def get_rings(gray_img, origin):
components_list = util.get_connected_components(gray_img)
polar_components_list = util.get_polar_components(components_list, origin)
rings = util.get_merged_components(polar_components_list)
temp = np.zeros((gray_img.shape))
mean_radius = []
for points in rings:
mean_radius.append(np.mean(points[:, 0]))
rings = [x for _, x in sorted(zip(mean_radius, rings))]
ring_number = 1
for points in rings:
for radius, theta in points:
x, y = util.polar2cart(radius, theta)
x += origin[0]
y += origin[1]
temp[int(y), int(x)] = ring_number
ring_number += 1
plt.figure()
plt.imshow(temp)
plt.show()
return rings
def scatter_plot(rings, C, origin):
cart_rings = []
for points in rings:
cart_ring = []
for radius, theta in points:
x, y = util.polar2cart(radius, theta)
x += origin[0]
y += origin[1]
cart_ring.append((x, y))
cart_rings.append(np.array(cart_ring).astype('float'))
fig = plt.figure()
ax = plt.axes(projection='3d')
for i in range(len(C)):
ax.scatter3D(cart_rings[i][:, 0], cart_rings[i][:, 1], C[i], 'gray')
plt.show()
def findPeakpt(img, pt1, pt2, verbose=False):
peakIdx, zi_diff, zi_int = findPeakIdx(img, pt1, pt2, verbose)
theta_rad = math.atan2(abs(pt2[1]-pt1[1]), abs(pt2[0]-pt1[0]))
x, y = util.polar2cart(peakIdx, theta_rad, pt1)
return x, y
def estimateCircleDiameter3(img, centerXY, diag_len, verbose, debug_img, show_plots=False):
(rows, cols) = img.shape[:2]
if False:
#patch_angles=[ (np.pi/4), (3*np.pi/4) ] # these only work if cols == rows
patch_angles=[ (np.pi/4), -(np.pi/4) ] # these only work if cols == rows
else:
theta_rad = math.atan2(rows, cols)
#patch_angles=[ theta_rad, (np.pi-theta_rad) ]
#patch_angles=[ np.pi/2-theta_rad, -(np.pi/2-theta_rad) ]
patch_angles=[ theta_rad, -(theta_rad) ]
#patch_width=100
patch_width=int(diag_len * 0.10)
if patch_width > 100: patch_width = 100
if patch_width < 25: patch_width = 25
dia = []
pt_sets = []
for idx, patch_angle in enumerate(patch_angles):
print(">>> Patch idx: %d, angle: %.2f, diag_len: %d, patch_width: %d" % (idx, np.rad2deg(patch_angle), diag_len, patch_width))
patch = subimage2(img, centerXY, (np.pi/2-patch_angle), patch_width, diag_len + patch_width)
#patch_blur = cv2.GaussianBlur(patch, (15, 15), 0)
patch_blur=patch
patch_mean = np.abs(np.mean(patch_blur, axis=1))
patch_diff = np.abs(np.diff(patch_mean))
patch_values = patch_mean
n = patch_values.size
print(" patch length = %d" % (n))
left_range = patch_values[n/2::-1]
right_range = patch_values[n/2:]
left_peakIdx = findFirstPeakAboveThresh(left_range, (np.max(left_range) * 0.5) )
right_peakIdx = findFirstPeakAboveThresh(right_range, (np.max(right_range) * 0.5) )
left_edge = n/2 - left_peakIdx
right_edge = n/2 + right_peakIdx
print(" left_peakIdx = %d, left_edge = %d" % (left_peakIdx, left_edge))
print(" right_peakIdx = %d, right_edge = %d" % (right_peakIdx, right_edge))
#~ print " >>>>> right_edge, left_edge: ", peak_sets[1][0], peak_sets[0][0]
#~ print " >>>>> patch_angle, patch_angle2: ", patch_angle, patch_angle + np.pi
if left_peakIdx == -99 or right_peakIdx == -99:
print(" Could not find peak...")
#return -99, pt_sets
if left_peakIdx == 0 or right_peakIdx == 0:
print(" Could only find one peak...")
