222 lines
13 KiB
Python
222 lines
13 KiB
Python
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# Date: 18.12.2021
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# Author: Kenan Gömek
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# Das Skript stellt die unterschiedlichen Farbkanäle der aufgenommenen Bilder dar und erstellt schnitte, um die Pixelwerte anzuzeigen.
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# Update:21.12.2021
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# Update Comment: Dominierender Farbkanal wird automatisch erkannt und die Zeilen- und Spaltenmarkierungen werden alle nach dem hellsten
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# Punkt im dominierenden Farbkanal gerichtet
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# Import needed Packages
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import cv2 as cv
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import numpy as np
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import time
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from datetime import datetime
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import os
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from matplotlib import pyplot as plt
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# define User Input
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# No vowels in path name!'
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PATH_IMAGE_FOLDER = r'U:\bwsyncshare\Auswertung'
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COMMENT = "" # User Comment for plot
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# select cut mode for determining pixel values
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cut_mode = 'auto' # options: 'auto', 'manual'
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ROW = 152 # Row-Number for Cut [Pixels] for cut_mode 'manual' index begins from 0
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COLUMN = 1742 # Column-Number for Cut [Pixels] for cut_mode 'manual' index begins from 0
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# define functions
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def calc_arithmetic_mean_of_brightness_per_pixel(r, g, b):
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"""Calculate overall brightness per pixel of the image. Mittelere Helligkeit pro pixel berechnen."""
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r = r.astype('uint16') # set dtype of one colour to uint16, because the sum of 255+255+255 >255 =765
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# the rest will also be uint16 then
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image_heigth = r.shape[0]
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image_width = r.shape[1]
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number_of_colour_channels = 3
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arithmetic_mean_of_brightness_image = np.sum((r+g+b)/number_of_colour_channels)
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arithmetic_mean_of_brightness_per_pixel = arithmetic_mean_of_brightness_image/(image_width*image_heigth)
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max_possible_brightness = 255 # maximum possible brightness for an uint8
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arithmetic_mean_of_brightness_per_pixel_relative = arithmetic_mean_of_brightness_per_pixel/max_possible_brightness # range: 0-1
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return arithmetic_mean_of_brightness_per_pixel_relative
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# start
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image_filenames = [f for f in os.listdir(PATH_IMAGE_FOLDER) if f.endswith('.png')]
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image_filenames.sort()
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for image_number, image_name in enumerate(image_filenames):
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print(f"Current image {image_number+1}/{len(image_filenames)}: {image_name}")
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# Split and Merge colour channels
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img_bgr = cv.imread(os.path.join(PATH_IMAGE_FOLDER,image_name), cv.IMREAD_COLOR)
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b, g, r = cv.split(img_bgr)
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img_rgb = img_bgr[:, :, ::-1]
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# Create Cuts and display Pixel Values
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image_width = r.shape[1]
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if image_width < 1000:
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plot_marker_offset = 4 # Thickness of marker-line in plot in pixels
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elif image_width >= 1000:
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plot_marker_offset = 10 # Thickness of marker-line in plot in pixels
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# Identify dominating colour channel and set cut row and column
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max_pixelvalue_red = r.max(); max_pixelvalue_green = g.max(); max_pixelvalue_blue = b.max()
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max_pixelvalue = np.max([max_pixelvalue_red, max_pixelvalue_green, max_pixelvalue_blue])
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idx_max_pixelvalue_red = np.unravel_index(r.argmax(), r.shape) # row=idx_..[0] column=idx_..[1]
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idx_max_pixelvalue_green = np.unravel_index(g.argmax(), g.shape) # row=idx_..[0] column=idx_..[1]
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idx_max_pixelvalue_blue = np.unravel_index(b.argmax(), b.shape) # row=idx_..[0] column=idx_..[1]
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if max_pixelvalue == max_pixelvalue_red:
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idx_max_pixelvalue = idx_max_pixelvalue_red; msg_dominating_colourchannel = 'red'
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elif max_pixelvalue == max_pixelvalue_green:
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idx_max_pixelvalue = idx_max_pixelvalue_green; msg_dominating_colourchannel = 'green'
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elif max_pixelvalue == max_pixelvalue_blue:
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idx_max_pixelvalue = idx_max_pixelvalue_blue; msg_dominating_colourchannel = 'blue'
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if cut_mode == 'auto':
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cut_row = idx_max_pixelvalue[0]; cut_column = idx_max_pixelvalue[1]
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elif cut_mode == 'manual':
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cut_row = ROW; cut_column = COLUMN
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else:
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print('Wrong cut_mode. End script.'); quit()
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# Red channel
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# Info linspace(start, stop, num):
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# stop: The end value of the sequence --> stop is included
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# num: Number of samples to generate.
