def parse_image(im, model, channel):
"""Parses graph image downloaded from innerfidelity.com"""
# Find graph edges
v_lines = ImageGraphParser.find_lines(im,
'vertical',
line_color=(204, 204, 204))
h_lines = ImageGraphParser.find_lines(im,
'horizontal',
line_color=(204, 204, 204))
h_lines = np.array(h_lines)
# Crop by left and right edges
box = (v_lines[0], h_lines[0], v_lines[-1], im.size[1])
im = im.crop(box)
h_lines -= h_lines[
0] # Cropped to first line, subtract distance from all lines
# Add missing horizontal lines 115 - 55
deltas = []
for i in range(1, len(h_lines)):
deltas.append(h_lines[i] - h_lines[i - 1])
delta = max(deltas, key=deltas.count)
_h_lines = list(h_lines[:1])
for _ in range(12): # First line + 12 additional lines
# Estimate where the line should be
estimate = _h_lines[-1] + delta
# Get original lines which are at most 1 pixel away from the estimate
originals = h_lines[np.abs(np.array(h_lines) - estimate) <= 1]
if len(originals):
# Original line found, use it
estimate = originals[0]
# Add new line
_h_lines.append(estimate)
h_lines = _h_lines
# Crop bottom
box = (0, 0, im.size[0], h_lines[-1])
im = im.crop(box)
px_a_max = 0
px_a_min = h_lines[-1]
#im.show()
# X axis
f_min = 20
f_max = 20000
f_step = (f_max / f_min)**(1 / im.size[0])
f = [f_min]
for _ in range(1, im.size[0]):
f.append(f[-1] * f_step)
# Y axis
a_max = 115
a_min = 55
a_res = (a_max - a_min) / (px_a_min - px_a_max)
# Colors
color_left = (79, 129, 189)
color_right = (119, 119, 119)
_im = im.copy()
inspection = _im.load()
amplitude = []
# Iterate each column
for x in range(im.size[0]):
pxs = [] # Graph pixels
# Iterate each row (pixel in column)
for y in range(im.size[1]):
# Convert read RGB pixel values and convert to HSV
rgba = im.getpixel((x, y))
r, g, b = rgba[:3]
# Graph pixels are colored
if (channel == 'left' and
(r, g, b) == color_left) or (channel == 'right' and
(r, g, b) == color_right):
pxs.append(float(y))
else:
p = im.getpixel((x, y))
inspection[x,
y] = (int(0.3 * p[0]), int(255 * 0.7 + 0.3 * p[1]),
int(0 + 0.3 * p[2]))
if not pxs:
# No graph pixels found on this column
amplitude.append(None)
else:
# Mean of recorded pixels
v = np.mean(pxs)
# Convert to dB value
v = a_max - v * a_res
amplitude.append(v)
# Inspection image
draw = ImageDraw.Draw(_im)
x0 = np.log(30 / f_min) / np.log(f_step)
x1 = np.log(10000 / f_min) / np.log(f_step)
y_0 = px_a_max + 10 / a_res
y_1 = px_a_min - 10 / a_res
draw.rectangle(((x0, y_0), (x1, y_1)), outline='magenta')
draw.rectangle(((x0 + 1, y_0 + 1), (x1 - 1, y_1 - 1)), outline='magenta')
# Create frequency response
fr = FrequencyResponse(model, f, amplitude)
fr.interpolate()
fr.center()
return fr, _im
def parse_image(im,
model,
px_top=800,
px_bottom=4400,
px_left=0,
px_right=2500):
"""Parses graph image downloaded from innerfidelity.com"""
# Crop out everything but graph area (roughly)
box = (px_left, px_top, im.size[0] - px_right, im.size[1] - px_bottom)
im = im.crop(box)
#im.show()
# Find graph edges
v_lines = ImageGraphParser.find_lines(im, 'vertical')
h_lines = ImageGraphParser.find_lines(im, 'horizontal')
# Crop by graph edges
box = (v_lines[0], h_lines[0], v_lines[1], h_lines[1])
im = im.crop(box)
#im.show()
# X axis
f_min = 10
f_max = 20000
f_step = (f_max / f_min)**(1 / im.size[0])
f = [f_min]
for _ in range(1, im.size[0]):
f.append(f[-1] * f_step)
# Y axis
a_max = 30
a_min = -20
a_res = (a_max - a_min) / im.size[1]
_im = im.copy()
pix = _im.load()
amplitude = []
y_legend = 40 / 50 * im.size[1]
x0_legend = np.log(70 / f_min) / np.log(f_step)
x1_legend = np.log(1000 / f_min) / np.log(f_step)
# Iterate each column
for x in range(im.size[0]):
pxs = [] # Graph pixels
# Iterate each row (pixel in column)
for y in range(im.size[1]):
if y > y_legend and x0_legend < x < x1_legend:
# Skip pixels in the legend box
continue
# Convert read RGB pixel values and convert to HSV
h, s, v = colorsys.rgb_to_hsv(
*[v / 255.0 for v in im.getpixel((x, y))])
# Graph pixels are colored
if 0.7 < s < 0.9 and 20 / 360 < h < 30 / 360:
pxs.append(float(y))
else:
p = im.getpixel((x, y))
pix[x, y] = (int(0.3 * p[0]), int(255 * 0.7 + 0.3 * p[1]),
int(0.3 * p[2]))
if not pxs:
# No graph pixels found on this column
amplitude.append(None)
else:
# Mean of recorded pixels
v = np.mean(pxs)
# Convert to dB value
v = a_max - v * a_res
amplitude.append(v)
# Inspection image
draw = ImageDraw.Draw(_im)
x0 = np.log(20 / f_min) / np.log(f_step)
x1 = np.log(10000 / f_min) / np.log(f_step)
draw.rectangle(((x0, 10 / a_res), (x1, 40 / a_res)), outline='magenta')
draw.rectangle(((x0 + 1, 10 / a_res + 1), (x1 - 1, 40 / a_res - 1)),
outline='magenta')
fr = FrequencyResponse(model, f, amplitude)
fr.interpolate()
fr.center()
return fr, _im