#return -99, pt_sets
pt1 = util.polar2cart(left_peakIdx, patch_angle, (centerXY[0], rows-centerXY[1]))
pt1 = ( int(np.round(pt1[0])), int(rows-np.round(pt1[1])) )
pt2 = util.polar2cart(right_peakIdx, patch_angle + np.pi, (centerXY[0], rows-centerXY[1]))
pt2 = ( int(np.round(pt2[0])), int(rows-np.round(pt2[1])) )
pt_sets.append( (pt1, pt2) )
#print(">>> left_edge: %d, right_edge: %d" % (left_edge,right_edge))
if verbose: print(" nominal dia: %d" % (right_edge-left_edge))
dia.append(right_edge-left_edge)
if show_plots:
#
# Rotate patch 90, then plot
#
pt1 = util.polar2cart(centerXY[0], patch_angles[1], (0,0))
pt1 = ( int(pt1[0]), int(pt1[1]) )
patch_rot = np.rot90(patch_blur, k=1).copy()
cv2.line(patch_rot, (pt1[0], 0), (pt1[0], 70), 128, 3)
plt.figure(figsize=(20,8))
plt.subplot(111)
plt.imshow(patch_rot, cmap='gray',vmin=0,vmax=255)
plt.title(('patch %d' % (idx)))
plt.xticks([t for t in range(50, patch_rot.shape[:2][1], 50)] )
#
# Calculate pts for patch width and add lines
#
pt1 = util.polar2cart(int(diag_len/2.0), patch_angle, centerXY)
pt1 = ( int(pt1[0]), int(pt1[1]) )
pt2 = util.polar2cart(int(diag_len/2.0), patch_angle + (4*np.pi/4), centerXY)
pt2 = ( int(pt2[0]), int(pt2[1]) )
#cv2.line(debug_img, pt1, pt2, (200, 255, 180), 2)
angle_offsets = [np.pi/2, 6*np.pi/4]
for angle_offset in angle_offsets:
pt1_1 = util.polar2cart(int(patch_width/2.0), patch_angle+angle_offset, pt1)
pt1_1 = ( int(pt1_1[0]), int(pt1_1[1]) )
pt2_1 = util.polar2cart(int(patch_width/2.0), patch_angle + (4*np.pi/4)-angle_offset, pt2)
pt2_1 = ( int(pt2_1[0]), int(pt2_1[1]) )
cv2.line(debug_img, pt1_1, pt2_1, (100, 155, 180), 2)
#
# plot the left and right patch mean values separately
#
if False:
plt.figure(figsize=(30,8))
plt.subplot(221)
plt.plot(left_range[::-1])
plt.xticks([t for t in range(0, left_range.size, 50)] )
plt.text(left_range.size-50, np.max(left_range)-50, ("%s" % (left_peakIdx)), fontsize=18)
plt.title("patch %d, left range (left half of patch_mean)" % (idx))
plt.subplot(222)
plt.plot(right_range)
plt.xticks([t for t in range(0, right_range.size, 50)] )
plt.text(20, np.max(right_range)-50, ("%s" % (right_peakIdx)), fontsize=18)
plt.title("patch %d, right range (right half of patch_mean)" % (idx))
#
# plot the patch_values
#
mid_pt = patch_values.size/2
max_pt = np.max(patch_values)/2
plt.figure(figsize=(50,6))
plt.subplot(220 + idx + 1)
plt.plot(patch_values)
plt.vlines(left_edge, 0, max_pt + 25, color='r', linewidth=4)
plt.text(left_edge-20, 20, ("%s" % left_edge), fontsize=12)
plt.vlines(right_edge, 0, max_pt + 25, color='r', linewidth=4)
plt.vlines(mid_pt, 0, max_pt, color='y', linewidth=2)
plt.text(right_edge-20, 20, ("%s" % right_edge), fontsize=12)
plt.text(left_edge+((right_edge-left_edge)/2), np.max(patch_values)-50, ("%s" % (right_edge-left_edge)), fontsize=18)
plt.text(left_edge+((mid_pt-left_edge)/2), max_pt-50, ("%s" % (left_peakIdx)), fontsize=18)
plt.text(mid_pt+((right_edge-mid_pt)/2), max_pt-50, ("%s" % (right_peakIdx)), fontsize=18)
plt.title("patch %d, mean" % (idx))