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# Because Array stars at index 0: e.g. image with 3280x2464 Pixels: for 3280 Pixels you need 3280 values between 0 and 3279
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# check: x_red_h must have 3280 values?? --> Yes from 0 to 3279 in 1 steps
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x_red_h = np.linspace(0, r.shape[1]-1, r.shape[1]); y_red_h = r[cut_row,:] # data horizontal cut ->width (e.g.: 3280)
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x_red_v = np.linspace(0, r.shape[0]-1, r.shape[0]); y_red_v = r[:,cut_column] # data vertical cut ->height (e.g.: 2464)
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msg1_red = f"Maximum Pixel value in red channel: {max_pixelvalue_red}"; print(msg1_red)
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msg2_red = f"Index of max Value in red channel (row, colum): {idx_max_pixelvalue_red}"; print(msg2_red)
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msg3_red = f"Maximum Pixel value in marked-row {cut_row}: {np.max(y_red_h)}"; print(msg3_red)
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msg4_red = f"Maximum Pixel value in marked-column {cut_column}: {np.max(y_red_v)}"; print(msg4_red)
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r_copy = r.copy(); r_copy[cut_row:cut_row+plot_marker_offset,:]=255; r_copy[:, cut_column:cut_column+plot_marker_offset]=255 # manipulate image for displaying in marked plot
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# Green channel
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x_green_h = np.linspace(0, g.shape[1]-1, g.shape[1]); y_green_h = g[cut_row,:] # data horizontal cut
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x_green_v = np.linspace(0, g.shape[0]-1, g.shape[0]); y_green_v = g[:,cut_column] # data vertical cut
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msg1_green = f"Maximum Pixel value in green channel: {max_pixelvalue_green}"; print(msg1_green)
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msg2_green = f"Index of max Value in green channel (row, colum): {idx_max_pixelvalue_green}"; print(msg2_green)
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msg3_green = f"Maximum Pixel value in marked-row {cut_row}: {np.max(y_green_h)}"; print(msg3_green)
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msg4_green = f"Maximum Pixel value in marked-column {cut_column}: {np.max(y_green_v)}"; print(msg4_green)
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g_copy = g.copy(); g_copy[cut_row:cut_row+plot_marker_offset,:]=255; g_copy[:, cut_column:cut_column+plot_marker_offset]=255 # manipulate image for displaying in marked plot
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# Blue channel
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x_blue_h = np.linspace(0, b.shape[1]-1, b.shape[1]); y_blue_h = b[cut_row,:] # data horizontal cut
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x_blue_v = np.linspace(0, b.shape[0]-1, b.shape[0]); y_blue_v = b[:,cut_column] # data vertical cut
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msg1_blue = f"Maximum Pixel value in blue channel: {max_pixelvalue_blue}"; print(msg1_blue)
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msg2_blue = f"Index of max Value in blue channel (row, colum): {idx_max_pixelvalue_blue}"; print(msg2_blue)
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msg3_blue = f"Maximum Pixel value in marked-row {cut_row}: {np.max(y_blue_h)}"; print(msg3_blue)
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msg4_blue = f"Maximum Pixel value in marked-column {cut_column}: {np.max(y_blue_v)}"; print(msg4_blue)
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b_copy = b.copy(); b_copy[cut_row:cut_row+plot_marker_offset,:]=255; b_copy[:, cut_column:cut_column+plot_marker_offset]=255 # manipulate image for displaying in marked plot
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# Create Plots
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fig1, ((ax_orig_1,ax01,ax02,ax03),(ax_red_1, ax_red_2, ax_red_3, ax_red_4),
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(ax_green_1, ax_green_2, ax_green_3, ax_green_4),(ax_blue_1, ax_blue_2, ax_blue_3, ax_blue_4)) \
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= plt.subplots(4, 4, figsize=(30,25))
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fig1.suptitle(f'Image: {image_name}', y=0.9)
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yticks=np.append(np.arange(0,230,25), 255) #set yticks for cuts
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xlim_max_h=r.shape[1] # xlim for horizontal cut. No special reason for choosing red channel.
