def test_3():
print("\nTest Step 3: Project a Regular Image into the Detected Zone.")
input_images = ['ar-3-a_base.jpg', 'ar-3-b_base.jpg', 'ar-3-c_base.jpg']
output_images = ['ar-3-a-1.png', 'ar-3-a-2.png', 'ar-3-a-3.png']
# Advertisement image
advert = cv2.imread(os.path.join(IMG_DIR, "img-3-a-1.png"))
advert = cv2.resize(advert, (ds_w, ds_h), interpolation=cv2.INTER_AREA)
src_points = ar.get_corners_list(advert)
# Optional template image
template = cv2.imread(os.path.join(IMG_DIR, "template.jpg"))
for img_in, img_out in zip(input_images, output_images):
print("Input image: {}".format(img_in))
# Open image and identify the four marker positions
scene = cv2.imread(os.path.join(IMG_DIR, img_in))
scene = cv2.resize(scene, (ds_w, ds_h), interpolation=cv2.INTER_AREA)
markers = ar.find_markers(scene, template)
# homography = ar.find_four_point_transform(src_points, markers)
homography, _ = cv2.findHomography(np.float32(src_points),
np.float32(markers))
projected_img = ar.project_imageA_onto_imageB(advert, scene,
homography)
projected_img = ar.draw_box(projected_img, markers, 1)
save_image(img_out, projected_img)
def test_2():
print("\nTest Step 2: Draw Box around Detected Marker Zone.")
input_images = [
'ar-2-a_base.jpg', 'ar-2-b_base.jpg', 'ar-2-c_base.jpg',
'ar-2-d_base.jpg', 'ar-2-e_base.jpg'
]
output_images = [
'ar-2-a-1.png', 'ar-2-a-2.png', 'ar-2-a-3.png', 'ar-2-a-4.png',
'ar-2-a-5.png'
]
# Optional template image
template = cv2.imread(os.path.join(IMG_DIR, "template.jpg"))
for img_in, img_out in zip(input_images, output_images):
print("Input image: {}".format(img_in))
# Open image and identify the four marker positions
scene = cv2.imread(os.path.join(IMG_DIR, img_in))
scene = cv2.resize(scene, (ds_w, ds_h), interpolation=cv2.INTER_AREA)
markers = ar.find_markers(scene, template)
image_with_box = ar.draw_box(scene, markers, 1)
save_image(img_out, image_with_box)
def test_1():
"""
"""
print("\nTest Step 1: Extract markers in pictures.")
input_images = [
'sim_clear_scene.jpg', 'sim_noisy_scene_1.jpg', 'sim_noisy_scene_2.jpg'
]
output_images = ['ar-1-a-1.png', 'ar-1-a-2.png', 'ar-1-a-3.png']
# Optional template image
template = cv2.imread(os.path.join(IMG_DIR, "template.jpg"))
for img_in, img_out in zip(input_images, output_images):
print("Input image: {}".format(img_in))
# Open image and identify the four marker positions
scene = cv2.imread(os.path.join(IMG_DIR, img_in))
marker_positions = ar.find_markers(scene, template)
for marker in marker_positions:
mark_location(scene, marker)
save_image(img_out, scene)
def unit_for_test_4_and_5(video_name, fps, frame_ids, output_prefix,
counter_init, is_part5):
video = os.path.join(VID_DIR, video_name)
image_gen = ar.video_frame_generator(video)
image = image_gen.__next__()
image = cv2.resize(image, (ds_w, ds_h), interpolation=cv2.INTER_AREA)
h, w, d = image.shape
out_path = "ar_{}-{}".format(output_prefix[4:], video_name)
video_out = mp4_video_writer(out_path, (w, h), fps)
# Optional template image
template = cv2.imread(os.path.join(IMG_DIR, "template.jpg"))
if is_part5:
advert = cv2.imread(os.path.join(IMG_DIR, "img-3-a-1.png"))
src_points = ar.get_corners_list(advert)
output_counter = counter_init
frame_num = 1
while image is not None:
print("Processing fame {}".format(frame_num))
markers = ar.find_markers(image, template)
if is_part5:
# homography = ar.find_four_point_transform(src_points, markers)
homography, _ = cv2.findHomography(np.float32(src_points),
np.float32(markers))
image = ar.project_imageA_onto_imageB(advert, image, homography)
else:
for marker in markers:
mark_location(image, marker)
frame_id = frame_ids[(output_counter - 1) % 3]
if frame_num == frame_id:
out_str = output_prefix + "-{}.png".format(output_counter)
save_image(out_str, image)
output_counter += 1
video_out.write(image)
image = image_gen.__next__()
if image is not None:
image = cv2.resize(image, (ds_w, ds_h),
interpolation=cv2.INTER_AREA)
frame_num += 1
video_out.release()
def unit_for_test_6(video_name,
fps,
frame_ids,
output_prefix,
counter_init,
my_video,
isWebcam=False):
video = os.path.join(VID_DIR, video_name)
image_gen = ar.video_frame_generator(video)
if isWebcam:
image_gen2 = ar.video_frame_generator(0)
else:
video2 = os.path.join(VID_DIR, my_video)
image_gen2 = ar.video_frame_generator(video2)
image = image_gen.__next__()
image = cv2.resize(image, (ds_w, ds_h), interpolation=cv2.INTER_AREA)
h, w, d = image.shape
image2 = image_gen2.__next__()
image2 = cv2.resize(image2, (ds_w, ds_h), interpolation=cv2.INTER_AREA)
h2, w2, d2 = image2.shape
