由于多处理导致脚本之间的范围问题

Question

我正在关注这个tutorial。克隆她的存储库并让“track”命令工作后，我想尝试集成扫描功能。

我进入了她的 manager.py 脚本，并在set_servos 函数中添加了我的扫描程序，如下所示（粗体）。这在servos_process中运行：

import logging
from multiprocessing import Value, Process, Manager, Queue

import pantilthat as pth
import signal
import sys
import time
import RPi.GPIO as GPIO

from rpi_deep_pantilt.detect.util.visualization import visualize_boxes_and_labels_on_image_array
from rpi_deep_pantilt.detect.camera import run_pantilt_detect
from rpi_deep_pantilt.control.pid import PIDController

GPIO.setmode(GPIO.BCM)
GPIO.setwarnings(False)
GPIO.setup(8,GPIO.OUT)

logging.basicConfig()
LOGLEVEL = logging.getLogger().getEffectiveLevel()

RESOLUTION = (320, 320)

SERVO_MIN = -90
SERVO_MAX = 90

CENTER = (
    RESOLUTION[0] // 2,
    RESOLUTION[1] // 2
)


# function to handle keyboard interrupt
def signal_handler(sig, frame):
    # print a status message
    print("[INFO] You pressed `ctrl + c`! Exiting...")

    # disable the servos
    pth.servo_enable(1, False)
    pth.servo_enable(2, False)
    GPIO.output(8,GPIO.LOW)

    # exit
    sys.exit()

def in_range(val, start, end):
    # determine the input value is in the supplied range
    return (val >= start and val <= end)


def set_servos(pan, tilt, scan):
    # signal trap to handle keyboard interrupt
    signal.signal(signal.SIGINT, signal_handler)
    
    **
    #visualize_boxes_and_labels_on_image_array()
    print(scan.value) # output: 't'
    
    while scan.value == 't':**
        print('Scanning')
        pth.servo_one(90)
        pth.servo_two(25)
        time.sleep(10)
    
        pth.servo_one(30)
        pth.servo_two(25)
        time.sleep(10)
            
        pth.servo_one(-30)
        pth.servo_two(25)
        time.sleep(10)
        
        pth.servo_one(-90)
        pth.servo_two(25)
        time.sleep(10)
        
        pth.servo_one(-30)
        pth.servo_two(25)
        time.sleep(10)
            
        pth.servo_one(30)
        pth.servo_two(25)
        pth.time.sleep(10)
            
        pth.servo_one(90)
        pth.servo_two(25)
        time.sleep(10)
        
        continue
    
    while True:
        pan_angle = -1 * pan.value
        tilt_angle = tilt.value
        
        # if the pan angle is within the range, pan
        if in_range(pan_angle, SERVO_MIN, SERVO_MAX):
            pth.pan(pan_angle)
        else:
            logging.info(f'pan_angle not in range {pan_angle}')

        if in_range(tilt_angle, SERVO_MIN, SERVO_MAX):
            pth.tilt(tilt_angle)
        else:
            logging.info(f'tilt_angle not in range {tilt_angle}')

    
    
def pid_process(output, p, i, d, box_coord, origin_coord, action):
    # signal trap to handle keyboard interrupt
    signal.signal(signal.SIGINT, signal_handler)

    # create a PID and initialize it
    p = PIDController(p.value, i.value, d.value)
    p.reset()
    

    # loop indefinitely
    while True:
        error = origin_coord - box_coord.value
        output.value = p.update(error)
        # logging.info(f'{action} error {error} angle: {output.value}')
    

def pantilt_process_manager(
    model_cls,
    labels=('Raspi',),
    rotation=0
):
    
    pth.servo_enable(1, True)
    pth.servo_enable(2, True)
    with Manager() as manager:
        
        **scan = manager.Value('c', 't')**
        
        # set initial bounding box (x, y)-coordinates to center of frame
        center_x = manager.Value('i', 0)
        center_y = manager.Value('i', 0)

        center_x.value = RESOLUTION[0] // 2
        center_y.value = RESOLUTION[1] // 2
        

        # pan and tilt angles updated by independent PID processes
        pan = manager.Value('i', 0)
        tilt = manager.Value('i', 0)