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xlim_max_v=r.shape[0] # xlim for vertical cut. No special reason for choosing red channel.
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ax_orig_1.imshow(img_rgb); ax_orig_1.set_title("Original Image");
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ax_orig_1.set_xlabel('Width=H=Columns'); ax_orig_1.set_ylabel('Heigth=V=Rows')
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# red channel
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ax_red_1.imshow(r, cmap = 'gray'); ax_red_1.set_title("Red Channel");
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ax_red_1.set_xlabel('Width=H=Columns'); ax_red_1.set_ylabel('Heigth=V=Rows')
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ax_red_2.imshow(r_copy, cmap = 'gray'); ax_red_2.set_title("Red Channel - marked");
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ax_red_2.set_xlabel('Width=H=Columns'); ax_red_2.set_ylabel('Heigth=V=Rows')
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ax_red_3.plot(x_red_h,y_red_h, linewidth=2.0); ax_red_3.set_title("Horizontal Cut");
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ax_red_3.grid(True); ax_red_3.set_ylim(ymin=0, ymax=260); ax_red_3.set_yticks(yticks); ax_red_3.set_xlim(0,xlim_max_h)
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ax_red_3.set_xlabel('Width=H=Columns'); ax_red_3.set_ylabel('Pixel Value')
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ax_red_4.plot(x_red_v,y_red_v, linewidth=2.0); ax_red_4.set_title("Vertical Cut");
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ax_red_4.grid(True); ax_red_4.set_ylim(ymin=0, ymax=260); ax_red_4.set_yticks(yticks); ax_red_4.set_xlim(0, xlim_max_v)
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ax_red_4.set_xlabel('Heigth=V=Rows'); ax_red_4.set_ylabel('Pixel Value')
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# green channel
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ax_green_1.imshow(g, cmap = 'gray'); ax_green_1.set_title("Green Channel");
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ax_green_1.set_xlabel('Width=H=Columns'); ax_green_1.set_ylabel('Heigth=V=Rows')
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ax_green_2.imshow(g_copy, cmap = 'gray'); ax_green_2.set_title("Green Channel - marked");
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ax_green_2.set_xlabel('Width=H=Columns'); ax_green_2.set_ylabel('Heigth=V=Rows')
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ax_green_3.plot(x_green_h,y_green_h, linewidth=2.0); ax_green_3.set_title("Horizontal Cut");
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ax_green_3.grid(True); ax_green_3.set_ylim(ymin=0, ymax=260); ax_green_3.set_yticks(yticks); ax_green_3.set_xlim(0,xlim_max_h)
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ax_green_3.set_xlabel('Width=H=Columns'); ax_green_3.set_ylabel('Pixel Value')
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ax_green_4.plot(x_green_v,y_green_v, linewidth=2.0); ax_green_4.set_title("Vertical Cut");
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ax_green_4.grid(True); ax_green_4.set_ylim(ymin=0, ymax=260); ax_green_4.set_yticks(yticks); ax_green_4.set_xlim(0, xlim_max_v)
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ax_green_4.set_xlabel('Heigth=V=Rows'); ax_green_4.set_ylabel('Pixel Value')
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# blue channel
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ax_blue_1.imshow(b, cmap = 'gray'); ax_blue_1.set_title("Blue Channel");
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ax_blue_1.set_xlabel('Width=H=Columns'); ax_blue_1.set_ylabel('Heigth=V=Rows')
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ax_blue_2.imshow(b_copy, cmap = 'gray'); ax_blue_2.set_title("Blue Channel - marked");
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ax_blue_2.set_xlabel('Width=H=Columns'); ax_blue_2.set_ylabel('Heigth=V=Rows')
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ax_blue_3.plot(x_blue_h,y_blue_h, linewidth=2.0); ax_blue_3.set_title("Horizontal Cut");
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ax_blue_3.grid(True); ax_blue_3.set_ylim(ymin=0, ymax=260); ax_blue_3.set_yticks(yticks); ax_blue_3.set_xlim(0,xlim_max_h)
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ax_blue_3.set_xlabel('Width=H=Columns'); ax_blue_3.set_ylabel('Pixel Value')
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ax_blue_4.plot(x_blue_v,y_blue_v, linewidth=2.0); ax_blue_4.set_title("Vertical Cut");
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ax_blue_4.grid(True); ax_blue_4.set_ylim(ymin=0, ymax=260); ax_blue_4.set_yticks(yticks); ax_blue_4.set_xlim(0, xlim_max_v)
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ax_blue_4.set_xlabel('Heigth=V=Rows'); ax_blue_4.set_ylabel('Pixel Value')
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# Calculate overall brightness per pixel of the image. Mittelere Helligkeit pro pixel berechnen.