plt.xticks([t for t in range(50, patch_values.size, 50)] )
dia_np=np.array(dia)
delta = np.abs(dia_np.mean() - diag_len)
#percent_diff = np.abs(diag_len - dia_np.mean()) / ((dia_np.mean() + diag_len)/2.0)
if dia_np.std() > (dia_np.max() * 0.2):
found = False
print(" estimation failed : %.2f > %2f" % (dia_np.std(), (dia_np.max() * 0.2)))
else:
found = True
if not found:
return -99, pt_sets
else:
return dia_np.mean(), pt_sets
def estimateCircleDiameter2(img, centerXY, diag_len, verbose, debug_img, show_plots=False):
#(rows, cols) = img.shape[:2]
# pt = (center[1]+(376/2), center[0]-(488/2))
# end_pt=(cols-1, 1)
# offset=(0, 0)
# x_len = abs(cols)
# y_len = abs(rows)
# hypot = int(np.hypot(x_len, y_len))
# theta_rad = math.atan2(abs(y_len), abs(x_len))
# #theta_rad = math.atan2((end_pt[1]-center_pt[1]), (end_pt[0]-center_pt[0]))
# print("angle: %f" % np.rad2deg(theta_rad))
# x,y = util.polar2cart(hypot/2, theta_rad, center_pt)
# print "rot pt (center): ", (x,y)
# x,y = abs(x+offset[0]), abs(y+offset[1])
# print "rot pt (center) after offset: ", (x,y)
if show_plots:
plot_lst = []
plot_lst.append( (img, 'img') )
if True:
patch_angles=[ (np.pi/4), (3*np.pi/4) ] # these only work if cols == rows
else:
(rows, cols) = img.shape[:2]
theta_rad = math.atan2(rows, cols)
patch_angles=[ theta_rad, (np.pi-theta_rad) ]
patch_width=70
dia = []
pt_sets = []
for idx, patch_angle in enumerate(patch_angles):
patch = subimage2(img, centerXY, patch_angle, patch_width, diag_len + (1*150))
#patch_blur = cv2.GaussianBlur(patch, (15, 15), 0)
patch_blur=patch
patch_mean = np.abs(np.mean(patch_blur, axis=1))
patch_diff = np.abs(np.diff(patch_mean))
if show_plots:
plot_lst.append( (patch_blur, ('patch %d' % (idx))) )
pt1 = util.polar2cart(int(diag_len/2.0), patch_angle, centerXY)
pt1 = ( int(pt1[0]), int(pt1[1]) )
pt2 = util.polar2cart(int(diag_len/2.0), patch_angle + (4*np.pi/4), centerXY)
pt2 = ( int(pt2[0]), int(pt2[1]) )
#cv2.line(debug_img, pt1, pt2, (200, 255, 180), 2)
angle_offsets = [np.pi/2, 6*np.pi/4]
for angle_offset in angle_offsets:
pt1_1 = util.polar2cart(int(patch_width/2.0), patch_angle+angle_offset, pt1)
pt1_1 = ( int(pt1_1[0]), int(pt1_1[1]) )
pt2_1 = util.polar2cart(int(patch_width/2.0), patch_angle + (4*np.pi/4)-angle_offset, pt2)
pt2_1 = ( int(pt2_1[0]), int(pt2_1[1]) )
cv2.line(debug_img, pt1_1, pt2_1, (100, 155, 180), 2)
n = patch_diff.size
ranges = []
#~ lt_range = patch_diff[n/2::-1]
#~ rt_range = patch_diff[n/2:]
lt_range = patch_mean[n/2::-1]
rt_range = patch_mean[n/2:]
ranges.append(lt_range)
ranges.append(rt_range)
if show_plots:
plt.figure(figsize=(10*2,10*2))
plt.subplot(221)
plt.plot(lt_range)
plt.title("patch %d, left range (left half of patch_diff)" % (idx))
plt.subplot(222)
plt.plot(rt_range)
plt.title("patch %d, right range (right half of patch_diff)" % (idx))
# iter through each range, find the first peak past threshold
peak_sets = []
for r in ranges:
delta_threshold = np.max(r) * 0.55
if verbose: print(" (estimateCircleDiameter2) threshold: ", delta_threshold)
