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How to use update_percentage method in avocado

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som.py

Source:som.py

1import sys2from csv import reader3import pygame4import math5import numpy as np6import matplotlib.pyplot as plt7# import random8hexColor = pygame.Color('#61A4BC')9title_color = pygame.Color('black')10rows = 911MATRIX_SIZE = 912MIDDLE_ROW_IDX = 413MIN_ROW_SIZE = 514EPOCHS = 1015radius = 2016colors = ['#91BAD6', '#73A5C6', '#528AAE', '#2E5984', '#1E3F66']17# parse the file received as an argument18def get_input(filePath):19    csvRows = []20    # read CSV file21    with open(filePath) as inputFile:22        csv_reader = reader(inputFile)23        header = next(csv_reader)24        # read each row25        for row in csv_reader:26            title = row[0]27            del row[0]28            csvRows.append([title, [int(val) for val in row]])29    return header, csvRows30# euclidean distance31def euclidean_dist(x1, x2):32    return np.linalg.norm(x1-x2)33# The function receives an input vector and returns the index of the vector closest to it in som,34# and the distance between them35def get_closest_idx_vector(input_vector, matrix):36    min_val = float('inf')37    min_x, min_y = 0, 038    for i in range(MATRIX_SIZE):39        for j in range(MATRIX_SIZE):40            if matrix[i][j] is not None:41                dist = euclidean_dist(input_vector, matrix[i][j])42                # update closest details43                if dist < min_val:44                    min_val = dist45                    min_x, min_y = i, j46    return min_x, min_y, min_val47# initialize random vector, the vector size is the size of the input vectors48def initialize_random_vector(vector_len):49    random_range = 1500050    vector = np.random.randint(0, random_range, vector_len)51    return vector52# Initialize a matrix with 61 random vectors depending on the hexagonal shape,53# so that the other cells in the matrix have none54def initialize_matrix(vector_len):55    matrix = [[None for _ in range(MATRIX_SIZE)] for _ in range(MATRIX_SIZE)]56    # The top of the hexagon57    for i in range(MIDDLE_ROW_IDX + 1):58        for j in range(MIN_ROW_SIZE + i):59            matrix[i][j] = initialize_random_vector(vector_len)60    iteration = 161    # The bottom of the hexagon62    for i in range(MIDDLE_ROW_IDX + 1, MATRIX_SIZE):63        for j in range(MATRIX_SIZE - iteration):64            matrix[i][j] = initialize_random_vector(vector_len)65        iteration += 166    return matrix67# The function gets an index of hexagon and returns the indexes of the neighbors68def get_neighbors(i, j, matrix):69    neighbors = []70    # invalid index71    if not (0 <= i < MATRIX_SIZE) or not (0 <= j < MATRIX_SIZE) or matrix[i][j] is None:72        return neighbors73    # left neighbor74    if j + 1 < MATRIX_SIZE and matrix[i][j + 1] is not None:75        neighbors.append((i, j + 1))76    # right neighbor77    if j - 1 >= 0:78        neighbors.append((i, j - 1))79    # The top of the hexagon80    if i < MIDDLE_ROW_IDX:81        # top left neighbor82        if i - 1 >= 0 and j - 1 >= 0:83            neighbors.append((i - 1, j - 1))84        # top right neighbor85        if i - 1 >= 0 and matrix[i - 1][j] is not None:86            neighbors.append((i - 1, j))87        # bottom left neighbor88        if i + 1 < MATRIX_SIZE and matrix[i + 1][j] is not None:89            neighbors.append((i + 1, j))90        # bottom right neighbor91        if i + 1 < MATRIX_SIZE and j + 1 < MATRIX_SIZE and matrix[i + 1][j + 1] is not None:92            neighbors.append((i + 1, j + 1))93    # The middle row of the hexagon94    elif i == MIDDLE_ROW_IDX:95        # top left neighbor96        if i - 1 >= 0 and j - 1 >= 0:97            neighbors.append((i - 1, j - 1))98        # top right neighbor99        if i - 1 >= 0 and matrix[i - 1][j] is not None:100            neighbors.append((i - 1, j))101        # bottom left neighbor102        if i + 1 < MATRIX_SIZE and j - 1 >= 0 and matrix[i + 1][j - 1] is not None:103            neighbors.append((i + 1, j - 1))104        # bottom right neighbor105        if i + 1 < MATRIX_SIZE and matrix[i + 1][j] is not None:106            neighbors.append((i + 1, j))107    # The bottom of the hexagon108    else:109        # top left neighbor110        if i - 1 >= 0:111            neighbors.append((i - 1, j))112        # top right neighbor113        if i - 1 >= 0 and j + 1 < MATRIX_SIZE and matrix[i - 1][j + 1] is not None:114            neighbors.append((i - 1, j + 1))115        # bottom left neighbor116        if i + 1 < MATRIX_SIZE and j - 1 >= 0 and matrix[i + 1][j - 1] is not None:117            neighbors.append((i + 1, j - 1))118        # bottom right neighbor119        if i + 1 < MATRIX_SIZE and matrix[i + 