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arithmetic_mean_of_brightness_per_pixel_relative = calc_arithmetic_mean_of_brightness_per_pixel(r,g,b)
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if arithmetic_mean_of_brightness_per_pixel_relative >= 0.01:
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msg1_brightness = f"Overall brightness per pixel: {round(arithmetic_mean_of_brightness_per_pixel_relative*100,2)} %" #value in percent
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elif arithmetic_mean_of_brightness_per_pixel_relative < 0.01:
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msg1_brightness = f"Overall brightness per pixel: {round(arithmetic_mean_of_brightness_per_pixel_relative*10e3,2)} per mil" # value in promille
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print(msg1_brightness)
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# add pixel stats under Figure
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pixelstats_red= '\n'.join([msg1_red, msg2_red, msg3_red, msg4_red])
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pixelstats_green= '\n'.join([msg1_green, msg2_green, msg3_green, msg4_green])
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pixelstats_blue= '\n'.join([msg1_blue, msg2_blue, msg3_blue, msg4_blue])
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pixelstats_overall_brightness = '\n'.join([msg1_brightness])
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pixelstats = '\n\n'.join([f"pixel stats: {msg_dominating_colourchannel} channel dominating",
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pixelstats_red, pixelstats_green, pixelstats_blue, pixelstats_overall_brightness])
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text_x_pos = 0.1; text_y_pos = -0.015 # text position: 0,0 is lower-left and 1,1 is upper-right)
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fig1.text(text_x_pos, text_y_pos, pixelstats, ha='left')
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# add Comment under Figure
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text_x_pos = 0.5; text_y_pos = -0.025 # text position: 0,0 is lower-left and 1,1 is upper-right)
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log_filename=None
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try:
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log_filename=[f for f in os.listdir(PATH_IMAGE_FOLDER) if f.endswith('.txt')][0]
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if log_filename:
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with open(os.path.join(PATH_IMAGE_FOLDER,log_filename), encoding='utf-8') as f:
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log_text = f.readlines()
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if COMMENT != "":
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COMMENT = '\nPlot Comment: '+COMMENT
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log_text.append(COMMENT)
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txt=''.join(log_text)
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fig1.text(text_x_pos, text_y_pos, txt, ha='left')
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except IndexError:
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if COMMENT != "":
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COMMENT = '\nPlot Comment: '+COMMENT
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fig1.text(text_x_pos, text_y_pos, COMMENT, ha='left')
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else:
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pass
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# handle numpy memmory error on Windows:
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switch_overwrite=0 # do not overwrite files, if they exist
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if switch_overwrite == 1:
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fig1.savefig(os.path.join(PATH_IMAGE_FOLDER,f'{image_name}.pdf'), bbox_inches='tight') #save plot
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print('Save pdf')
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else:
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if os.path.isfile(os.path.join(PATH_IMAGE_FOLDER,f'{image_name}.pdf')):
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pass # skip this pdf file, because it already exists
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else:
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fig1.savefig(os.path.join(PATH_IMAGE_FOLDER,f'{image_name}.pdf'), bbox_inches='tight') #save plot
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print('Save pdf')
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plt.close('all') # close all figures
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print('') # new line for better readability in console
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