        # PID gains for panning
        pan_p = manager.Value('f', 0.05)
        # 0 time integral gain until inferencing is faster than ~50ms
        pan_i = manager.Value('f', 0.1)
        pan_d = manager.Value('f', 0)

        # PID gains for tilting
        tilt_p = manager.Value('f', 0.15)
        # 0 time integral gain until inferencing is faster than ~50ms
        tilt_i = manager.Value('f', 0.2)
        tilt_d = manager.Value('f', 0)

        **detect_processr = Process(target=run_pantilt_detect,
                                  args=(center_x, center_y, labels, model_cls, rotation, scan))**

        pan_process = Process(target=pid_process,
                              args=(pan, pan_p, pan_i, pan_d, center_x, CENTER[0], 'pan'))

        tilt_process = Process(target=pid_process,
                               args=(tilt, tilt_p, tilt_i, tilt_d, center_y, CENTER[1], 'tilt'))

        **servo_process = Process(target=set_servos, args=(pan, tilt, scan))**
        
        
        detect_processr.start()
        pan_process.start()
        tilt_process.start()
        servo_process.start()
        
        detect_processr.join()
        pan_process.join()
        tilt_process.join()
        servo_process.join()
        
        
if __name__ == '__main__':
    pantilt_process_manager()

在名为visualization.py 的单独脚本中，visualize_boxes_and_labels_on_image_array 函数中有一个语句，该语句负责在检测到对象后在相机源上覆盖边界框，如下所示（以粗体表示）。这在 detect_processr 中运行：

# python
import collections
import logging

# lib
import numpy as np
import PIL.Image as Image
import PIL.ImageColor as ImageColor
import PIL.ImageDraw as ImageDraw
import PIL.ImageFont as ImageFont
import six
import RPi.GPIO as GPIO
import time
from time import sleep
import pantilthat as pth

GPIO.setmode(GPIO.BCM)
GPIO.setwarnings(False)
GPIO.setup(8,GPIO.OUT)


STANDARD_COLORS = [
    'AliceBlue', 'Chartreuse', 'Aqua', 'Aquamarine', 'Azure', 'Beige', 'Bisque',
    'BlanchedAlmond', 'BlueViolet', 'BurlyWood', 'CadetBlue', 'AntiqueWhite',
    'Chocolate', 'Coral', 'CornflowerBlue', 'Cornsilk', 'Crimson', 'Cyan',
    'DarkCyan', 'DarkGoldenRod', 'DarkGrey', 'DarkKhaki', 'DarkOrange',
    'DarkOrchid', 'DarkSalmon', 'DarkSeaGreen', 'DarkTurquoise', 'DarkViolet',
    'DeepPink', 'DeepSkyBlue', 'DodgerBlue', 'FireBrick', 'FloralWhite',
    'ForestGreen', 'Fuchsia', 'Gainsboro', 'GhostWhite', 'Gold', 'GoldenRod',
    'Salmon', 'Tan', 'HoneyDew', 'HotPink', 'IndianRed', 'Ivory', 'Khaki',
    'Lavender', 'LavenderBlush', 'LawnGreen', 'LemonChiffon', 'LightBlue',
    'LightCoral', 'LightCyan', 'LightGoldenRodYellow', 'LightGray', 'LightGrey',
    'LightGreen', 'LightPink', 'LightSalmon', 'LightSeaGreen', 'LightSkyBlue',
    'LightSlateGray', 'LightSlateGrey', 'LightSteelBlue', 'LightYellow', 'Lime',
    'LimeGreen', 'Linen', 'Magenta', 'MediumAquaMarine', 'MediumOrchid',
    'MediumPurple', 'MediumSeaGreen', 'MediumSlateBlue', 'MediumSpringGreen',
    'MediumTurquoise', 'MediumVioletRed', 'MintCream', 'MistyRose', 'Moccasin',
    'NavajoWhite', 'OldLace', 'Olive', 'OliveDrab', 'Orange', 'OrangeRed',
    'Orchid', 'PaleGoldenRod', 'PaleGreen', 'PaleTurquoise', 'PaleVioletRed',
    'PapayaWhip', 'PeachPuff', 'Peru', 'Pink', 'Plum', 'PowderBlue', 'Purple',
    'Red', 'RosyBrown', 'RoyalBlue', 'SaddleBrown', 'Green', 'SandyBrown',
    'SeaGreen', 'SeaShell', 'Sienna', 'Silver', 'SkyBlue', 'SlateBlue',
    'SlateGray', 'SlateGrey', 'Snow', 'SpringGreen', 'SteelBlue', 'GreenYellow',
    'Teal', 'Thistle', 'Tomato', 'Turquoise', 'Violet', 'Wheat', 'White',
    'WhiteSmoke', 'Yellow', 'YellowGreen'
]


