def show_line_field(im, p1, p2):
distance_from_line_field(im, *(p1 + p2) )
fig, ax = plt.subplots(2)
ax[0].imshow(im, cm.rainbow_r, interpolation = 'nearest')
ax[0].plot( *zip(p1, p2), color='w' )
ax[0].set_xlim( 0, xres-1)
ax[0].set_ylim( 0, yres-1)
pixels = []
def putpixel_collect(im, x1, y1, value):
putpixel (im, x1, y1, value)
pixels.append ( (x1, y1) )
im2 = np.copy(im)
line_bresenham_int(im2, *(p1 + p2 + ( 128, putpixel_collect)) )
ax[1].imshow(im2, cm.rainbow_r, interpolation = 'nearest')
ax[1].plot( *zip(p1, p2), color='b' )
ax[1].plot( *zip(*pixels), color='b', marker='o', markersize=2, linestyle='None' )
ax[1].set_xlim( 0, xres-1)
ax[1].set_ylim( 0, yres-1)
plt_show()
def show_line_vs_pixel_grid(xres, yres):
p1 = ( xres // 4, yres // 4)
p2 = ( 3 * xres // 4 , 3 * yres // 4 )
fix, axis = plt.subplots (1)
axis.set_xlim( 0, xres-1)
axis.set_ylim( 0, yres-1)
axis.set_title('Line: (%s, %s) - (%s, %s)' % (p1 + p2) )
start, end = axis.get_xlim()
for x in np.arange(start + 0.5, end + 0.5, 1): # draw lines at half-points manually
axis.axvline (x, color='lightgray')
axis.xaxis.set_ticks(xrange(int(start), int(end+1)))
start, end = axis.get_ylim()
for y in np.arange(start + 0.5, end + 0.5, 1):
axis.axhline (y, color='lightgray')
axis.yaxis.set_ticks(xrange(int(start), int(end+1)))
axis.plot( *zip(p1, p2), color='g' )
axis.plot( *zip(p1, p2), color='b', marker='o', markersize=3, linestyle='None' )
plt_show()
def on_click(event):
"""Plot click event handler - zoom into set"""
global box
print event.inaxes
box_width = (box[1].real - box[0].real)
box_height = (box[1].imag - box[0].imag)
center = complex(box[0].real + (float(event.xdata)/get_image_size(img)[0])*box_width,
box[0].imag + (1.0-(float(event.ydata)/get_image_size(img)[1]))*box_height)
new_box_size = complex(box_width/1.5, box_height/1.5)
box[0] = center - new_box_size/2
box[1] = center + new_box_size/2
render_mandel_iterative(img, box[0], box[1], max_iter)
ax.imshow(img, cm.spectral, interpolation='nearest')
plt_show()
def show_line_dda_with_rounding_error(xres, yres):
im = get_grayscale_image(xres, yres)
p1 = ( xres // 5, yres // 3 )
p2 = ( 4 * xres // 5, 2 * yres // 3)
dx = abs(p1[0]-p2[0])
dy = abs(p1[1]-p2[1])
args = {}
if dx > dy:
args['sharex'] = True
else:
args['sharey'] = True
error = line_dda(im, *(p1 + p2 + ( 255, )) )
#fig, axis = plt.subplots( 2, **args )
grid = gridspec.GridSpec (2, 1, height_ratios = [2, 1])
axis = [plt.subplot(grid[i]) for i in [0, 1]]
axis[0].imshow(im, cm.gray, interpolation = 'nearest')
axis[0].plot( *zip(p1, p2), color='g' )
axis[0].set_xlim( 0, xres-1)
axis[0].set_ylim( 0, yres-1)
axis[0].xaxis.set_ticks( xrange(0, xres, 8) )
axis[0].grid(True, color = 'white', which = 'major')
axis[1].plot(*zip(*error))
axis[1].set_xlim( 0, xres-1)
#axis[1].xaxis.set_ticks(0, xres-1 )
axis[1].xaxis.set_ticks( xrange(0, xres, 8) )
axis[1].grid(True, which = 'major')
#axis[1].set_aspect(1)
#axis[1].set_autoscaley_on(False)
#axis[1].set_ylim( -0.6, 0.6 )
plt.tight_layout()
plt_show()
def show_image(path):
# edges = engine.edges
img, faces = engine.read_image(path)
_, landmarks = faces[0]
# def get_color(idx1, idx2):
# f = (engine.get_landmark_id(idx1) == engine.LANDMARK_LEFT_EYE) \
# + (engine.get_landmark_id(idx2) == engine.LANDMARK_LEFT_EYE)
# if f == 0:
# f = (engine.get_landmark_id(idx1) == engine.LANDMARK_RIGHT_EYE) \
# + (engine.get_landmark_id(idx2) == engine.LANDMARK_RIGHT_EYE)
# if f == 0:
# return 0, 0, 255
# if f == 1:
# return 255, 0, 0
# if f == 2:
# return 0, 255, 0
# return 255, 255, 255
#
# for i, j in edges:
# cv2.line(img, landmarks[i], landmarks[j], get_color(i, j), 1, cv2.LINE_AA)
for a, b, c in engine.triangles:
cv2.line(img, landmarks[a], landmarks[b], (0, 0, 255), 1, cv2.LINE_AA)
