower_hsv = np.array([15, 75, 75])
+yellow_upper_hsv = np.array([40, 255, 255])
+
+white_lower_hsv = np.array([0, 0, 190])
+white_upper_hsv = np.array([180, 65, 255])
+
+black_lower_hsv = np.array([0, 0, 1]) # because 0 0 0 pixels are ignored as background
+black_upper_hsv = np.array([180, 255, 30])
+
+def get_color_percentage(image: cv.Mat) -> dict:
+	hsv = cv.cvtColor(image, cv.COLOR_BGR2HSV)
+
+	mask_red1 = cv.inRange(hsv, red_lower1_hsv, red_upper1_hsv)
+	mask_red2 = cv.inRange(hsv, red_lower2_hsv, red_upper2_hsv)
+	mask_red = mask_red1 | mask_red2
+	mask_blue = cv.inRange(hsv, blue_lower_hsv, blue_upper_hsv)
+	mask_yellow = cv.inRange(hsv, yellow_lower_hsv, yellow_upper_hsv)
+	mask_white = cv.inRange(hsv, white_lower_hsv, white_upper_hsv)
+	mask_black = cv.inRange(hsv, black_lower_hsv, black_upper_hsv)
+
+	width, height = image.shape[:2]
+	image_pixels = width*height
+
+	red_pixels = cv.countNonZero(mask_red) 
+	blue_pixels = cv.countNonZero(mask_blue) 
+	yellow_pixels = cv.countNonZero(mask_yellow) 
+	white_pixels = cv.countNonZero(mask_white)
+	black_pixels = cv.countNonZero(mask_black)
+	
+	red_percentage = int((red_pixels / image_pixels) * 100)
+	blue_percentage = int((blue_pixels / image_pixels) * 100)
+	yellow_percentage = int((yellow_pixels / image_pixels) * 100)
+	white_percentage = int((white_pixels / image_pixels) * 100)
+	black_percentage = int((black_pixels / image_pixels) * 100)
+	none_percentage = 100 - red_percentage - blue_percentage - yellow_percentage - white_percentage - black_percentage
+
+	return {
+		"red": red_percentage,
+		"blue": blue_percentage,
+		"yellow": yellow_percentage,
+		"white": white_percentage,
+		"black": black_percentage,
+		"none": none_percentage
+	}
 

src/feature_vector.py

@@ -1,5 +1,8 @@
 from typing import Any
+import os
+import pickle
 
+from feature_vector import FeatureVector
 from concept import Concept
 
 
@@ -24,4 +27,18 @@ class FeatureVector:
 
 	def get_feature_value(self, key) -> Any:
 		return self.features[key]
+	
+
+	def save(self, path: os.path) -> None:
+		with open(path, "wb") as write:
+			pickle.dump(self, write, pickle.HIGHEST_PROTOCOL)
+
+
+	def load(self, path: os.path) -> FeatureVector:
+		with open(path, "rb") as read:
+			feature_vector: FeatureVector = pickle.load(read)
+		self.features = feature_vector.features
+		self.concept = feature_vector.concept
+		return feature_vector
+
 

src/image_processing.py

@@ -1,14 +1,17 @@
 import cv2 as cv
 import numpy as np
 
-def crop(image: cv.Mat) -> cv.Mat:	
+def crop(image: cv.Mat, lower_rgb: np.ndarray, upper_rgb: np.ndarray) -> cv.Mat:	
 	width, height = image.shape[:2]
 
