def main():
if len(sys.argv == 2):
img_path = sys.argv[1]
else:
img_path = DEFAULT_LIVE_IMG_PATH
prediction = Predict(img_path)
p_clothing = []
p_body_type = []
probability = None
# If there was an update to the live img data location
# Get live image/vid data
live_imgs = LoadData(DEFAULT_LIVE_IMG_PATH, (IMAGE_WIDTH, IMAGE_HEIGHT))
live_frame_dataset = live_imgs.get_last_n_frames(n=1)
# Get the initial photos, labels, and location of individuals
initial_people = LoadData(DEFAULT_INDIVIDUAL_IMG_PATH)
person_dataset = initial_people.get_all_frames()
# Given an individual's clothing hist and body type:
# Compare that to the unkown body type and clothing histogram
clothing_prob = ClothingEM()
unkown_clothing_prob = ClothingEM(live_frame_dataset)
unkown_clothing_dist = unkown_clothing_prob.clothing_distribution()
body_prob = BodyTypeClassifier()
for person in person_dataset:
clothing_prob.add_frames(person)
individual_clothing_dist = clothing_prob.clothing_distribution()
# Compare each individual distribution with the unknown distribution and add it to arrays
clothing_prediction = prediction.compare_individual_w_clothing(
unkown_clothing_dist, individual_clothing_dist)
body_prediction = prediction.compare_individual_w_body(
unkown_body_distr, individual_body_distr)
p_clothing.append({person: clothing_prediction})
p_body_type.append({person: body_prediction})