1][j] is not None:120            neighbors.append((i + 1, j))121    return neighbors122# The function draws an regular polygon for drawing hexagons123def draw_regular_polygon(surface, color, vertex_count, radius, position, width=0):124    n, r = vertex_count, radius125    x, y = position126    pygame.draw.polygon(surface, color, [127        (x + r * math.cos(math.pi / 2 + 2 * math.pi * i / n), y + r * math.sin(math.pi / 2 + 2 * math.pi * i / n))128        for i in range(n)129    ], width)130    pygame.display.update()131# show hexagon map on screen132def show_screen(map_input_vectors, input_vectors):133    pygame.init()134    screen = pygame.display.set_mode([750, 500])135    screen.fill('white')136    running = True137    while running:138        for event in pygame.event.get():139            if event.type == pygame.QUIT:140                running = False141        graphic(map_input_vectors, input_vectors, screen)142# Hexagonal drawing in a particular index received as input143def draw_hexagon_by_index(i, j, color, screen):144    y, x_start = 0, 0145    # init location by index146    if i == 0:147        x_start, y = 300, 100148    elif i == 1:149        x_start, y = 280, 130150    elif i == 2:151        x_start, y = 260, 160152    elif i == 3:153        x_start, y = 240, 190154    elif i == 4:155        x_start, y = 220, 220156    elif i == 5:157        x_start, y = 240, 250158    elif i == 6:159        x_start, y = 260, 280160    elif i == 7:161        x_start, y = 280, 310162    elif i == 8:163        x_start, y = 300, 340164    # draw hexagon165    draw_regular_polygon(screen, color, 6, radius, (x_start + j * 40, y))166# The function returns the socioeconomic average of vectors mapped to hexagon167def get_avg(indexes, input_vectors):168    # if the list is empty, returns -1 (to paint the appropriate hexagon in black)169    if len(indexes) == 0:170        return -1171    sum_economy = 0172    # calculate the average173    for index in indexes:174        sum_economy += input_vectors[index][0]175    return sum_economy / len(indexes)176# The function returns the hexagonal color corresponding to the socioeconomic mean of hexagonal vectors177def get_color(avg):178    if avg == -1:179        return pygame.Color('black')180    if 0 <= avg <= 2:181        return colors[0]182    elif 2 < avg <= 4:183        return colors[1]184    elif 4 < avg <= 6:185        return colors[2]186    elif 6 < avg <= 8:187        return colors[3]188    elif 8 < avg <= 10:189        return colors[4]190# Drawing 61 hexagons as map191def graphic(map_input_vectors, input_vectors, screen):192    num_of_hexagon = 5193    add = True194    for i in range(rows):195        for j in range(num_of_hexagon):196            indexes = map_input_vectors[i][j]197            avg = get_avg(indexes, input_vectors)198            draw_hexagon_by_index(i, j, get_color(avg), screen)199        # determine the number of hexagons in each row200        if num_of_hexagon == 8 and add:201            num_of_hexagon += 1202            add = False203        elif add:204            num_of_hexagon += 1205        else:206            num_of_hexagon -= 1207# Update the som vector to bring it closer to the input vector208def update_vector(input_vector, som_vector, update_percentage):209    # go over every cell and cell and update the value according to the distance210    for i in range(len(input_vector)):211        if input_vector[i] > som_vector[i]:212            dist = input_vector[i] - som_vector[i]213            som_vector[i] += update_percentage * dist214        else:215            dist = som_vector[i] - input_vector[i]216            som_vector[i] -= update_percentage * dist217    return som_vector218# The som algorithm219def som_algorithm(input_vectors, matrix):220    for i in range(EPOCHS):221        for vector in input_vectors:222            # Getting the closet vector223            x, y, dist = get_closest_idx_vector(vector, matrix)224            updated_indexes = []225            # Update the closest vector with an update percentage of 0.8226            update_percentage = 0.8227            updated_vector = update_vector(vector, matrix[x][y], update_percentage)228            matrix[x][y] = updated_vector229            updated_indexes.append((x, y))230            # Getting the neighbors of the closest vector231            neighbors = get_neighbors(x, y, matrix)232            # Update the neighbors of the closest vector with an update percentage of 0.3233            update_percentage = 0.3234            neighbors_deg2 = []235            for n in neighbors:236                if n not in updated_indexes:237                    updated_indexes.append(n)238                    updated_vector = update_vector(vector, matrix[n[0]][n[1]], update_percentage)239                    matrix[n[0]][n[1]] = updated_vector240                    # save the neighbors of the neighbors in a list241                    neighbors_deg2.append(get_neighbors(n[0], n[1], matrix))242            flat_list_neighbors = [x for xs in neighbors_deg2 for x in xs]243            # Update the neighbors of the neighbors of the closest vector with an update percentage of 0.1244            update_percentage = 0.1245            for n in flat_list_neighbors:246                if n not in updated_indexes:247                    updated_indexes.append(n)248                    updated_vector = update_vector(vector, matrix[n[0]][n[1]], update_percentage)249                    matrix[n[0]][n[1]] = updated_vector250    # Creating a map so that in each cell we save the indexes of the vectors251    # that closest to the appropriate vector in the hexagon grid252    sum_distances = 0253    map_input_vectors = [[[] for _ in range(MATRIX_SIZE)] for _ in range(MATRIX_SIZE)]254    for i in range(len(input_vectors)):255        x, y, dist = get_closest_idx_vector(input_vectors[i], matrix)256        map_input_vectors[x][y].append(i)257        sum_distances += dist258    return map_input_vectors, sum_distances / len(input_vectors)259# Get a list of cities mapped to index i, j260def get_cities(i, j, titles, map_input_vectors):261    indexes = map_input_vectors[i][j]262    cities = []263    for index in indexes:264        cities.append(titles[index])265    return cities266# Run the algorithm several times and choose the best solution267def multiple_runs_som(vector_length, input_vectors):268    iterations_num = 10269    best_map = []270    best_matrix = []271    min_dist = float('inf')272    distances = []273    for i in range(iterations_num):274        # run som algorithm with a new random grid275        mtx = initialize_matrix(vector_length)276        map_input_vectors, avg_dist = som_algorithm(input_vectors, mtx)277        distances.append(avg_dist)278        # save best solution279        if avg_dist < min_dist:280            min_dist = avg_dist281            best_matrix = mtx282            best_map = map_input_vectors283    return best_map, best_matrix, distances284# add labels to bar chart285def add_value_label(x_list, y_list):286    for i in range(len(x_list)):287        plt.text(i, y_list[i].round(2), y_list[i].round(2), ha="center")288# Displays a graph for the averages of the distances in the various solutions289def distances_graph(distances):290    iterations = list(range(len(distances)))291    values = list(distances)292    plt.figure(figsize=(10, 5))293    # creating the bar plot294    plt.bar(iterations, values, color='maroon', width=0.4)295    add_value_label(iterations, values)296    plt.xlabel("Solution number")297    plt.ylabel("Average distances")298    plt.title("Average distances in each solution")299    plt.show()300def main():301    header, csvRows = get_input(sys.argv[1])302    vector_length = len(csvRows[0][1])303    # Mixing the input rows -- test for Section C in the exercise304    # random.shuffle(csvRows)305    # Sorting the rows by the economic index -- test for Section C in the exercise306    # csvRows = sorted(csvRows, key=lambda x: x[1][0])307    input_vectors = []308    titles = []309    # save the input vectors and the titles in arrays310    for row in csvRows:311        input_vectors.append(row[1])312        titles.append(row[0])313    # run the algorithm 10 times and get the best solution314    map_input_vectors, mtx, distances_for_graph = multiple_runs_som(vector_length, input_vectors)315    # display graph316    distances_graph(distances_for_graph)317    # show hexagon map318    show_screen(map_input_vectors, input_vectors)319    # print the cities mapped to each hexagon320    for i in range(MATRIX_SIZE):321        for j in range(MATRIX_SIZE):322            if mtx[i][j] is not None:323                print(i, j, " : ", get_cities(i, j, titles, map_input_vectors))324if __name__ == '__main__':...

backend_of_ui.py

Source:backend_of_ui.py

...204        print("back to UI")205        print(val)206        self.count = val207        self.totalfaces.setText(str(int(val))+' faces detected in video')208    def update_percentage(self,percentage):209        self.progressBar.setValue(percentage)210class ThreadClass(QtCore.QThread):211    update_progressbar = pyqtSignal(float)212    def __init__(self,temp, parent=None):213        super(ThreadClass,self).__init__(parent)214        self.temp = temp215    def run(self):216        count = vid_frame_cap.extract_to_folder(self.temp)217        print("thread complete")218        self.update_progressbar.emit(count)219class videorecog(QtCore.QThread):220    update_videorecog = pyqtSignal(str)221    update_percentage = pyqtSignal(int)222    def __init__(self,files, parent=None):...

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