I have a PDF with the art of a ticket for a fundraising dinner. I am providing a mock-up here so you can reproduce my problem:
mock up ticket
I would like to run the following pseudocode:
for i in 1:200
copy "mock up.pdf" to $i.pdf
inject $i into $i.pdf using font "OpenDyslexic" # place the ticket number in the pdf
end
create "final.pdf"
i = 0
for p in 1:20
add page to "final.pdf"
for column in 1:2
for row in 1:5
i = i + 1
inject $i.pdf in "final.pdf" in row, column of page p
end
end
end
Thank you!
I might have a solution:
#!/bin/env python3
# adapted from https://pymupdf.readthedocs.io/en/latest/faq.html#how-to-insert-text
# and https://pymupdf.readthedocs.io/en/latest/faq.html#how-to-combine-single-pages
import fitz # in fact, pip install pymupdf
#from sys import argv # in a future, I might get all the parameter via command line
width, height = fitz.paper_size("A4")
r = fitz.Rect(0, 0, width, height)
doc = fitz.open("mock up.pdf")
page = doc[0]
print("input file information:")
print(doc.metadata)
print("rotation=",page.rotation)
print("cropbox=",page.cropbox)
print("mediabox=",page.mediabox)
print("rect=",page.rect)
page.set_rotation(0)
artsize=page.rect
(nx, ny) = (200,140) # position of the ticket number inside the pdf
(dx, dy) = page.mediabox_size # the displacement of the ticket inside the output pdf
ntickets=25
nrows=5 # of tickets vertically
ncols=2 # of tickets horizontally
ntickets_per_page = nrows*ncols
outpdf = fitz.open()
nrow = ncol = 0
i = 0
while i < ntickets:
if i % ntickets_per_page == 0:
#print("new page for ticket #",i)
newpage = outpdf.new_page()
nrow, ncol = 0, 0
for ncol in range(1,ncols+1):
for nrow in range(1,nrows+1):
i += 1
if i > ntickets:
break
text = "{:04d}".format(i)
locr = fitz.Rect((ncol-1)*dx,(nrow-1)*dy,ncol*dx,nrow*dy)
#print("location of the ticket:", locr)
newpage.show_pdf_page(locr,doc,0)
p = fitz.Point(nx+(ncol-1)*dx,ny+(nrow-1)*dy)
#print("location of the number for ticket ", i, ": ", p)
rc = newpage.insert_text(p, # bottom left of 1st char
text,
fontname="tibo", # Times, bold
fontsize=12,
rotate=0,
)
i -= 1
print("%i lines printed on %i tickets." % (rc, i))
outpdf.save("tmp{:04d}.pdf".format(i))
Related
I am trying to render 2D images of point clouds from different viewpoints and save them as images.
I found a code online which does the same thing but for meshes. I tweaked it a little bit to import the 3D point cloud. But the code does not work and gives back black images. Please help me with this. I am open to use another library too if you know the solution. I just want to render the 2D images. Thank You
Code:
import os.path
import math
import sys
C = bpy.context
D = bpy.data
scene = D.scenes['Scene']
# cameras: a list of camera positions
# a camera position is defined by two parameters: (theta, phi),
# where we fix the "r" of (r, theta, phi) in spherical coordinate system.
# 5 orientations: front, right, back, left, top
cameras = [
(60, 0), (60, 90), (60, 180), (60, 270),
(0, 0)
]
# 12 orientations around the object with 30-deg elevation
# cameras = [(60, i) for i in range(0, 360, 30)]
render_setting = scene.render
# output image size = (W, H)
w = 500
h = 500
render_setting.resolution_x = w
render_setting.resolution_y = h
def main():
argv = sys.argv
argv = argv[argv.index('--') + 1:]
if len(argv) != 2:
print('phong.py args: <3d mesh path> <image dir>')
exit(-1)
model = argv[0]
image_dir = argv[1]
# blender has no native support for off files
# install_off_addon()
# init_camera()
fix_camera_to_origin()
do_model(model, image_dir)
def install_off_addon():
try:
# bpy.ops.preferences.addon_install(
# overwrite=False,
# filepath=os.path.dirname(__file__) +
# '/blender-off-addon/import_off.py'
# )
bpy.ops.preferences.addon_enable(module='import_off')
except Exception as e:
print(e)
print("""Import blender-off-addon failed.