idx_jumps = []
for idx, delta in enumerate(r):
if delta > delta_threshold:
idx_jumps.append( (idx, delta) )
#print(" idx_jump found: idx=%d, delta=%d" % (idx, delta))
if len(idx_jumps) > 0:
peak_set = idx_jumps[0]
#print(" peakIdx, delta:", peak_set[0], peak_set[1])
else:
peak_set = (-99, 0)
peak_sets.append(peak_set)
#print(" peaks:", peak_sets)
lt_peakIdx = n/2 - peak_sets[0][0]
rt_peakIdx = n/2 + peak_sets[1][0]
#~ print " >>>>> rt_peakIdx, lt_peakIdx: ", peak_sets[1][0], peak_sets[0][0]
#~ print " >>>>> patch_angle, patch_angle2: ", patch_angle, patch_angle + (4*np.pi/4)
pt1 = util.polar2cart(peak_sets[0][0], patch_angle, centerXY)
pt1 = ( int(pt1[0]), int(pt1[1]) )
pt2 = util.polar2cart(peak_sets[1][0], patch_angle + (4*np.pi/4), centerXY)
pt2 = ( int(pt2[0]), int(pt2[1]) )
pt_sets.append( (pt1, pt2) )
#print(">>> lt_peakIdx: %d, rt_peakIdx: %d" % (lt_peakIdx,rt_peakIdx))
if verbose: print(" (estimateCircleDiameter) nominal dia: %d" % (rt_peakIdx-lt_peakIdx))
dia.append(rt_peakIdx-lt_peakIdx)
if show_plots:
util.plot_imgs(plot_lst)
#----- old:
#~ patch1 = subimage2(img, centerXY, (np.pi/4), 60, diag_len + 150)
#~ patch2 = subimage2(img, centerXY, (3*np.pi/4), 60, diag_len + 150)
#~ #patch3 = subimage2(img, centerXY, (np.pi/2), 60, cols)
#~
#~ patch1_blur = cv2.GaussianBlur(patch1, (15, 15), 0)
#~ patch2_blur = cv2.GaussianBlur(patch2, (15, 15), 0)
#~ #patch3_blur = cv2.GaussianBlur(patch3, (15, 15), 0)
#~
#~ patch1_mean = np.abs(np.mean(patch1_blur, axis=1))
#~ patch1_diff = np.abs(np.diff(patch1_mean))
#~ patch2_mean = np.abs(np.mean(patch2_blur, axis=1))
#~ patch2_diff = np.abs(np.diff(patch2_mean))
#~ #patch3_mean = np.abs(np.mean(patch3_blur, axis=1))
#~ #patch3_diff = np.abs(np.diff(patch3_mean))
#~
#~
#~ patches = []
#~ patches.append(patch1_diff)
#~ patches.append(patch2_diff)
#~ #patches.append(patch3_diff)
#~
#~ if show_plots:
#~ util.plot_imgs([(img, 'img'), (patch1_blur, 'patch1'), (patch2_blur, 'patch2')], color=False)
#~
#~ vector_sets=[ ]
#~ dia=[]
#~ for patch_diff in patches:
#~ # split into two ranges: middle->left, middle->right
#~ n = patch_diff.size
#~ ranges = []
#~ lt_range = patch_diff[n/2::-1]
#~ rt_range = patch_diff[n/2:]
#~ ranges.append(lt_range)
#~ ranges.append(rt_range)
#~
#~ if show_plots:
#~ plt.figure(figsize=(10*2,10*2))
#~ plt.subplot(221)
#~ plt.plot(lt_range)
#~ plt.subplot(222)
#~ plt.plot(rt_range)
#~
#~ # iter through each range, find the first peak past threshold
#~ peak_sets = []
#~ for r in ranges:
#~ delta_threshold = np.max(r) * 0.25
#~ #if verbose: print " (estimateCircleDiameter) threshold: ", delta_threshold
#~ idx_jumps = []
#~ for idx, delta in enumerate(r):
#~ if delta > delta_threshold:
#~ idx_jumps.append( (idx, delta) )
#~ #print(" idx_jump found: idx=%d, delta=%d" % (idx, delta))
#~ if len(idx_jumps) > 0:
#~ peak_set = idx_jumps[0]
#~ #print " peakIdx, delta:", peak_set[0], peak_set[1]
#~ else:
#~ peak_set = (-99, 0)
#~ peak_sets.append(peak_set)
#~ #print " peaks:", peak_sets
#~
