diff options
Diffstat (limited to 'scripts/PYTHON_SOFTWARE/test/helpers/cv_display_test.py')
| -rw-r--r-- | scripts/PYTHON_SOFTWARE/test/helpers/cv_display_test.py | 147 |
1 files changed, 0 insertions, 147 deletions
diff --git a/scripts/PYTHON_SOFTWARE/test/helpers/cv_display_test.py b/scripts/PYTHON_SOFTWARE/test/helpers/cv_display_test.py deleted file mode 100644 index 7321667..0000000 --- a/scripts/PYTHON_SOFTWARE/test/helpers/cv_display_test.py +++ /dev/null @@ -1,147 +0,0 @@ -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 |