Board: Client Test Suite Initial Commit

1 files changed, 147 insertions, 0 deletions

diff --git a/scripts/PYTHON_SOFTWARE/test/helpers/cv_display_test.py b/scripts/PYTHON_SOFTWARE/test/helpers/cv_display_test.py
new file mode 100644
index 0000000..7321667
--- /dev/null
+++ b/scripts/PYTHON_SOFTWARE/test/helpers/cv_display_test.py
@@ -0,0 +1,147 @@
+import cv2
+import numpy as np
+import subprocess
+
+
+
+
+def adjust_gamma(image, gamma=1.0):
+    invGamma = 1.0 / gamma
+    table = np.array([((i / 255.0) ** invGamma) * 255
+                          for i in np.arange(0, 256)]).astype("uint8")
+    return cv2.LUT(image, table)
+
+def pattern_detect(cam_device=0):
+
+    # RETURN 0 only if the test is ok
+    msg="0"
+    # Capture the corresponding camera device [0,1]
+    #capture = cv2.VideoCapture(0)
+    camid = subprocess.check_output("ls /dev/v4l/by-id/usb-Creative_Technology*", stderr=subprocess.STDOUT, shell=True)
+    camid = camid.decode('ascii').rstrip()
+    capture = cv2.VideoCapture(camid)
+    try:
+        _, image = capture.read()
+    except:
+        msg="Camera error"
+        print(msg)
+        return msg
+
+    # If the USB cam is not connected the capture image doesn't not have size atribbute
+    if not hasattr(image, 'shape'):
+        msg = "USB camera connection error"
+        print(msg)
+        return msg
+    else:
+        size_img=image.shape
+    # Create little squares of each color section
+    # The size of the suare will be 1/6 of the total Y size on the y axis
+    # and 1/25 of the total X size on the x axis
+    y1 = int(size_img[0] / 2 - size_img[0] / 12)
+    y2 = int(size_img[0] / 2 + size_img[0] / 12)
+
+    # Square for RED COLOR
+    xr1 = int(size_img[1] / 6 - size_img[1] / 25)
+    xr2 = int(size_img[1] / 6 + size_img[1] / 25)
+    red_cal = image[y1:y2, xr1:xr2]
+    # Square for GREEN COLOR
+    xg1 = int(size_img[1] / 2 - size_img[1] / 25)
+    xg2 = int(size_img[1] / 2 + size_img[1] / 25)
+    green_cal = image[y1:y2, xg1:xg2]
+    # Square for BLUE COLOR
+    xb1 = int(5 * size_img[1] / 6 - size_img[1] / 25)
+    xb2 = int(5 * size_img[1] / 6 + size_img[1] / 25)
+    blue_cal = image[y1:y2, xb1:xb2]
+    # Get the average color in the box as repeting the np.average funtion
+    # Average color for red
+    avg_color_per_row = np.average(red_cal, axis=0)
+    avg_color_rawr = np.average(avg_color_per_row, axis=0)
+    # Average color for green
+    avg_color_per_row = np.average(green_cal, axis=0)
+    avg_color_rawg = np.average(avg_color_per_row, axis=0)
+    # Average color for blue
+    avg_color_per_row = np.average(blue_cal, axis=0)
+    avg_color_rawb = np.average(avg_color_per_row, axis=0)
+
+    # In cas  of the illumintion is not correct, it is performed a gamma filter in order to
+    # correct this illumination problem
+    # After testing the gamma factor correction is computed with the green color
+    # gamma = gamma_factor / blue+red into green part
+    # gamma factor = 50-100
+    #gamma = (100 / (avg_color_rawg[0] + avg_color_rawg[2] + 1))
+    gamma=1
+    # Adjust the image acording to this gamma value
+    adjusted = adjust_gamma(image, gamma=gamma)
+#    adjusted=image
+    cv2.imwrite( "/home/root/result_hdmi_img.jpg", adjusted);
+    # Calculate again the average color using the gamma adjusted image
+    # Crop the gamma adjusted image for wach color section
+    red_cal = adjusted[y1:y2, xr1:xr2]
+    green_cal = adjusted[y1:y2, xg1:xg2]
+    blue_cal = adjusted[y1:y2, xb1:xb2]
+    # Calculate the average for the red
+    avg_color_per_row = np.average(red_cal, axis=0)
+    avg_color_red = np.average(avg_color_per_row, axis=0)
+    # Calculate the average for the green
+    avg_color_per_row = np.average(green_cal, axis=0)
+    avg_color_green = np.average(avg_color_per_row, axis=0)
+    # Calculate the average for the blue
+    avg_color_per_row = np.average(blue_cal, axis=0)
+    avg_color_blue = np.average(avg_color_per_row, axis=0)
+    # In order to count colour use the hsv conversion
+    hsv = cv2.cvtColor(adjusted, cv2.COLOR_BGR2HSV)
+    # Create a mask for each color, definig the upper and lower bound of each color in hsv space
+    # Create the blue mask with the bounds
+    lower_blue = np.array([100, 50, 50])
+    upper_blue = np.array([130, 255, 255])
+    mask_b = cv2.inRange(hsv, lower_blue, upper_blue)
+    # Create the green mask with the bounds
+    lower_green = np.array([50, 50, 50])
+    upper_green = np.array([80, 255, 255])
+    mask_g = cv2.inRange(hsv, lower_green, upper_green)
+    # Create the red mask with the bounds. In this case we use a composed mask, sum of two different
+    # group of bounds
+    # First red mask
+    lower_red = np.array([0, 50, 50])
+    upper_red = np.array([10, 255, 255])
+    mask0 = cv2.inRange(hsv, lower_red, upper_red)
+    # Second red mask
+    lower_red = np.array([170, 50, 50])
+    upper_red = np.array([180, 255, 255])
+    mask1 = cv2.inRange(hsv, lower_red, upper_red)
+    # compose both masks
+    mask_r = mask0 + mask1
+    # mask_r = cv2.inRange(hsv, lower_red, upper_red)
+    # Perform a morphological open to expand
+#    kernel = np.ones((5, 5), np.uint8)
+#    closing_r = cv2.morphologyEx(mask_r, cv2.MORPH_OPEN, kernel)
+#    closing_b = cv2.morphologyEx(mask_b, cv2.MORPH_OPEN, kernel)
+#    closing_g = cv2.morphologyEx(mask_g, cv2.MORPH_OPEN, kernel)
+    # Count the number of pixels that are not of the corresponding color (black)
+#    count_r = cv2.countNonZero(closing_r)
+#    count_b = cv2.countNonZero(closing_b)
+#    count_g = cv2.countNonZero(closing_g)
+    #-----------
+    count_r = cv2.countNonZero(mask_r)
+    count_b = cv2.countNonZero(mask_b)
+    count_g = cv2.countNonZero(mask_g)
+    #-------
+    if (count_r < 5):
+        msg = "RED COUNT FAIL"
+        return msg
+    if (count_g < 5):
+        msg = "GREEN COUNT FAIL"
+        return msg
+    if (count_b < 5):
+        msg = "BLUE COUNT FAIL"
+        return msg
+    if (avg_color_red[2] < 150 or avg_color_rawr[2] < 200):
+        msg = "AVG RED COUNT FAIL"
+        return msg
+    if (avg_color_green[1] < 200 or avg_color_rawg[1] < 200):
+        msg = "AVG GREEN COUNT FAIL"
+        return msg
+    if (avg_color_blue[0] < 200 or avg_color_rawb[0] < 200):
+        msg = "AVG BLUE COUNT FAIL"
+        return msg
+    return msg