#~ lt_peakIdx = n/2 - peak_sets[0][0]
#~ rt_peakIdx = n/2 + peak_sets[1][0]
#~
#~ vector_sets.append( (rt_peakIdx, lt_peakIdx) )
#~
#~ #print(">>> lt_peakIdx: %d, rt_peakIdx: %d" % (lt_peakIdx,rt_peakIdx))
#~ if verbose: print(" (estimateCircleDiameter) nominal dia: %d" % (rt_peakIdx-lt_peakIdx))
#~ dia.append(rt_peakIdx-lt_peakIdx)
dia_np=np.array(dia)
delta = np.abs(dia_np.mean() - diag_len)
#percent_diff = np.abs(diag_len - dia_np.mean()) / ((dia_np.mean() + diag_len)/2.0)
if dia_np.std() > (dia_np.max() * 0.2):
found = False
print(" estimation failed1: %.2f > %2f" % (dia_np.std(), (dia_np.max() * 0.2)))
#~ elif percent_diff > 0.1:
#~ found = False
#~ print(" estimation failed2: %.2f > 0.3" % (percent_diff))
#~ print " diag_len : ", diag_len
#~ print " dia_np.mean(): ", dia_np.mean()
else:
found = True
if not found:
return -99, pt_sets
else:
return dia_np.mean(), pt_sets
def findPeakpt(img, pt1, pt2, verbose=False):
peakIdx, zi_diff, zi_int = findPeakIdx(img, pt1, pt2, verbose)
theta_rad = math.atan2(abs(pt2[1]-pt1[1]), abs(pt2[0]-pt1[0]))
x, y = util.polar2cart(peakIdx, theta_rad, pt1)
return x, y
def estimateCircleDiameter3(img, centerXY, diag_len, verbose, debug_img, show_plots=False):
(rows, cols) = img.shape[:2]
if False:
#patch_angles=[ (np.pi/4), (3*np.pi/4) ] # these only work if cols == rows
patch_angles=[ (np.pi/4), -(np.pi/4) ] # these only work if cols == rows
else:
theta_rad = math.atan2(rows, cols)
#patch_angles=[ theta_rad, (np.pi-theta_rad) ]
#patch_angles=[ np.pi/2-theta_rad, -(np.pi/2-theta_rad) ]
patch_angles=[ theta_rad, -(theta_rad) ]
#patch_width=100
patch_width=int(diag_len * 0.10)
if patch_width > 100: patch_width = 100
if patch_width < 25: patch_width = 25
dia = []
pt_sets = []
for idx, patch_angle in enumerate(patch_angles):
print(">>> Patch idx: %d, angle: %.2f, diag_len: %d, patch_width: %d" % (idx, np.rad2deg(patch_angle), diag_len, patch_width))
patch = subimage2(img, centerXY, (np.pi/2-patch_angle), patch_width, diag_len + patch_width)
#patch_blur = cv2.GaussianBlur(patch, (15, 15), 0)
patch_blur=patch
patch_mean = np.abs(np.mean(patch_blur, axis=1))
patch_diff = np.abs(np.diff(patch_mean))
patch_values = patch_mean
n = patch_values.size
print(" patch length = %d" % (n))
left_range = patch_values[n/2::-1]
right_range = patch_values[n/2:]
left_peakIdx = findFirstPeakAboveThresh(left_range, (np.max(left_range) * 0.5) )
right_peakIdx = findFirstPeakAboveThresh(right_range, (np.max(right_range) * 0.5) )
left_edge = n/2 - left_peakIdx
right_edge = n/2 + right_peakIdx
print(" left_peakIdx = %d, left_edge = %d" % (left_peakIdx, left_edge))
print(" right_peakIdx = %d, right_edge = %d" % (right_peakIdx, right_edge))
#~ print " >>>>> right_edge, left_edge: ", peak_sets[1][0], peak_sets[0][0]
#~ print " >>>>> patch_angle, patch_angle2: ", patch_angle, patch_angle + np.pi
if left_peakIdx == -99 or right_peakIdx == -99:
print " Could not find peak..."
#return -99, pt_sets
if left_peakIdx == 0 or right_peakIdx == 0:
print " Could only find one peak..."