Did you pull the blender-off-addon submodule?
$ git submodule update --recursive --remote
""")
exit(-1)
def init_camera():
cam = D.objects['Camera']
# select the camera object
scene.objects.active = cam
cam.select = True
# set the rendering mode to orthogonal and scale
C.object.data.type = 'ORTHO'
C.object.data.ortho_scale = 2.
def fix_camera_to_origin():
origin_name = 'Origin'
# create origin
try:
origin = D.objects[origin_name]
except KeyError:
bpy.ops.object.empty_add(type='SPHERE')
D.objects['Empty'].name = origin_name
origin = D.objects[origin_name]
origin.location = (0, 0, 0)
cam = D.objects['Camera']
# scene.objects.active = cam
# cam.select = True
if 'Track To' not in cam.constraints:
bpy.ops.object.constraint_add(type='TRACK_TO')
cam.constraints['Track To'].target = origin
cam.constraints['Track To'].track_axis = 'TRACK_NEGATIVE_Z'
cam.constraints['Track To'].up_axis = 'UP_Y'
def do_model(path, image_dir):
name = load_model(path)
center_model(name)
normalize_model(name)
image_subdir = os.path.join(image_dir, name)
for i, c in enumerate(cameras):
move_camera(c)
render()
save(image_subdir, '%s.%d' % (name, i))
# delete_model(name)
def load_model(path):
d = os.path.dirname(path)
ext = path.split('.')[-1]
name = os.path.basename(path).split('.')[0]
# handle weird object naming by Blender for stl files
if ext == 'stl':
name = name.title().replace('_', ' ')
if name not in D.objects:
print('loading :' + name)
if ext == 'stl':
bpy.ops.import_mesh.stl(filepath=path, directory=d,
filter_glob='*.stl')
elif ext == 'off':
bpy.ops.import_mesh.off(filepath=path, filter_glob='*.off')
elif ext == 'obj':
bpy.ops.import_scene.obj(filepath=path, filter_glob='*.obj')
else:
bpy.ops.import_mesh.ply(filepath=path, filter_glob='*.ply')
return name
def delete_model(name):
for ob in scene.objects:
if ob.type == 'MESH' and ob.name.startswith(name):
ob.select = True
else:
ob.select = False
bpy.ops.object.delete()
def center_model(name):
bpy.ops.object.origin_set(type='GEOMETRY_ORIGIN')
D.objects[name].location = (0, 0, 0)
def normalize_model(name):
obj = D.objects[name]
dim = obj.dimensions
print('original dim:' + str(dim))
if max(dim) > 0:
dim = dim / max(dim)
obj.dimensions = dim
print('new dim:' + str(dim))
def move_camera(coord):
def deg2rad(deg):
return deg * math.pi / 180.
r = 3.
theta, phi = deg2rad(coord[0]), deg2rad(coord[1])
loc_x = r * math.sin(theta) * math.cos(phi)
loc_y = r * math.sin(theta) * math.sin(phi)
loc_z = r * math.cos(theta)
D.objects['Camera'].location = (loc_x, loc_y, loc_z)
def render():
bpy.ops.render.render()
def save(image_dir, name):
path = os.path.join(image_dir, name + '.png')
D.images['Render Result'].save_render(filepath=path)
print('save to ' + path)
if __name__ == '__main__':
main()
I'm trying to count the number of piglets that enter and leave a zone. This is important because, in my project, there is a balance underneath the zone that computes the weight of the animals. My goal is to find the pig's weight, so, to achieve that, I will count the number of piglets that enter the zone, and if this number is zero, I have the pig's weight, and according to the number of piglets that get in I will calculate the weight of each as well.