 	x1 = None
 	for x in range(width):
 		for y in range(height):
 			b, g, r = image[x][y]
-			if(r < 150 and g < 150 and b < 150):
+			if(r <= lower_rgb[0] and r >= upper_rgb[0] and
+	  		   g <= lower_rgb[1] and g >= upper_rgb[1] and
+			   b <= lower_rgb[2] and b >= upper_rgb[2]
+			):
 				x1 = x
 				break
 		if x1 is not None:
@@ -18,7 +21,10 @@ def crop(image: cv.Mat) -> cv.Mat:
 	for y in range(height):
 		for x in range(width):
 			b, g, r = image[x][y]
-			if(r < 150 and g < 150 and b < 150):
+			if(r <= lower_rgb[0] and r >= upper_rgb[0] and
+	  		   g <= lower_rgb[1] and g >= upper_rgb[1] and
+			   b <= lower_rgb[2] and b >= upper_rgb[2]
+			):
 				y1 = y
 				break
 		if y1 is not None:
@@ -28,7 +34,10 @@ def crop(image: cv.Mat) -> cv.Mat:
 	for x in range(width-1, 0, -1):
 		for y in range(height):
 			b, g, r = image[x][y]
-			if(r < 150 and g < 150 and b < 150):
+			if(r <= lower_rgb[0] and r >= upper_rgb[0] and
+	  		   g <= lower_rgb[1] and g >= upper_rgb[1] and
+			   b <= lower_rgb[2] and b >= upper_rgb[2]
+			):
 				x2 = x
 				break
 		if x2 is not None:
@@ -38,7 +47,10 @@ def crop(image: cv.Mat) -> cv.Mat:
 	for y in range(height-1, 0, -1):
 		for x in range(width):
 			b, g, r = image[x][y]
-			if(r < 150 and g < 150 and b < 150):
+			if(r <= lower_rgb[0] and r >= upper_rgb[0] and
+	  		   g <= lower_rgb[1] and g >= upper_rgb[1] and
+			   b <= lower_rgb[2] and b >= upper_rgb[2]
+			):
 				y2 = y
 				break
 		if y2 is not None:
@@ -47,26 +59,29 @@ def crop(image: cv.Mat) -> cv.Mat:
 	return image[x1-2:x2+2, y1-2:y2+2]
 
 
-def remove_background(image: cv.Mat, lower_rgb: np.ndarray) -> cv.Mat:
+def remove_background(image: cv.Mat, lower_rgb: np.ndarray, upper_rgb: np.ndarray) -> cv.Mat:
 	width, height = image.shape[:2]
 
 	# left to right
 	for x in range(width):
 		for y in range(height):
 			b, g, r = image[x][y]
-			if(r >= lower_rgb[0] and g >= lower_rgb[1] and b >= lower_rgb[2]):
+			if(r >= lower_rgb[0] and r <= upper_rgb[0] and
+	  		   g >= lower_rgb[1] and g <= upper_rgb[1] and
+			   b >= lower_rgb[2] and b <= upper_rgb[2]
+			):
 				image[x][y] = [0, 0, 0]
 			else:
 				break
 	
-	# top to bottom
+	# # top to bottom
-	for y in range(height):
+	# for y in range(height):
-		for x in range(width):
+	# 	for x in range(width):
-			b, g, r = image[x][y]
+	# 		b, g, r = image[x][y]
-			if(r >= lower_rgb[0] and g >= lower_rgb[1] and b >= lower_rgb[2]):
+	# 		if(r >= lower_rgb[0] and g >= lower_rgb[1] and b >= lower_rgb[2]):
-				image[x][y] = [0, 0, 0]
+	# 			image[x][y] = [0, 0, 0]
-			else:
+	# 		else:
-				break
+	# 			break
 
 	# right to left
 	for x in range(width):
@@ -78,16 +93,30 @@ def remove_background(image: cv.Mat, lower_rgb: np.ndarray) -> cv.Mat:
 				break
 	
 	# bottom to top
-	for y in range(height):
+	# for y in range(height):
-		for x in range(width-1, 0, -1):
+	# 	for x in range(width-1, 0, -1):
-			b, g, r = image[x][y]
+	# 		b, g, r = image[x][y]
-			if(r >= lower_rgb[0] and g >= lower_rgb[1] and b >= lower_rgb[2]):
+	# 		if(r >= lower_rgb[0] and g >= lower_rgb[1] and b >= lower_rgb[2]):
-				image[x][y] = [0, 0, 0]
+	# 			image[x][y] = [0, 0, 0]
-			else:
+	# 		else:
-				break
+	# 			break
 
 	return image
 
 
-def raster_image(image: cv.Mat, num_cols: int = 4, num_rows: int = 4) -> dict[cv.Mat]:
+def raster_image(image: cv.Mat, num_cols: int = 4, num_rows: int = 4) -> list[list[cv.Mat]]:
-	pass
+	width, height = image.shape[:2]
+	print("w", width, " h", height)
+	rasters = [[0 for x in range(num_rows)] for y in range(num_cols)]
+	print(rasters)
+	for y in range(num_cols):
+		for x in range(num_rows):
+			x1 = int(x * (width/num_rows))
+			y1 = int(y * (height/num_cols))
+			x2 = int((x+1) * (width/num_rows))
+			y2 = int((y+1) * (height/num_cols))
+			print(" x1", x1, " y1", y1, " x2", x2, " y2", y2)
+
+			rasters[x][y] = image[x1:x2, y1:y2]
+
+	return rasters