Added shuffling and evaluation

imageElaboration.py

@@ -1,47 +1,49 @@
-import numpy as np
-import cv2
-from utilities import *
-
-
-# This function applies all elaboration steps to the image
-def elaborateImage(newFrame):
-    # Adjusting brightness and contrast
-    newFrameAdjusted = apply_brightness_contrast(newFrame, 90, 90)
-
-    # Threshold so that only yellow and white are kept. Result is greyscale
-    newFrameThreshold = thresholdWhiteAndYellow(newFrameAdjusted)
-
-    # Apply Gaussian blur to reduce noise
-    newFrameBlurred = cv2.GaussianBlur(newFrameThreshold, (5, 5), 0)
-
-    # Applying canny edge detection
-    newFrameEdges = cv2.Canny(newFrameBlurred, 100, 200)
-
-    # Cutting a region of interest
-    height, width = newFrameEdges.shape
-    # Creating white polygonal shape on black image
-    bottomLeft = [10, height - 130]
-    topLeft = [width / 3 + 60, height / 2]
-    topRight = [width * 2 / 3 - 60, height / 2]
-    bottomRight = [width - 10, height - 130]
-    pts = np.array([bottomLeft, topLeft, topRight, bottomRight], np.int32)
-    pts = pts.reshape((-1, 1, 2))
-    blackImage = np.zeros((height, width, 1), np.uint8)
-    polygonalShape = cv2.fillPoly(blackImage, [pts], (255, 255, 255))
-    # Doing AND operation with newFrameEdges
-    newFrameROI = cv2.bitwise_and(newFrameEdges, newFrameEdges, mask=polygonalShape)
-
-    # Hough transform to detect straight lines. Returns an array of r and theta values
-    lines = cv2.HoughLinesP(newFrameROI, 1, np.pi / 180, 15)
-    blackImage = np.zeros((height, width, 1), np.uint8)
-    newFrameHough = drawHoughTransformLines(blackImage, lines)
-
-    # Drawing road from original frame
-    newFrameGrey = cv2.cvtColor(newFrameAdjusted, cv2.COLOR_BGR2GRAY)
-    coloredMaskedRoad = cv2.bitwise_and(newFrameGrey, newFrameGrey, mask=polygonalShape)
-    newFrameMaskAndRoad = cv2.add(coloredMaskedRoad, newFrameROI)   # Adding canny edge overlay to highlight the lane markers
-
-    # Cutting image basing on mask size
-    result = cutTopAndBottom(coloredMaskedRoad, int(height / 2), int(height - 130))
-
+import numpy as np
+import cv2
+from utilities import *
+
+
+# This function applies all elaboration steps to the image
+def elaborateImage(newFrame):
+    # Adjusting brightness and contrast
+    newFrameAdjusted = apply_brightness_contrast(newFrame, 90, 90)
+
+    # Threshold so that only yellow and white are kept. Result is greyscale
+    newFrameThreshold = thresholdWhiteAndYellow(newFrameAdjusted)
+
+    # Apply Gaussian blur to reduce noise
+    newFrameBlurred = cv2.GaussianBlur(newFrameThreshold, (5, 5), 0)
+
+    # Applying canny edge detection
+    newFrameEdges = cv2.Canny(newFrameBlurred, 100, 200)
+
+    # Cutting a region of interest
+    height, width = newFrameEdges.shape
+    # Creating white polygonal shape on black image
+    bottomLeft = [0, height - 130]
+    topLeft = [10, height / 2 - 15]
+    topRight = [width -30, height / 2 - 15]
+    bottomRight = [width, height - 130]
+    pts = np.array([bottomLeft, topLeft, topRight, bottomRight], np.int32)
+    pts = pts.reshape((-1, 1, 2))
+    blackImage = np.zeros((height, width, 1), np.uint8)
+    polygonalShape = cv2.fillPoly(blackImage, [pts], (255, 255, 255))
+    # Doing AND operation with newFrameEdges
+    newFrameROI = cv2.bitwise_and(newFrameEdges, newFrameEdges, mask=polygonalShape)
+
+    # Hough transform to detect straight lines. Returns an array of r and theta values
+    lines = cv2.HoughLinesP(newFrameROI, 1, np.pi / 180, 15)
+    blackImage = np.zeros((height, width, 1), np.uint8)
+    newFrameHough = drawHoughTransformLines(blackImage, lines)
+
+    # Drawing road from original frame
+    newFrameGrey = cv2.cvtColor(newFrame, cv2.COLOR_BGR2GRAY)
+    coloredMaskedRoad = cv2.bitwise_and(newFrameGrey, newFrameGrey, mask=polygonalShape)
+    #coloredMaskedRoad = cv2.equalizeHist(coloredMaskedRoad)
+    newFrameMaskAndRoad = cv2.add(coloredMaskedRoad, newFrameROI)   # Adding canny edge overlay to highlight the lane markers
+    newFrameMaskAndRoadBlurred = cv2.GaussianBlur(newFrameMaskAndRoad, (5, 5), 0)
+
+    # Cutting image basing on mask size
+    result = cutTopAndBottom(coloredMaskedRoad, int(height / 2 - 15), int(height - 130))
+
     return result