But the weight history is for the future. Currently, I need help in the counting process.
The video can be seen here. The entrance occurs from the minute 00:40 until 02:00 and the exit starts on the minute 03:54 and goes all the way through the video because the piglets start, at this point, to enter and exit the zone.
I've successfully counted the entrance with the code below. I defined a region of interest, very small, and filter the pigs according to their colors. It works fine until the piglets start to move around and get very active, leaving and entering the zone all the time.
I'm out of ideas to proceed with this challenge. If you have any suggestions, please, tell me!
Thanks!!
import cv2
FULL_VIDEO_PATH = "PATH TO THE FULL VIDEO"
MAX_COLOR = (225, 215, 219)
MIN_COLOR = (158, 141, 148)
def get_centroid(x, y, w, h):
x1 = int(w / 2)
y1 = int(h / 2)
cx = x + x1
cy = y + y1
return cx, cy
def filter_mask(frame):
# create a copy from the ROI to be filtered
ROI = (frame[80:310, 615:620]).copy()
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
# create a green rectangle on the structure that creates noise
thicker_line_filtered = cv2.rectangle(ROI, (400, 135), (0, 165), (20, 200, 20), -1)
closing = cv2.morphologyEx(thicker_line_filtered, cv2.MORPH_CLOSE, kernel)
opening = cv2.morphologyEx(closing, cv2.MORPH_OPEN, kernel)
dilation = cv2.dilate(opening, kernel, iterations=2)
# Filter the image according to the colors
segmented_line = cv2.inRange(dilation, MIN_COLOR, MAX_COLOR)
# Resize segmented line only for plot
copy = cv2.resize(segmented_line, (200, 400))
cv2.imshow('ROI', copy)
return segmented_line
def count_pigs():
cap = cv2.VideoCapture(FULL_VIDEO_PATH)
ret, frame = cap.read()
total_pigs = 0
frames_not_seen = 0
last_center = 0
is_position_ok = False
is_size_ok = False
total_size = 0
already_counted = False
while ret:
# Window interval used for counting
count_window_interval = (615, 0, 620, 400)
# Filter frame
fg_mask = filter_mask(frame)
# Draw a line on the frame, which represents when the pigs will be counted
frame_with_line = cv2.line(frame, count_window_interval[0:2], count_window_interval[2:4],(0,0,255), 1)
contours, _ = cv2.findContours(fg_mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# If no contour is found, increments the variable
if len(contours) == 0:
frames_not_seen += 1
# If no contours are found within 5 frames, set last_center to 0 to generate the position difference when
# a new counter is found.
if frames_not_seen > 5:
last_center = 0
for c in contours:
frames_not_seen = 0
# Find the contour coordinates
(x, y, w, h) = cv2.boundingRect(c)
# Calculate the rectangle's center
centroid = get_centroid(x, y, w, h)
# Get the moments from the contour to calculate its size
moments = cv2.moments(c)
# Get contour's size
size = moments['m00']
# Sum the size until count the current pig
if not already_counted:
total_size += size
# If the difference between the last center and the current one is bigger than 80 - which means a new pig
# enter the counting zone - set the position ok and set the already_counted to False to mitigate noises
# with significant differences to be counted
if abs(last_center - centroid[1]) > 80:
is_position_ok = True
already_counted = False
# Imposes limits to the size to evaluate if the contour is consistent
# Min and Max value determined experimentally
if 1300 < total_size < 5500:
is_size_ok = True
# If all conditions are True, count the pig and reset all of them.
if is_position_ok and is_size_ok and not already_counted:
is_position_ok = False
is_size_ok = False
already_counted = True
total_size = 0
total_pigs += 1
last_center = centroid[1]
frame_with_line = cv2.putText(frame_with_line, f'Pigs: {total_pigs}', (100, 370) , cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0,0,0), 2)
cv2.imshow('Frame', frame_with_line)
cv2.moveWindow('ROI', 1130, 0)
cv2.moveWindow('Frame', 0, 0)
k = cv2.waitKey(15) & 0xff
if k == 27:
break
elif k == 32:
cv2.waitKey() & 0xff
ret, frame = cap.read()
cv2.destroyAllWindows()
cap.release()
if __name__ == '__main__':
count_pigs()
I'm trying to take 5 consecutive pixels from each image of a database, and position them consecutively to create a new image of 250x250px. all images in the database are 250x250px.
