def get_people_diff(cls, person1: 'ClassPeopleReId',
person2: 'ClassPeopleReId'):
# Comparing people between list
diff_upper = ClassUtils.get_color_diff_rgb(person1.color_upper,
person2.color_upper)
diff_lower = ClassUtils.get_color_diff_rgb(person1.color_lower,
person2.color_lower)
diff_colors_1 = ClassUtils.get_color_diff_rgb(person1.color_upper,
person1.color_lower)
diff_colors_2 = ClassUtils.get_color_diff_rgb(person2.color_upper,
person2.color_lower)
diff_colors = math.fabs(diff_colors_1 - diff_colors_2)
diff_k_means = ClassDescriptors.get_kmeans_diff(
person1.hist_pose, person2.hist_pose)
distance = cls.get_person_distance(person1, person2)
return_data = {
'diffUpper': diff_upper,
'diffLower': diff_lower,
'diffColors': diff_colors,
'distance': distance,
'diffKMeans': diff_k_means
}
return return_data
def test_color_compare():
print('Test color comparision')
print('Loading image comparision')
# Loading instances
instance_pose = ClassOpenPose()
# Avoid to open two prompts
obj_img = ClassDescriptors.load_images_comparision_ext(instance_pose,
min_score,
load_one_img=True)
hist_pose1 = obj_img['listPoints1']
list_process = list()
# Iterating in examples folder
for root, _, files in os.walk(EXAMPLES_FOLDER):
for file in files:
full_path = os.path.join(root, file)
extension = ClassUtils.get_filename_extension(full_path)
if extension == '.jpg':
list_process.append(full_path)
# Sorting list
list_process.sort()
list_result = list()
score_max_pt = -1
for full_path in list_process:
print('Processing file: {0}'.format(full_path))
json_path = full_path.replace('.jpg', '.json')
with open(json_path, 'r') as f:
obj_json = json.loads(f.read())
his_pose2 = obj_json['histPose']
diff = ClassDescriptors.get_kmeans_diff(hist_pose1, his_pose2)
print('Diff {0}'.format(diff))
if diff <= 15:
res = True
else:
res = False
list_result.append({
'filename':
ClassUtils.get_filename_no_extension(full_path),
'score':
res
})
# list_result.sort(key=lambda x: x['score'])
print('Printing list result')
print(json.dumps(list_result, indent=2))
print('min_score: {0}'.format(score_max_pt))
print('Done!')
def test_color_compare_hist(perform_eq=False):
print('Test color comparision')
print('Loading image comparision')
# Loading instances
instance_pose = ClassOpenPose()
ignore_json_color = False
if perform_eq:
ignore_json_color = True
# Avoid to open two prompts
obj_img = ClassDescriptors.load_images_comparision_ext(
instance_pose,
min_score,
load_one_img=True,
perform_eq=perform_eq,
ignore_json_color=ignore_json_color)
# Extract color comparision from image
hist1 = obj_img['listPoints1']
# Generating examples folder
list_process = list()
for root, _, files in os.walk(EXAMPLES_FOLDER):
for file in files:
full_path = os.path.join(root, file)
extension = ClassUtils.get_filename_extension(full_path)
if extension == '.jpg':
list_process.append(full_path)
# Sorting list
list_process.sort()
"""
list_result = list()
score_max_pt = -1
"""
for full_path in list_process:
print('Processing file: {0}'.format(full_path))
json_path = full_path.replace('.jpg', '.json')
with open(json_path, 'r') as f:
obj_json = json.loads(f.read())
image2 = cv2.imread(full_path)
if perform_eq:
image2 = ClassUtils.equalize_hist(image2)
if 'vector' in obj_json:
pose2 = obj_json['vector']
elif 'vectors' in obj_json:
pose2 = obj_json['vectors']
else:
raise Exception('Invalid vector property for vector custom')
hist2 = ClassDescriptors.get_points_by_pose(image2, pose2, min_score)
diff = ClassDescriptors.get_kmeans_diff(hist1, hist2)
# Getting mean y from image 2 - discarding purposes
pt1, pt2 = ClassUtils.get_rectangle_bounds(pose2, min_score)
image2_crop = image2[pt1[1]:pt2[1], pt1[0]:pt2[0]]
image2_ycc = cv2.cvtColor(image2_crop, cv2.COLOR_BGR2YCrCb)
mean_y = np.mean(image2_ycc[:, :, 0])
print('Diff color: {0} - Mean y: {1}'.format(diff, mean_y))
"""
list_result.sort(key=lambda x: x['score'])
print('Printing list result')
print(list_result)
print('min_score: {0}'.format(score_max_pt))
"""
print('Done!')