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 do_color_analysis():
print('Initializing main function')
print('HSV analysis')
pt1 = np.array([[[129, 120, 116]]], dtype=np.uint8)
pt2 = np.array([[[99, 101, 101]]], dtype=np.uint8)
diff = ClassUtils.get_color_diff_rgb([129, 120, 116], [99, 101, 101])
print(diff)
hsv1 = cv2.cvtColor(pt1, cv2.COLOR_BGR2HSV)
hsv2 = cv2.cvtColor(pt2, cv2.COLOR_BGR2HSV)
hsv2[0, 0, 2] = 129
pt_con = cv2.cvtColor(hsv2, cv2.COLOR_HSV2BGR)
print(pt1)
print(pt2)
print(pt_con)
diff = ClassUtils.get_color_diff_rgb(to_rgb_arr(pt1), to_rgb_arr(pt_con))
print(diff)
print('LAB analysis')
lab1 = cv2.cvtColor(pt1, cv2.COLOR_BGR2LAB)
lab2 = cv2.cvtColor(pt2, cv2.COLOR_BGR2LAB)
lab2[0, 0, 0] = 129
pt_con = cv2.cvtColor(lab2, cv2.COLOR_LAB2BGR)
print(pt1)
print(pt2)
print(pt_con)
diff = ClassUtils.get_color_diff_rgb(to_rgb_arr(pt1), to_rgb_arr(pt_con))
print(diff)
def process_btf():
print('Processing BTF transformation')
# Loading instances
instance_pose = ClassOpenPose()
image1, image2, pose1, pose2 = ClassDescriptors.load_images_comparision(
instance_pose, min_score)
hists1 = ClassDescriptors.get_color_histograms(pose1,
min_score,
image1,
decode_img=False)
hists2 = ClassDescriptors.get_color_histograms(pose2,
min_score,
image2,
decode_img=False)
# Plotting
# plot_histograms(hists1)
# Showing first images without transformation
cv2.namedWindow('main_window', cv2.WINDOW_AUTOSIZE)
upper1, lower1 = ClassDescriptors.process_colors_person(pose1,
min_score,
image1,
decode_img=False)
upper2, lower2 = ClassDescriptors.process_colors_person(pose2,
min_score,
image2,
decode_img=False)
print('Diff1: {0}'.format(ClassUtils.get_color_diff_rgb(upper1, upper2)))
print('Diff2: {0}'.format(ClassUtils.get_color_diff_rgb(lower1, lower2)))
cv2.imshow('main_window', np.hstack((image1, image2)))
print('Press any key to continue')
cv2.waitKey(0)
# Perform image transformation
image_tr = ClassDescriptors.transform_image(image2, hists2, hists1)
upper1, lower1 = ClassDescriptors.process_colors_person(pose1,
min_score,
image1,
decode_img=False)
upper2, lower2 = ClassDescriptors.process_colors_person(pose2,
min_score,
image_tr,
decode_img=False)
print('Diff1: {0}'.format(ClassUtils.get_color_diff_rgb(upper1, upper2)))
print('Diff2: {0}'.format(ClassUtils.get_color_diff_rgb(lower1, lower2)))
cv2.imshow('main_window', np.hstack((image1, image_tr)))
print('Press any key to continue')
cv2.waitKey(0)
cv2.destroyAllWindows()
print('Done!')
def main():
print('Initializing main function')
color1 = [158, 145, 179]
color2 = [70, 73, 93]
delta_e = ClassUtils.get_color_diff_rgb(color1, color2)
print('Color diff: {0}'.format(delta_e))
# Showing colors
# In 100 x 100 window
# Trying to compare colors visually
cv2.namedWindow('main_window', cv2.WND_PROP_AUTOSIZE)
img_size = 100
image1 = np.zeros((img_size, img_size, 3), np.uint8)
image2 = np.zeros((img_size, img_size, 3), np.uint8)
# BGR format -> Generating
image1[:, :] = (color1[2], color1[1], color1[0])
image2[:, :] = (color2[2], color2[1], color2[0])
# Showing image stacked
img = np.hstack((image1, image2))
cv2.imshow('main_window', img)
print('Press a key to continue')
cv2.waitKey(0)
print('Done!')