The Numpy array I'm getting has only 250 items in it, although the database has about 13,000 photos in it. Can someone help me spot the problem?
Current output for 'len(new_img_pxl)' = 250
Illustration
#edit:
from imutils import paths
import cv2
import numpy as np
# access database
database_path = list(paths.list_images('database'))
#grey scale database
img_gray = []
x = -5
y = 0
r = 0
new_img_pxl = []
# open as grayscale, resize
for img_path in database_path:
img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
img_resize = cv2.resize(img, (250, 250))
img_gray.append(img_resize)
# take five consecutive pixel from each image
for item in img_gray:
x += 5
y += 5
five_pix = item[[r][x:y]]
for pix in five_pix:
new_img_pxl.append(pix)
if y == 250:
r += 1
x = -5
y = 0
# convert to array
new_img_pxl_array = np.array(new_img_pxl)
reshape_new_img = new_img_pxl_array.reshape(25,10)
# Convert the pixels into an array using numpy
array = np.array(reshape_new_img, dtype=np.uint8)
new_img_output = cv2.imwrite('new_output_save/001.png',reshape_new_img)
your bug is in the second loop.
for item in img_gray:
for every image (i) in the list img_gray you do:
for a in item:
for each row (j) in the image (i), extract 5 pixels and append them to new_img_pxl.
the first bug is that you don't take just 5 pixels from each image, you take 5 pixels from each row of each image.
your 2nd bug is that after extracting 250 pixels the values of the variables x and y are higher than 250 (the length of a row). As a result, when you try to access the pixels [250:255] and so on you get 'None'.
If I understand your intentions, then the way you should have implemented this is as follows:
r = 0
# As Mark Setchell suggested, you might want to change iterating
# over a list of images to iterating over the list of paths
# for img_path in database_path:
for item in img_gray:
# As Mark Setchell suggested, you might wat to load and
# process your image here, overwriting the past image and
# having the memory released
x += 5
y += 5
# when you finish a row jump to the next?
if x==250:
x = 0
y = 5
r+=1
# not sure what you wanna do when you get to the end of the image.
# roll back to the start?
if r==249 && x==250:
r = 0
x = 0
y = 5
five_pix = a[r, x:y]
for pix in five_pix:
new_img_pxl.append(pix)
In my other question, some of the posters asked to see the code and suggested I make a new question. As requested, here is most of the code I'm using. I've removed the Vector class, simply because it's a lot of code. It's well-understood math that I got from someone else (https://gist.github.com/mcleonard/5351452), and cProfile didn't have much to say about any of the functions there. I've provided a link in the code, if you want to make this run-able.
This code should run, if you paste the the vector class where indicated in the code.
The problem is, once I get above 20 critters, the framerate drops rapidly from 60fps to 11fps around 50 critters.
Please excuse the spaghetti-code. Much of this is diagnostic kludging or pre-code that I intend to either remove, or turn into a behavior (instead of a hard-coded value).
This app is basically composed of 4 objects.
A Vector object provides abstracted vector operations.
A Heat Block is able to track it's own "heat" level, increase it and decrease it. It can also draw itself.
A Heat Map is composed of heat blocks which are tiled across the screen. When given coordinates, it can choose the block that those coordinates fall within.
A Critter has many features that make it able to wander around the screen, bump off of the walls and other critters, choose a new random direction, and die.
The main loop iterates through each critter in the "flock" and updates its "condition" (whether or not it's "dying"), its location, its orientation, and the heat block on which it is currently standing. The loop also iterates over each heat block to let it "cool down."