def _get_multiplier_for_color_randomness():
    num_colors = len(STANDARD_COLORS)
    prime_candidates = [5, 7, 11, 13, 17]

    # Remove all prime candidates that divide the number of colors.
    prime_candidates = [p for p in prime_candidates if num_colors % p]
    if not prime_candidates:
        return 1

    # Return the closest prime number to num_colors / 10.
    abs_distance = [np.abs(num_colors / 10. - p) for p in prime_candidates]
    num_candidates = len(abs_distance)
    inds = [i for _, i in sorted(zip(abs_distance, range(num_candidates)))]
    return prime_candidates[inds[0]]


def draw_mask_on_image_array(image, mask, color='red', alpha=0.4):
    if image.dtype != np.uint8:
        raise ValueError('`image` not of type np.uint8')
    if mask.dtype != np.uint8:
        raise ValueError('`mask` not of type np.uint8')
    if np.any(np.logical_and(mask != 1, mask != 0)):
        raise ValueError('`mask` elements should be in [0, 1]')
    if image.shape[:2] != mask.shape:
        raise ValueError('The image has spatial dimensions %s but the mask has '
                         'dimensions %s' % (image.shape[:2], mask.shape))
    rgb = ImageColor.getrgb(color)
    pil_image = Image.fromarray(image)

    solid_color = np.expand_dims(
        np.ones_like(mask), axis=2) * np.reshape(list(rgb), [1, 1, 3])
    pil_solid_color = Image.fromarray(np.uint8(solid_color)).convert('RGBA')
    pil_mask = Image.fromarray(np.uint8(255.0*alpha*mask)).convert('L')
    pil_image = Image.composite(pil_solid_color, pil_image, pil_mask)
    np.copyto(image, np.array(pil_image.convert('RGB')))
    

def draw_bounding_box_on_image(image,
                               ymin,
                               xmin,
                               ymax,
                               xmax,
                               color='red',
                               thickness=4,
                               display_str_list=(),
                               use_normalized_coordinates=True):
        
    GPIO.output(8,GPIO.HIGH)
    print('Object Detected')
    
    draw = ImageDraw.Draw(image)
    im_width, im_height = image.size
    if use_normalized_coordinates:
        (left, right, top, bottom) = (xmin * im_width, xmax * im_width,
                                      ymin * im_height, ymax * im_height)
    else:
        (left, right, top, bottom) = (xmin, xmax, ymin, ymax)
    draw.line([(left, top), (left, bottom), (right, bottom),
               (right, top), (left, top)], width=thickness, fill=color)
    try:
        font = ImageFont.truetype('arial.ttf', 24)
    except IOError:
        font = ImageFont.load_default()