def process_lab_adj():
print('Process lab adjustment')
# Loading instances
instance_pose = ClassOpenPose()
# Loading images
image1, image2, pose1, pose2 = ClassDescriptors.load_images_comparision(
instance_pose, min_score)
# Performing color comparision
upper1, lower1 = ClassDescriptors.process_colors_person(pose1,
min_score,
image1,
decode_img=False)
upper2, lower2 = ClassDescriptors.process_colors_person(pose2,
min_score,
image2,
decode_img=False)
# Performing custom comparison first
diff_upper = ClassUtils.get_color_diff_rgb(upper1, upper2)
diff_lower = ClassUtils.get_color_diff_rgb(lower1, lower2)
print('Diff upper: {0}'.format(diff_upper))
print('Diff lower: {0}'.format(diff_lower))
# Performing lab conversion and equal L values
diff_upper_lum = ClassUtils.get_color_diff_rgb_lum(upper1, upper2)
diff_lower_lum = ClassUtils.get_color_diff_rgb_lum(lower1, lower2)
print('Diff upper lum: {0}'.format(diff_upper_lum))
print('Diff lower lum: {0}'.format(diff_lower_lum))
# Showing images
cv2.namedWindow('main_window', cv2.WND_PROP_AUTOSIZE)
cv2.imshow('main_window', np.hstack((image1, image2)))
cv2.waitKey(0)
print('Done!')
def compare_people_items(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)
distance = cls.get_person_distance(person1, person2)
# Color diff between 1 and 100
# Distance in cm
# Normalize diffs between 0 and 50 - If greater, diff is one
norm_upper = diff_upper
if norm_upper > 50:
norm_upper = 50
norm_upper /= 50
norm_lower = diff_lower
if norm_lower > 50:
norm_lower = 50
norm_lower /= 50
# Change distance for velocity
delta_time = math.fabs(
(person1.last_date - person2.last_date).total_seconds())
if delta_time == 0:
print('Hello!')
velocity = distance / delta_time
if velocity > 200:
velocity = 200
velocity /= 200
return norm_upper, norm_lower, velocity
def main():
print('Initializing main function')
color1 = [199, 164, 112]
color2 = [128, 112, 132]
delta_e = ClassUtils.get_color_diff_rgb(color1, color2)
print('Color diff: {0}'.format(delta_e))
# Showing colors
# In 100 x 100 window
# Trying to compare colors visually
cv2.namedWindow('main_window', cv2.WND_PROP_AUTOSIZE)
img_size = 100
image1_1 = np.zeros((img_size, img_size, 3), np.uint8)
image2_1 = np.zeros((img_size, img_size, 3), np.uint8)
# BGR format -> Generating
image1_1[:, :] = (color1[2], color1[1], color1[0])
image2_1[:, :] = (color2[2], color2[1], color2[0])
# Showing image stacked
img = np.hstack((image1_1, image2_1))
result1 = ClassColorComparison.classify_color(color1)
result2 = ClassColorComparison.classify_color(color2)
image1_2 = np.zeros((img_size, img_size, 3), np.uint8)
image2_2 = np.zeros((img_size, img_size, 3), np.uint8)
image1_2[:, :] = (result1[1][2], result1[1][1], result1[1][0])
image2_2[:, :] = (result2[1][2], result2[1][1], result2[1][0])
img_aux = np.hstack((image1_2, image2_2))
img_result = np.vstack((img, img_aux))
cv2.imshow('main_window', img_result)
print('Press a key to continue')
cv2.waitKey(0)
print('Done!')
def main():
print('Initializing main function')
Tk().withdraw()
# filename1 = '/home/mauricio/Pictures/Poses/temp/636550787632290048_419.jpg'
# filename2 = '/home/mauricio/Pictures/Poses/walk_front/636550801813440000_424.jpg'
filename1 = '/home/mauricio/Pictures/Poses/bend_left/636453039344460032_1.jpg'
filename2 = '/home/mauricio/Pictures/Poses/left_walk/636550795366450048_420.jpg'
print('Select first file')
init_dir = '/home/mauricio/Pictures/Poses'
options = {'initialdir': init_dir}
filename1_tmp = askopenfilename(**options)
if filename1_tmp:
filename1 = filename1_tmp
print('File selected: {0}'.format(filename1))
print('Select second file')
filename2_tmp = askopenfilename(**options)
if filename2_tmp:
filename2 = filename2_tmp