#return -99, pt_sets
pt1 = util.polar2cart(left_peakIdx, patch_angle, (centerXY[0], rows-centerXY[1]))
pt1 = ( int(np.round(pt1[0])), int(rows-np.round(pt1[1])) )
pt2 = util.polar2cart(right_peakIdx, patch_angle + np.pi, (centerXY[0], rows-centerXY[1]))
pt2 = ( int(np.round(pt2[0])), int(rows-np.round(pt2[1])) )
pt_sets.append( (pt1, pt2) )
#print(">>> left_edge: %d, right_edge: %d" % (left_edge,right_edge))
if verbose: print(" nominal dia: %d" % (right_edge-left_edge))
dia.append(right_edge-left_edge)
if show_plots:
#
# Rotate patch 90, then plot
#
pt1 = util.polar2cart(centerXY[0], patch_angles[1], (0,0))
pt1 = ( int(pt1[0]), int(pt1[1]) )
patch_rot = np.rot90(patch_blur, k=1).copy()
cv2.line(patch_rot, (pt1[0], 0), (pt1[0], 70), 128, 3)
plt.figure(figsize=(20,8))
plt.subplot(111)
plt.imshow(patch_rot, cmap='gray',vmin=0,vmax=255)
plt.title(('patch %d' % (idx)))
plt.xticks([t for t in range(50, patch_rot.shape[:2][1], 50)] )
#
# Calculate pts for patch width and add lines
#
pt1 = util.polar2cart(int(diag_len/2.0), patch_angle, centerXY)
pt1 = ( int(pt1[0]), int(pt1[1]) )
pt2 = util.polar2cart(int(diag_len/2.0), patch_angle + (4*np.pi/4), centerXY)
pt2 = ( int(pt2[0]), int(pt2[1]) )
#cv2.line(debug_img, pt1, pt2, (200, 255, 180), 2)
angle_offsets = [np.pi/2, 6*np.pi/4]
for angle_offset in angle_offsets:
pt1_1 = util.polar2cart(int(patch_width/2.0), patch_angle+angle_offset, pt1)
pt1_1 = ( int(pt1_1[0]), int(pt1_1[1]) )
pt2_1 = util.polar2cart(int(patch_width/2.0), patch_angle + (4*np.pi/4)-angle_offset, pt2)
pt2_1 = ( int(pt2_1[0]), int(pt2_1[1]) )
cv2.line(debug_img, pt1_1, pt2_1, (100, 155, 180), 2)
#
# plot the left and right patch mean values separately
#
if False:
plt.figure(figsize=(30,8))
plt.subplot(221)
plt.plot(left_range[::-1])
plt.xticks([t for t in range(0, left_range.size, 50)] )
plt.text(left_range.size-50, np.max(left_range)-50, ("%s" % (left_peakIdx)), fontsize=18)
plt.title("patch %d, left range (left half of patch_mean)" % (idx))
plt.subplot(222)
plt.plot(right_range)
plt.xticks([t for t in range(0, right_range.size, 50)] )
plt.text(20, np.max(right_range)-50, ("%s" % (right_peakIdx)), fontsize=18)
plt.title("patch %d, right range (right half of patch_mean)" % (idx))
#
# plot the patch_values
#
mid_pt = patch_values.size/2
max_pt = np.max(patch_values)/2
plt.figure(figsize=(50,6))
plt.subplot(220 + idx + 1)
plt.plot(patch_values)
plt.vlines(left_edge, 0, max_pt + 25, color='r', linewidth=4)
plt.text(left_edge-20, 20, ("%s" % left_edge), fontsize=12)
plt.vlines(right_edge, 0, max_pt + 25, color='r', linewidth=4)
plt.vlines(mid_pt, 0, max_pt, color='y', linewidth=2)
plt.text(right_edge-20, 20, ("%s" % right_edge), fontsize=12)
plt.text(left_edge+((right_edge-left_edge)/2), np.max(patch_values)-50, ("%s" % (right_edge-left_edge)), fontsize=18)
plt.text(left_edge+((mid_pt-left_edge)/2), max_pt-50, ("%s" % (left_peakIdx)), fontsize=18)
plt.text(mid_pt+((right_edge-mid_pt)/2), max_pt-50, ("%s" % (right_peakIdx)), fontsize=18)
plt.title("patch %d, mean" % (idx))
plt.xticks([t for t in range(50, patch_values.size, 50)] )
dia_np=np.array(dia)
delta = np.abs(dia_np.mean() - diag_len)
#percent_diff = np.abs(diag_len - dia_np.mean()) / ((dia_np.mean() + diag_len)/2.0)
if dia_np.std() > (dia_np.max() * 0.2):
found = False