out_path = "ar_{}-{}".format(output_prefix[4:], video_name)
video_out = mp4_video_writer(out_path, (w, h), fps)
# Optional template image
template = cv2.imread(os.path.join(IMG_DIR, "template.jpg"))
# advert = cv2.imread(os.path.join(IMG_DIR, "img-3-a-1.png"))
src_points = ar.get_corners_list(image2)
output_counter = counter_init
frame_num = 1
while image is not None and image2 is not None:
print("Processing fame {}".format(frame_num))
markers = ar.find_markers(image, template)
# homography = ar.find_four_point_transform(src_points, markers)
homography, _ = cv2.findHomography(np.float32(src_points),
np.float32(markers))
# image = ar.project_imageA_onto_imageB(image2, image, homography)
src_pts = np.float32(src_points)
dst_pts = np.float32(markers)
len_src = np.sum([
np.linalg.norm([src_pts[3], src_pts[0]]),
np.linalg.norm(src_pts[0:2]),
np.linalg.norm(src_pts[1:3]),
np.linalg.norm(src_pts[2:])
])
len_dst = np.sum([
np.linalg.norm([dst_pts[3], dst_pts[0]]),
np.linalg.norm(dst_pts[0:2]),
np.linalg.norm(dst_pts[1:3]),
np.linalg.norm(dst_pts[2:])
])
scale = len_src / len_dst
dst_pts = np.mean(dst_pts, 0) + (dst_pts - np.mean(dst_pts, 0)) * scale
# print(markers,src_points,scale,advert.shape)
m_homography, _ = cv2.findHomography(src_pts, dst_pts)
m_homography = np.linalg.inv(m_homography)
projected_img_mirror = ar.project_mirror_onto_imageB(
image2, image, homography, m_homography)
image = ar.draw_box(projected_img_mirror, markers, 2)
# save_image(img_mirror, projected_img_mirror)
frame_id = frame_ids[(output_counter - 1) % 3]
if frame_num == frame_id:
out_str = output_prefix + "-{}.png".format(output_counter)
save_image(out_str, image)
output_counter += 1
video_out.write(image)
if isWebcam:
# Display the resulting frame
cv2.imshow('frame', image)
if cv2.waitKey(1) & 0xFF == ord('q'): break
image = image_gen.__next__()
image2 = image_gen2.__next__()
if image is not None:
image = cv2.resize(image, (ds_w, ds_h),
interpolation=cv2.INTER_AREA)
if image2 is not None:
image2 = cv2.resize(image2, (ds_w, ds_h),
interpolation=cv2.INTER_AREA)
frame_num += 1
video_out.release()
def test_3_mirror():
print("\nTest Step 3M: Project a Mirror Image in the Right Perspective.")
input_images = [
'ar-2-a_base.jpg', 'ar-2-b_base.jpg', 'ar-2-c_base.jpg',
'ar-2-d_base.jpg', 'ar-2-e_base.jpg'
]
output_images = [
'ar-3-m-1.png', 'ar-3-m-2.png', 'ar-3-m-3.png', 'ar-3-m-4.png',
'ar-3-m-5.png'
]
output_images_mirror = [
'ar-3-m-1-mirror.png', 'ar-3-m-2-mirror.png', 'ar-3-m-3-mirror.png',
'ar-3-m-4-mirror.png', 'ar-3-m-5-mirror.png'
]
# Advertisement image
advert = cv2.imread(os.path.join(IMG_DIR, "static_subj.jpg"))
advert = cv2.resize(advert, (ds_w, ds_h), interpolation=cv2.INTER_AREA)
src_points = ar.get_corners_list(advert)
# Optional template image
template = cv2.imread(os.path.join(IMG_DIR, "template.jpg"))
# for img_in, img_out in zip(input_images, output_images):
L = len(input_images)
# L = 3
for i in range(L):
img_in = input_images[i]
img_out = output_images[i]
img_mirror = output_images_mirror[i]
print("Input image: {}".format(img_in))
# Open image and identify the four marker positions
scene = cv2.imread(os.path.join(IMG_DIR, img_in))
scene = cv2.resize(scene, (ds_w, ds_h), interpolation=cv2.INTER_AREA)
markers = ar.find_markers(scene, template)
# homography = ar.find_four_point_transform(src_points, markers)
homography, _ = cv2.findHomography(np.float32(src_points),
np.float32(markers))
projected_img = ar.project_imageA_onto_imageB(advert, scene,
homography)
projected_img = ar.draw_box(projected_img, markers, 1)
save_image(img_out, projected_img)
# m_homology = np.linalg.inv(homography)
# scale = np.diag([0.1,0.1,0.1])
# m_homology = scale * m_homology * np.linalg.inv(scale)
src_pts = np.float32(src_points)
dst_pts = np.float32(markers)
len_src = np.sum([
np.linalg.norm([src_pts[3], src_pts[0]]),
np.linalg.norm(src_pts[0:2]),
np.linalg.norm(src_pts[1:3]),
np.linalg.norm(src_pts[2:])
])
len_dst = np.sum([
np.linalg.norm([dst_pts[3], dst_pts[0]]),
np.linalg.norm(dst_pts[0:2]),
np.linalg.norm(dst_pts[1:3]),
np.linalg.norm(dst_pts[2:])
])
scale = 1.5 * len_src / len_dst
dst_pts = np.mean(dst_pts, 0) + (dst_pts - np.mean(dst_pts, 0)) * scale
# print(markers,src_points,scale,advert.shape)
m_homography, _ = cv2.findHomography(src_pts, dst_pts)
m_homography = np.linalg.inv(m_homography)
projected_img_mirror = ar.project_mirror_onto_imageB(
advert, scene, homography, m_homography)
projected_img_mirror = ar.draw_box(projected_img_mirror, markers, 1)
save_image(img_mirror, projected_img_mirror)