print('File selected: {0}'.format(filename2))
img1 = cv2.imread(filename1)
img2 = cv2.imread(filename2)
instance_pose = ClassOpenPose()
vectors1, img_draw1 = instance_pose.recognize_image_tuple(img1)
vectors2, img_draw2 = instance_pose.recognize_image_tuple(img2)
if len(vectors1) != 1:
raise Exception('Invalid len for vector 1')
if len(vectors2) != 1:
raise Exception('Invalid len for vector 2')
person_vector1 = vectors1[0]
person_vector2 = vectors2[0]
min_percent = 0.05
if not ClassUtils.check_vector_integrity_pos(person_vector1, min_percent):
raise Exception('Invalid integrity for vector 1')
if not ClassUtils.check_vector_integrity_pos(person_vector2, min_percent):
raise Exception('Invalid integrity for vector 2')
colors1 = ClassDescriptors.process_colors(vectors1, min_percent, img1, decode_img=False)
colors2 = ClassDescriptors.process_colors(vectors2, min_percent, img2, decode_img=False)
upper1 = colors1[0][0]
lower1 = colors1[1][0]
color_diff1 = ClassUtils.get_color_diff_rgb(upper1, lower1)
upper2 = colors2[0][0]
lower2 = colors2[1][0]
color_diff2 = ClassUtils.get_color_diff_rgb(upper2, lower2)
diff_upper = ClassUtils.get_color_diff_rgb(upper1, upper2)
diff_lower = ClassUtils.get_color_diff_rgb(lower1, lower2)
diff_diff = math.fabs(color_diff1 - color_diff2)
print('Diff upper: {0}'.format(diff_upper))
print('Diff lower: {0}'.format(diff_lower))
print('Diff diffs: {0}'.format(diff_diff))
cv2.namedWindow('main_window', cv2.WND_PROP_AUTOSIZE)
cv2.imshow('main_window', np.hstack((img_draw1, img_draw2)))
cv2.waitKey(0)
cv2.destroyAllWindows()
print('Done!')
def process_test_color_eq():
print('Processing color eq')
# Loading instances
instance_pose = ClassOpenPose()
im1, im2, pose1, pose2 = ClassDescriptors.load_images_comparision(
instance_pose, min_score)
upper1, lower1 = ClassDescriptors.process_colors_person(pose1,
min_score,
im1,
decode_img=False)
upper2, lower2 = ClassDescriptors.process_colors_person(pose2,
min_score,
im2,
decode_img=False)
img_size = 100
image1 = np.zeros((img_size, img_size, 3), np.uint8)
image2 = np.zeros((img_size, img_size, 3), np.uint8)
image3 = np.zeros((img_size, img_size, 3), np.uint8)
image4 = np.zeros((img_size, img_size, 3), np.uint8)
# Generating elements in list
cv2.namedWindow('aux_window', cv2.WND_PROP_AUTOSIZE)
cv2.imshow('aux_window', np.hstack((im1, im2)))
# BGR format -> Generating
image1[:, :] = (upper1[2], upper1[1], upper1[0])
image2[:, :] = (lower1[2], lower1[1], lower1[0])
image3[:, :] = (upper2[2], upper2[1], upper2[0])
image4[:, :] = (lower2[2], lower2[1], lower2[0])
# Performing custom comparison first
diff_upper = ClassUtils.get_color_diff_rgb(upper1, upper2)
diff_lower = ClassUtils.get_color_diff_rgb(lower1, lower2)
print('Diff upper: {0}'.format(diff_upper))
print('Diff lower: {0}'.format(diff_lower))
cv2.namedWindow('main_window', cv2.WND_PROP_AUTOSIZE)
cv2.imshow(
'main_window',
np.hstack((np.vstack((image1, image2)), np.vstack((image3, image4)))))
cv2.waitKey(0)
# Performing lab conversion and equal L values
diff_upper_lum = ClassUtils.get_color_diff_rgb_lum(upper1, upper2)
diff_lower_lum = ClassUtils.get_color_diff_rgb_lum(lower1, lower2)
# Transform point
upper2_eq = ClassUtils.eq_lum_rgb_colors(upper1, upper2)
lower2_eq = ClassUtils.eq_lum_rgb_colors(lower1, lower2)
print('Diff upper lum: {0}'.format(diff_upper_lum))
print('Diff lower lum: {0}'.format(diff_lower_lum))
image3[:, :] = (upper2_eq[2], upper2_eq[1], upper2_eq[0])
image4[:, :] = (lower2_eq[2], lower2_eq[1], lower2_eq[0])
# Showing again image
cv2.imshow(
'main_window',
np.hstack((np.vstack((image1, image2)), np.vstack((image3, image4)))))
cv2.waitKey(0)
# Destroying all windows
cv2.destroyAllWindows()
print('Done!')