print(" estimation failed : %.2f > %2f" % (dia_np.std(), (dia_np.max() * 0.2)))
else:
found = True
if not found:
return -99, pt_sets
else:
return dia_np.mean(), pt_sets
def estimateCircleDiameter2(img, centerXY, diag_len, verbose, debug_img, show_plots=False):
#(rows, cols) = img.shape[:2]
# pt = (center[1]+(376/2), center[0]-(488/2))
# end_pt=(cols-1, 1)
# offset=(0, 0)
# x_len = abs(cols)
# y_len = abs(rows)
# hypot = int(np.hypot(x_len, y_len))
# theta_rad = math.atan2(abs(y_len), abs(x_len))
# #theta_rad = math.atan2((end_pt[1]-center_pt[1]), (end_pt[0]-center_pt[0]))
# print("angle: %f" % np.rad2deg(theta_rad))
# x,y = util.polar2cart(hypot/2, theta_rad, center_pt)
# print "rot pt (center): ", (x,y)
# x,y = abs(x+offset[0]), abs(y+offset[1])
# print "rot pt (center) after offset: ", (x,y)
if show_plots:
plot_lst = []
plot_lst.append( (img, 'img') )
if True:
patch_angles=[ (np.pi/4), (3*np.pi/4) ] # these only work if cols == rows
else:
(rows, cols) = img.shape[:2]
theta_rad = math.atan2(rows, cols)
patch_angles=[ theta_rad, (np.pi-theta_rad) ]
patch_width=70
dia = []
pt_sets = []
for idx, patch_angle in enumerate(patch_angles):
patch = subimage2(img, centerXY, patch_angle, patch_width, diag_len + (1*150))
#patch_blur = cv2.GaussianBlur(patch, (15, 15), 0)
patch_blur=patch
patch_mean = np.abs(np.mean(patch_blur, axis=1))
patch_diff = np.abs(np.diff(patch_mean))
if show_plots:
plot_lst.append( (patch_blur, ('patch %d' % (idx))) )
pt1 = util.polar2cart(int(diag_len/2.0), patch_angle, centerXY)
pt1 = ( int(pt1[0]), int(pt1[1]) )
pt2 = util.polar2cart(int(diag_len/2.0), patch_angle + (4*np.pi/4), centerXY)
pt2 = ( int(pt2[0]), int(pt2[1]) )
#cv2.line(debug_img, pt1, pt2, (200, 255, 180), 2)
angle_offsets = [np.pi/2, 6*np.pi/4]
for angle_offset in angle_offsets:
pt1_1 = util.polar2cart(int(patch_width/2.0), patch_angle+angle_offset, pt1)
pt1_1 = ( int(pt1_1[0]), int(pt1_1[1]) )
pt2_1 = util.polar2cart(int(patch_width/2.0), patch_angle + (4*np.pi/4)-angle_offset, pt2)
pt2_1 = ( int(pt2_1[0]), int(pt2_1[1]) )
cv2.line(debug_img, pt1_1, pt2_1, (100, 155, 180), 2)
n = patch_diff.size
ranges = []
#~ lt_range = patch_diff[n/2::-1]
#~ rt_range = patch_diff[n/2:]
lt_range = patch_mean[n/2::-1]
rt_range = patch_mean[n/2:]
ranges.append(lt_range)
ranges.append(rt_range)
if show_plots:
plt.figure(figsize=(10*2,10*2))
plt.subplot(221)
plt.plot(lt_range)
plt.title("patch %d, left range (left half of patch_diff)" % (idx))
plt.subplot(222)
plt.plot(rt_range)
plt.title("patch %d, right range (right half of patch_diff)" % (idx))
# iter through each range, find the first peak past threshold
peak_sets = []
for r in ranges:
delta_threshold = np.max(r) * 0.55
if verbose: print " (estimateCircleDiameter2) threshold: ", delta_threshold
idx_jumps = []
for idx, delta in enumerate(r):
if delta > delta_threshold:
idx_jumps.append( (idx, delta) )
#print(" idx_jump found: idx=%d, delta=%d" % (idx, delta))
if len(idx_jumps) > 0:
peak_set = idx_jumps[0]
#print " peakIdx, delta:", peak_set[0], peak_set[1]
else:
peak_set = (-99, 0)
peak_sets.append(peak_set)
#print " peaks:", peak_sets
lt_peakIdx = n/2 - peak_sets[0][0]
rt_peakIdx = n/2 + peak_sets[1][0]
#~ print " >>>>> rt_peakIdx, lt_peakIdx: ", peak_sets[1][0], peak_sets[0][0]
#~ print " >>>>> patch_angle, patch_angle2: ", patch_angle, patch_angle + (4*np.pi/4)