    # If the total height of the display strings added to the top of the bounding
    # box exceeds the top of the image, stack the strings below the bounding box
    # instead of above.
    display_str_heights = [font.getsize(ds)[1] for ds in display_str_list]
    # Each display_str has a top and bottom margin of 0.05x.
    total_display_str_height = (1 + 2 * 0.05) * sum(display_str_heights)

    if top > total_display_str_height:
        text_bottom = top
    else:
        text_bottom = bottom + total_display_str_height
    # Reverse list and print from bottom to top.
    for display_str in display_str_list[::-1]:
        text_width, text_height = font.getsize(display_str)
        margin = np.ceil(0.05 * text_height)
        draw.rectangle(
            [(left, text_bottom - text_height - 2 * margin), (left + text_width,
                                                              text_bottom)],
            fill=color)
        draw.text(
            (left + margin, text_bottom - text_height - margin),
            display_str,
            fill='black',
            font=font)
        text_bottom -= text_height - 2 * margin
    
def draw_bounding_box_on_image_array(image,
                                     ymin,
                                     xmin,
                                     ymax,
                                     xmax,
                                     color='red',
                                     thickness=4,
                                     display_str_list=(),
                                     use_normalized_coordinates=True):

    image_pil = Image.fromarray(np.uint8(image)).convert('RGB')
    draw_bounding_box_on_image(image_pil, ymin, xmin, ymax, xmax, color,
                               thickness, display_str_list,
                               use_normalized_coordinates)
    np.copyto(image, np.array(image_pil))
    


def draw_keypoints_on_image(image,
                            keypoints,
                            color='red',
                            radius=2,
                            use_normalized_coordinates=True):

    draw = ImageDraw.Draw(image)
    im_width, im_height = image.size
    keypoints_x = [k[1] for k in keypoints]
    keypoints_y = [k[0] for k in keypoints]
    if use_normalized_coordinates:
        keypoints_x = tuple([im_width * x for x in keypoints_x])
        keypoints_y = tuple([im_height * y for y in keypoints_y])
    for keypoint_x, keypoint_y in zip(keypoints_x, keypoints_y):
        draw.ellipse([(keypoint_x - radius, keypoint_y - radius),
                      (keypoint_x + radius, keypoint_y + radius)],
                     outline=color, fill=color)


def draw_keypoints_on_image_array(image,
                                  keypoints,
                                  color='red',
                                  radius=2,
                                  use_normalized_coordinates=True):
  
    image_pil = Image.fromarray(np.uint8(image)).convert('RGB')
    draw_keypoints_on_image(image_pil, keypoints, color, radius,
                            use_normalized_coordinates)
    np.copyto(image, np.array(image_pil))

def visualize_boxes_and_labels_on_image_array(
        image,
        boxes,
        classes,
        scores,
        category_index,
 **     scan,    **
        instance_masks=None,
        instance_boundaries=None,
        keypoints=None,
        track_ids=None,
        use_normalized_coordinates=False,
        max_boxes_to_draw=20,
        min_score_thresh=.5,
        agnostic_mode=False,
        line_thickness=4,
        groundtruth_box_visualization_color='black',
        skip_scores=False,
        skip_labels=False,
        skip_track_ids=False):
    
    GPIO.output(8,GPIO.LOW)
    
   # Create a display string (and color) for every box location, group any boxes
    # that correspond to the same location.
    box_to_display_str_map = collections.defaultdict(list)
    box_to_color_map = collections.defaultdict(str)
    box_to_instance_masks_map = {}
    box_to_instance_boundaries_map = {}
    box_to_keypoints_map = collections.defaultdict(list)
    box_to_track_ids_map = {}
    if not max_boxes_to_draw:
        max_boxes_to_draw = boxes.shape[0]
    for i in range(min(max_boxes_to_draw, boxes.shape[0])):
        if scores is None or scores[i] > min_score_thresh:
            box = tuple(boxes[i].tolist())
            if instance_masks is not None:
                box_to_instance_masks_map[box] = instance_masks[i]
            if instance_boundaries is not None:
                box_to_instance_boundaries_map[box] = instance_boundaries[i]
            if keypoints is not None:
                box_to_keypoints_map[box].extend(keypoints[i])
            if track_ids is not None:
                box_to_track_ids_map[box] = track_ids[i]
            if scores is None:
                box_to_color_map[box] = groundtruth_box_visualization_color
            else:
                display_str = ''
                if not skip_labels:
                    if not agnostic_mode:
                        if classes[i] in six.viewkeys(category_index):
                            class_name = category_index[classes[i]]['name']
                        else:
                            class_name = 'N/A'
                        display_str = str(class_name)
                if not skip_scores:
                    if not display_str:
                        display_str = '{}%'.format(int(100*scores[i]))
                    else:
                        display_str = '{}: {}%'.format(
                            display_str, int(100*scores[i]))
                if not skip_track_ids and track_ids is not None:
                    if not display_str:
                        display_str = 'ID {}'.format(track_ids[i])
                    else:
                        display_str = '{}: ID {}'.format(
                            display_str, track_ids[i])
                box_to_display_str_map[box].append(display_str)
                if agnostic_mode:
                    box_to_color_map[box] = 'DarkOrange'
                elif track_ids is not None:
                    prime_multipler = _get_multiplier_for_color_randomness()
                    box_to_color_map[box] = STANDARD_COLORS[
                        (prime_multipler * track_ids[i]) % len(STANDARD_COLORS)]
                else:
                    box_to_color_map[box] = STANDARD_COLORS[
                        classes[i] % len(STANDARD_COLORS)]