def main():
Tk().withdraw()
instance_pose = ClassOpenPose()
print('Initializing main function')
list_files1 = os.listdir(base1_folder)
hists_1 = read_hists(base1_folder, list_files1)
list_files2 = os.listdir(base2_folder)
hists_2 = read_hists(base2_folder, list_files2)
cum_hists_1 = ClassDescriptors.get_cumulative_hists(hists_1)
cum_hists_2 = ClassDescriptors.get_cumulative_hists(hists_2)
# Processing img
filename1 = '/home/mauricio/Pictures/Poses/bend_left/636453039344460032_1.jpg'
filename2 = '/home/mauricio/Pictures/Poses/left_walk/636550795366450048_420.jpg'
init_dir = '/home/mauricio/Pictures'
options = {'initialdir': init_dir}
filename1 = askopenfilename(**options)
if not filename1:
filename1 = '/home/mauricio/Pictures/2_1.jpg'
ext1 = os.path.splitext(filename1)[1]
if ext1 != '.jpg' and ext1 != '.jpeg':
raise Exception('Extension1 is not jpg or jpeg')
print('Loading filename 2')
filename2 = askopenfilename(**options)
if not filename2:
filename2 = '/home/mauricio/Pictures/2_2.jpg'
ext2 = os.path.splitext(filename2)[1]
if ext2 != '.jpg' and ext2 != '.jpeg':
raise Exception('Extension2 is not jpg or jpeg')
image1 = cv2.imread(filename1)
if image1 is None:
raise Exception('Invalid image in filename {0}'.format(filename1))
image2 = cv2.imread(filename2)
if image2 is None:
raise Exception('Invalid image in filename {0}'.format(filename2))
is_json = True
new_file_1 = filename1.replace('.jpeg', '.json')
new_file_1 = new_file_1.replace('.jpg', '.json')
new_file_2 = filename2.replace('.jpeg', '.json')
new_file_2 = new_file_2.replace('.jpg', '.json')
if not os.path.exists(new_file_1):
print('File not found: {0}'.format(new_file_1))
is_json = False
if not os.path.exists(new_file_2):
print('File not found: {0}'.format(new_file_2))
is_json = False
if not is_json:
poses1 = instance_pose.recognize_image(image1)
poses2 = instance_pose.recognize_image(image2)
if len(poses1) != 1:
raise Exception('Invalid len for pose1: {0}'.format(len(poses1)))
if len(poses2) != 1:
raise Exception('Invalid len for pose2: {0}'.format(len(poses2)))
if not ClassUtils.check_vector_integrity_pos(poses1[0], min_score):
raise Exception('Pose 1 not valid')
if not ClassUtils.check_vector_integrity_pos(poses2[0], min_score):
raise Exception('Pose 2 not valid')
pose1 = poses1[0]
pose2 = poses2[0]
else:
with open(new_file_1, 'r') as f:
obj_json1 = json.loads(f.read())
with open(new_file_2, 'r') as f:
obj_json2 = json.loads(f.read())
pose1 = obj_json1['vector']
pose2 = obj_json2['vector']
if not ClassUtils.check_vector_integrity_pos(pose1, min_score):
raise Exception('Pose 1 not valid')
if not ClassUtils.check_vector_integrity_pos(pose2, min_score):
raise Exception('Pose 2 not valid')
# Plotting
# plot_histograms(hists1)
# Showing first images without transformation
cv2.namedWindow('main_window', cv2.WINDOW_AUTOSIZE)
upper1, lower1 = ClassDescriptors.process_colors_person(pose1,
min_score,
image1,
decode_img=False)
upper2, lower2 = ClassDescriptors.process_colors_person(pose2,
min_score,
image2,
decode_img=False)
print('Upper1 {0} - Lower1 {0}'.format(upper1, lower1))
print('Upper2 {0} - Lower2 {0}'.format(upper2, lower2))
print('Diff1: {0}'.format(ClassUtils.get_color_diff_rgb(upper1, upper2)))
print('Diff2: {0}'.format(ClassUtils.get_color_diff_rgb(lower1, lower2)))
cv2.imshow('main_window', np.hstack((image1, image2)))
print('Press any key to continue')
cv2.waitKey(0)
# Perform image transformation
image_tr = ClassDescriptors.transform_image(image2, cum_hists_2,
cum_hists_1)
upper1, lower1 = ClassDescriptors.process_colors_person(pose1,
min_score,
image1,
decode_img=False)
upper2, lower2 = ClassDescriptors.process_colors_person(pose2,
min_score,
image_tr,
decode_img=False)
print('Diff1: {0}'.format(ClassUtils.get_color_diff_rgb(upper1, upper2)))
print('Diff2: {0}'.format(ClassUtils.get_color_diff_rgb(lower1, lower2)))
cv2.imshow('main_window', np.hstack((image1, image_tr)))
print('Press any key to continue')
cv2.waitKey(0)
cv2.destroyAllWindows()
print('Done!')