pt1 = util.polar2cart(peak_sets[0][0], patch_angle, centerXY)
pt1 = ( int(pt1[0]), int(pt1[1]) )
pt2 = util.polar2cart(peak_sets[1][0], patch_angle + (4*np.pi/4), centerXY)
pt2 = ( int(pt2[0]), int(pt2[1]) )
pt_sets.append( (pt1, pt2) )
#print(">>> lt_peakIdx: %d, rt_peakIdx: %d" % (lt_peakIdx,rt_peakIdx))
if verbose: print(" (estimateCircleDiameter) nominal dia: %d" % (rt_peakIdx-lt_peakIdx))
dia.append(rt_peakIdx-lt_peakIdx)
if show_plots:
util.plot_imgs(plot_lst)
#----- old:
#~ patch1 = subimage2(img, centerXY, (np.pi/4), 60, diag_len + 150)
#~ patch2 = subimage2(img, centerXY, (3*np.pi/4), 60, diag_len + 150)
#~ #patch3 = subimage2(img, centerXY, (np.pi/2), 60, cols)
#~
#~ patch1_blur = cv2.GaussianBlur(patch1, (15, 15), 0)
#~ patch2_blur = cv2.GaussianBlur(patch2, (15, 15), 0)
#~ #patch3_blur = cv2.GaussianBlur(patch3, (15, 15), 0)
#~
#~ patch1_mean = np.abs(np.mean(patch1_blur, axis=1))
#~ patch1_diff = np.abs(np.diff(patch1_mean))
#~ patch2_mean = np.abs(np.mean(patch2_blur, axis=1))
#~ patch2_diff = np.abs(np.diff(patch2_mean))
#~ #patch3_mean = np.abs(np.mean(patch3_blur, axis=1))
#~ #patch3_diff = np.abs(np.diff(patch3_mean))
#~
#~
#~ patches = []
#~ patches.append(patch1_diff)
#~ patches.append(patch2_diff)
#~ #patches.append(patch3_diff)
#~
#~ if show_plots:
#~ util.plot_imgs([(img, 'img'), (patch1_blur, 'patch1'), (patch2_blur, 'patch2')], color=False)
#~
#~ vector_sets=[ ]
#~ dia=[]
#~ for patch_diff in patches:
#~ # split into two ranges: middle->left, middle->right
#~ n = patch_diff.size
#~ ranges = []
#~ lt_range = patch_diff[n/2::-1]
#~ rt_range = patch_diff[n/2:]
#~ ranges.append(lt_range)
#~ ranges.append(rt_range)
#~
#~ if show_plots:
#~ plt.figure(figsize=(10*2,10*2))
#~ plt.subplot(221)
#~ plt.plot(lt_range)
#~ plt.subplot(222)
#~ plt.plot(rt_range)
#~
#~ # iter through each range, find the first peak past threshold
#~ peak_sets = []
#~ for r in ranges:
#~ delta_threshold = np.max(r) * 0.25
#~ #if verbose: print " (estimateCircleDiameter) threshold: ", delta_threshold
#~ idx_jumps = []
#~ for idx, delta in enumerate(r):
#~ if delta > delta_threshold:
#~ idx_jumps.append( (idx, delta) )
#~ #print(" idx_jump found: idx=%d, delta=%d" % (idx, delta))
#~ if len(idx_jumps) > 0:
#~ peak_set = idx_jumps[0]
#~ #print " peakIdx, delta:", peak_set[0], peak_set[1]
#~ else:
#~ peak_set = (-99, 0)
#~ peak_sets.append(peak_set)
#~ #print " peaks:", peak_sets
#~
#~ lt_peakIdx = n/2 - peak_sets[0][0]
#~ rt_peakIdx = n/2 + peak_sets[1][0]
#~
#~ vector_sets.append( (rt_peakIdx, lt_peakIdx) )
#~
#~ #print(">>> lt_peakIdx: %d, rt_peakIdx: %d" % (lt_peakIdx,rt_peakIdx))
#~ if verbose: print(" (estimateCircleDiameter) nominal dia: %d" % (rt_peakIdx-lt_peakIdx))
#~ dia.append(rt_peakIdx-lt_peakIdx)
dia_np=np.array(dia)
delta = np.abs(dia_np.mean() - diag_len)
#percent_diff = np.abs(diag_len - dia_np.mean()) / ((dia_np.mean() + diag_len)/2.0)
if dia_np.std() > (dia_np.max() * 0.2):
found = False
print(" estimation failed1: %.2f > %2f" % (dia_np.std(), (dia_np.max() * 0.2)))
#~ elif percent_diff > 0.1:
#~ found = False
#~ print(" estimation failed2: %.2f > 0.3" % (percent_diff))
#~ print " diag_len : ", diag_len
#~ print " dia_np.mean(): ", dia_np.mean()
else:
found = True
if not found:
return -99, pt_sets
else:
return dia_np.mean(), pt_sets