    # Draw all boxes onto image.
    for box, color in box_to_color_map.items():
        ymin, xmin, ymax, xmax = box
        if instance_masks is not None:
            draw_mask_on_image_array(
                image,
                box_to_instance_masks_map[box],
                color=color
            )
        if instance_boundaries is not None:
            draw_mask_on_image_array(
                image,
                box_to_instance_boundaries_map[box],
                color='red',
                alpha=1.0
            )        
        draw_bounding_box_on_image_array(
            image,
            ymin,
            xmin,
            ymax,
            xmax,
            color=color,
            thickness=line_thickness,
            display_str_list=box_to_display_str_map[box],
            use_normalized_coordinates=use_normalized_coordinates)
        
        **scan.value = 'f'
        print(scan.value) # output: 'f'**
        
        if keypoints is not None:
            draw_keypoints_on_image_array(
                image,
                box_to_keypoints_map[box],
                color=color,
                radius=line_thickness / 2,
                use_normalized_coordinates=use_normalized_coordinates)
    return image

希望一旦检测到对象，扫描功能就会中断。 Visualization.py 在detect_process 中执行，但仅在检测到对象时执行。

这些语句从我的打印语句中正确中继如下，但循环仍然没有中断：

$ rpi-deep-pantilt track Raspi
t
Scanning
Object Detected
f
Object Detected
f
Object Detected
f
Object Detected
f
Object Detected
f
Object Detected
f
Object Detected
f
Object Detected
f
Object Detected
f
Object Detected
f
Object Detected
f
Object Detected
f
Object Detected
f
Object Detected
f
Object Detected
f
Object Detected
f
Object Detected
f
^C

Answer 1

您在这里遇到的问题是内存（通常）在多处理 python 程序的各个部分之间不共享。在这种情况下，scan_on 是 manager.py 脚本中的一个局部变量，然后在visualization.py 脚本中重新实例化。

要在进程之间共享数据，我们可以使用Values。这些是客观化的值，允许通过简单地将其作为参数传递来在进程之间共享状态。

为什么？当您将整数和布尔值等简单值传递给函数时，它们会被复制，而不是在它们的多个实例中维护状态。但是，Value 对象只是将 reference 传递给该值，而不是直接复制。

虽然我没看到你打电话给visualize_boxes_and_labels_on_image_array()，但这是一个开始：

scan_on = manager.Value('c', 't') # the 'c' references the unsigned c-char, 
# other types found here: https://docs.python.org/3/library/array.html#module-array

# we'll edit your set_servo process to include this variable
servo_process = Process(target=set_servos, args=(pan, tilt, scan_on))

当您调用visualize_boxes_and_labels_on_image_array() 时，您还需要传递此与 scan_on 的相同实例。您不必重新声明它。要访问或编辑 scan_on 的值，请使用 .value 字段。

print(scan_on.value) # output: 't'
scan_on.value = 'f'
print(scan_on.value) # output: 'f'