def read_image_sequence(self, video_name: str):
self.set_image_sequence_name(video_name)
# Set video directory
video_dir = os_path_join(
self.im_seqs_dir,
video_name
)
# List all the images included inside the video directory
images_names = os_listdir(video_dir)
# Sort the images with respect to their name
sorted_images_names = sorted(images_names)
# Read every image and store it in a list.
# Turn this list to a numpy array.
""" In an example the colors where not right
self.image_sequence = np_array(
[self.image_handler.load_image(os_path_join(video_dir, img_name)) for img_name in sorted_images_names]
)
"""
self.image_sequence = np_array(
[cv2_imread(os_path_join(video_dir, img_name)) for img_name in sorted_images_names]
)
return self.image_sequence
def img_load(path, w, h, resize=True):
img = cv2_imread(path)
if len(img.shape) == 2:
img = np.dstack((img, img, img))
if resize:
img = cv2_resize(img, (w, h)).astype(np.uint8)
img = cvtColor(img, COLOR_BGR2RGB)
return img
def read_image_sequence(self, video_name: str):
self.set_image_sequence_name(video_name)
# Set video directory
video_dir = os_path_join(self.im_seqs_dir, video_name)
# List all the images included inside the video directory
images_names = os_listdir(video_dir)
# Sort the images with respect to their name
sorted_images_names = sorted(images_names)
# Read every image and store it in a list.
# Turn this list to a numpy array.
self.left_sequence = np_array([
cv2_imread(os_path_join(video_dir, img_name))
for img_name in sorted_images_names if img_name.endswith('L.jpg')
])
self.right_sequence = np_array([
cv2_imread(os_path_join(video_dir, img_name))
for img_name in sorted_images_names if img_name.endswith('R.jpg')
])
return self.left_sequence, self.right_sequence
def image_search(self, path, threshold=0.7, max_n=50):
"""
Search similar logos
@param: path : the complete path to the image file
@returns: two list of 'Logo' instance, where the first one contains good matches, and the second normal ones
"""
im = cv2_imread(path)
if im is None:
print("No image at '%s' ,match failed!" % path)
return [], []
im = im[:,:,::-1] # BGR -> RGB
logo_inds_s, scores_s = self._image_search_sift(im, max_n=50)
logo_inds_c, scores_c = self._image_search_color(im, max_n=50)
scores = {}
r = 0.5 # the ratio between color score and sift score
for ind, score in zip(logo_inds_s, scores_s):
scores.setdefault(ind, 0)
scores[ind] += r * score
for ind, score in zip(logo_inds_c, scores_c):
scores.setdefault(ind, 0)
scores[ind] += (1-r) * score
logo_inds = np.array(scores.keys())
scores = np.array(scores.values())
# get max_n
max_n = min(np.sum(scores > threshold / 4), max_n)
inds = np.argpartition(scores, -max_n)[-max_n:]
inds = inds[np.argsort(scores[inds])][::-1]
scores = scores[inds]
logo_inds = logo_inds[inds]
logo_inds += 1 # ind begins with 1
logos = self.get_logos_from_db(logo_inds)
good = []
normal = []
for logo, score in zip(logos, scores):
(good if score > threshold else normal).append(logo)
return good, normal
def load_path(path):
img = cv2_imread(path)
if len(img.shape) == 2:
img = np.dstack((img, img, img))
img = cvtColor(img, COLOR_BGR2RGB)
return img
def get_im_cv2_1024(path):
img = cv2_imread(path)
resized = cv2_resize(img, (1024, 1024), cv2_INTER_AREA)
return [path, resized]
def get_im_cv2_512(path):
img = cv2_imread(path)
resized = cv2_resize(img, (512, 512), cv2_INTER_AREA)
return [path, resized]
def get_im_cv2_256(path):
img = cv2_imread(path)
resized = cv2_resize(img, (256, 256), cv2_INTER_AREA)
return [path, resized]
def get_im_cv2_64(path):
img = cv2_imread(path)
resized = cv2_resize(img, (64, 64), cv2_INTER_AREA)
return [path, resized]
def get_im_cv2_32(path):
img = cv2_imread(path)
resized = cv2_resize(
img, (32, 32),
cv2_INTER_AREA) #use cv2_resize(img, (64, 64), cv2_INTER_AREA)
return [path, resized]