cv2.line(img, landmarks[a], landmarks[c], (0, 0, 255), 1, cv2.LINE_AA)
cv2.line(img, landmarks[c], landmarks[b], (0, 0, 255), 1, cv2.LINE_AA)
for triangle in engine.bound_triangles:
ps = list(
map(
lambda x: landmarks[x]
if x >= 0 else engine.get_bound_point(img, x), triangle))
# print(ps[0], ps[1], ps[2])
cv2.line(img, ps[0], ps[1], (0, 255, 0), 1, cv2.LINE_AA)
cv2.line(img, ps[0], ps[2], (0, 255, 0), 1, cv2.LINE_AA)
cv2.line(img, ps[2], ps[1], (0, 255, 0), 1, cv2.LINE_AA)
utils.plt_show(img)
def main():
# engine.save_face_models(FACE_MODEL_FILE, 'Data/img/005.jpg')
engine.load_face_models(FACE_MODEL_FILE)
# img_filename = 'Data/img/005.jpg'
img_filename = 'Test/0007_01.jpg'
# show_image(img_filename)
# init_svm(train=True, test=True)
# init_svm(train=False, test=False)
# img, img_morph = svm_beautify(img_filename)
init_knn(train=False)
img, img_morph = knn_beautify(img_filename, 'male')
# img, img_morph = make_bigger_eyes(img_filename, 0.05)
# img, img_morph = make_thinner_outline(img_filename, 0.1)
utils.plt_show(img)
utils.plt_show(img_morph)
def process_batch(batch, show=False):
output = np.clip(utils.tensor_to_img(batch)[0], 0., 1.)
plt.imsave(args['output_path'], output)
if show:
utils.plt_show(output)
return output
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import numpy as np
fig, ax = plt.subplots(1)
xres, yres = 100, 80
img = get_grayscale_image(xres, yres)
ticks = np.linspace(0, min(xres, yres)-1, endpoint=True, num=9).astype(np.int32)
x, y = np.meshgrid(ticks, ticks)
pts = np.dstack((y, x))
c = 0
for i in xrange(pts.shape[0]-1):
for j in xrange(pts.shape[1]-1):
draw_polygon(img,
[pts[j][i], pts[j+1][i],
pts[j+1][i+1], pts[j][i+1]],
(((i+j) % 2) + 1) * 100)
c += 1
ax.imshow(img, cm.gray, interpolation='nearest')
plt_show()
face_detector = mtcnn.MTCNN()
face_encoder = load_model(encoder_model)
img = cv2.imread(test_img_path)
# plt_show(img)
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
results = face_detector.detect_faces(img_rgb)
for res in results:
face, pt_1, pt_2 = get_face(img_rgb, res['box'])
encode = get_encode(face_encoder, face, required_size)
encode = l2_normalizer.transform(np.expand_dims(encode, axis=0))[0]
name = 'unknown'
distance = float("inf")
for db_name, db_encode in encoding_dict.items():
dist = cosine(db_encode, encode)
if dist < recognition_t and dist < distance:
name = db_name
distance = dist
if name == 'unknown':
cv2.rectangle(img, pt_1, pt_2, (0, 0, 255), 2)
cv2.putText(img, name, pt_1, cv2.FONT_HERSHEY_PLAIN, 2, (0, 0, 255), 2)
else:
cv2.rectangle(img, pt_1, pt_2, (0, 255, 0), 2)
cv2.putText(img, name + f'__{distance:.2f}', pt_1, cv2.FONT_HERSHEY_PLAIN, 2, (0, 255, 0), 2)
cv2.imwrite(test_res_path, img)
plt_show(img)
import matplotlib.pyplot as plt
import cv2
import numpy as np
from utils import process_config,frame_factory,frame_diff,thresh_otsu,concatenate,resize_and_gray,show_img,open_op,PIL_filter,\
plt_show,shape_filter
from ipywidgets import interact, FloatSlider
params = process_config('..\\config.cfg')
frames = frame_factory(params)
fgbg = cv2.createBackgroundSubtractorMOG2()
for i in range(200):
img, gray = resize_and_gray(frames[i], True)
#gray=filter_img.filter(gray)
fgmask = fgbg.apply(gray)
#absdiff=cv2.absdiff(gray,fgbg.getBackgroundImage())
#cv2.imshow('frame',concatenate(img,fgmask))
#cv2.imshow('bs',np.concatenate([gray,absdiff],axis=1))
#k = cv2.waitKey(100)
#if k == 27:
# break
#else:
# continue
#cv2.destroyAllWindows()
img, gray = resize_and_gray(frames[200], True)
fgmask = fgbg.apply(gray)
plt_show(fgmask)
plt_show(cv2.absdiff(gray, fgbg.getBackgroundImage()))
absdiff = cv2.absdiff(gray, fgbg.getBackgroundImage())
ret, bg = cv2.threshold(fgmask, 126, 255, cv2.THRESH_BINARY)
ret, fg = cv2.threshold(fgmask, 128, 255, cv2.THRESH_BINARY)
plt_show(bg)
plt_show(fg)