Then the main loop asks the heat map to draw itself, and then each critter in the flock to draw itself.
import pygame
from pygame import gfxdraw
import pygame.locals
import os
import math
import random
import time
(I got a nice vector class from someone else. It's large, and mostly likely not the problem.)
(INSERT CONTENTS OF VECTOR.PY FROM https://gist.github.com/mcleonard/5351452 HERE)
pygame.init()
#some global constants
BLUE = (0, 0, 255)
WHITE = (255,255,255)
diagnostic = False
SPAWN_TIME = 1 #number of seconds between creating new critters
FLOCK_LIMIT = 30 #number of critters at which the flock begins being culled
GUIDs = [0] #list of guaranteed unique IDs for identifying each critter
# Set the position of the OS window
position = (30, 30)
os.environ['SDL_VIDEO_WINDOW_POS'] = str(position[0]) + "," + str(position[1])
# Set the position, width and height of the screen [width, height]
size_x = 1000
size_y = 500
size = (size_x, size_y)
FRAMERATE = 60
SECS_FOR_DYING = 1
screen = pygame.display.set_mode(size)
screen.set_alpha(None)
pygame.display.set_caption("My Game")
# Used to manage how fast the screen updates
clock = pygame.time.Clock()
def random_float(lower, upper):
num = random.randint(lower*1000, upper*1000)
return num/1000
def new_GUID():
num = GUIDs[-1]
num = num + 1
while num in GUIDs:
num += 1
GUIDs.append(num)
return num
class HeatBlock:
def __init__(self,_tlx,_tly,h,w):
self.tlx = int(_tlx)
self.tly = int(_tly)
self.height = int(h)+1
self.width = int(w)
self.heat = 255.0
self.registered = False
def register_tresspasser(self):
self.registered = True
self.heat = max(self.heat - 1, 0)
def cool_down(self):
if not self.registered:
self.heat = min(self.heat + 0.1, 255)
self.registered = False
def hb_draw_self(self):
screen.fill((255,int(self.heat),int(self.heat)), [self.tlx, self.tly, self.width, self.height])
class HeatMap:
def __init__(self, _h, _v):
self.h_freq = _h #horizontal frequency
self.h_rez = size_x/self.h_freq #horizontal resolution
self.v_freq = _v #vertical frequency
self.v_rez = size_y/self.v_freq #vertical resolution
self.blocks = []
def make_map(self):
h_size = size_x/self.h_freq
v_size = size_y/self.v_freq
for h_count in range(0, self.h_freq):
TLx = h_count * h_size #TopLeft corner, x
col = []
for v_count in range(0, self.v_freq):
TLy = v_count * v_size #TopLeft corner, y
col.append(HeatBlock(TLx,TLy,v_size,h_size))
self.blocks.append(col)
def hm_draw_self(self):
for col in self.blocks:
for block in col:
block.cool_down()
block.hb_draw_self()
def register(self, x, y):
#convert the given coordinates of the trespasser into a col/row block index
col = max(int(math.floor(x / self.h_rez)),0)
row = max(int(math.floor(y / self.v_rez)),0)
self.blocks[col][row].register_tresspasser()
class Critter:
def __init__(self):
self.color = (random.randint(1, 200), random.randint(1, 200), random.randint(1, 200))
self.linear_speed = random_float(20, 100)
self.radius = int(round(10 * (100/self.linear_speed)))
self.angular_speed = random_float(0.1, 2)
self.x = int(random.randint(self.radius*2, size_x - (self.radius*2)))
self.y = int(random.randint(self.radius*2, size_y - (self.radius*2)))
self.orientation = Vector(0, 1).rotate(random.randint(-180, 180))
self.sensor = Vector(0, 20)
self.sensor_length = 20
self.new_orientation = self.orientation
self.draw_bounds = False
self.GUID = new_GUID()
self.condition = 0 #0 = alive, [1-fps] = dying, >fps = dead
self.delete_me = False
def c_draw_self(self):
#if we're alive and not dying, draw our normal self
if self.condition == 0:
#diagnostic
if self.draw_bounds:
pygame.gfxdraw.rectangle(screen, [int(self.x), int(self.y), 1, 1], BLUE)
temp = self.orientation * (self.linear_speed * 20)
pygame.gfxdraw.line(screen, int(self.x), int(self.y), int(self.x + temp[0]), int(self.y + temp[1]), BLUE)
#if there's a new orientation, match it gradually
temp = self.new_orientation * self.linear_speed
#draw my body
pygame.gfxdraw.aacircle(screen, int(self.x), int(self.y), self.radius, self.color)
#draw a line indicating my new direction
pygame.gfxdraw.line(screen, int(self.x), int(self.y), int(self.x + temp[0]), int(self.y + temp[1]), BLUE)
#draw my sensor (a line pointing forward)
self.sensor = self.orientation.normalize() * self.sensor_length
pygame.gfxdraw.line(screen, int(self.x), int(self.y), int(self.x + self.sensor[0]), int(self.y + self.sensor[1]), BLUE)
#otherwise we're dying, draw our dying animation
elif 1 <= self.condition <= FRAMERATE*SECS_FOR_DYING:
#draw some lines in a spinningi circle
for num in range(0,10):
line = Vector(0, 1).rotate((num*(360/10))+(self.condition*23))
line = line*self.radius
pygame.gfxdraw.line(screen, int(self.x), int(self.y), int(self.x+line[0]), int(self.y+line[1]), self.color)
def print_self(self):
#diagnostic
print("==============")
print("radius:", self.radius)
print("color:", self.color)
print("linear_speed:", self.linear_speed)
print("angular_speed:", self.angular_speed)
print("x:", self.x)
print("y:", int(self.y))
print("orientation:", self.orientation)
def avoid_others(self, _flock):
for _critter in _flock:
#if the critter isn't ME...
if _critter.GUID is not self.GUID and _critter.condition == 0:
#and it's touching me...
if self.x - _critter.x <= self.radius + _critter.radius:
me = Vector(self.x, int(self.y))
other_guy = Vector(_critter.x, _critter.y)
distance = me - other_guy
#give me new orientation that's away from the other guy
if distance.norm() <= ((self.radius) + (_critter.radius)):
new_direction = me - other_guy
self.orientation = self.new_orientation = new_direction.normalize()
def update_location(self, elapsed):
boundary = '?'
while boundary != 'X':
boundary = self.out_of_bounds()
if boundary == 'N':
self.orientation = self.new_orientation = Vector(0, 1).rotate(random.randint(-20, 20))
self.y = (self.radius) + 2
elif boundary == 'S':
self.orientation = self.new_orientation = Vector(0,-1).rotate(random.randint(-20, 20))
self.y = (size_y - (self.radius)) - 2
elif boundary == 'E':
self.orientation = self.new_orientation = Vector(-1,0).rotate(random.randint(-20, 20))
self.x = (size_x - (self.radius)) - 2
elif boundary == 'W':
self.orientation = self.new_orientation = Vector(1,0).rotate(random.randint(-20, 20))
self.x = (self.radius) + 2
point = Vector(self.x, self.y)
self.x, self.y = (point + (self.orientation * (self.linear_speed*(elapsed/1000))))
boundary = self.out_of_bounds()
def update_orientation(self, elapsed):
#randomly choose a new direction, from time to time
if random.randint(0, 100) > 98:
self.choose_new_orientation()
difference = self.orientation.argument() - self.new_orientation.argument()
self.orientation = self.orientation.rotate((difference * (self.angular_speed*(elapsed/1000))))
def still_alive(self, elapsed):
return_value = True #I am still alive
if self.condition == 0:
return_value = True
elif self.condition <= FRAMERATE*SECS_FOR_DYING:
self.condition = self.condition + (elapsed/17)
return_value = True
if self.condition > FRAMERATE*SECS_FOR_DYING:
return_value = False
return return_value
def choose_new_orientation(self):
if self.new_orientation:
if (self.orientation.argument() - self.new_orientation.argument()) < 5:
rotation = random.randint(-300, 300)
self.new_orientation = self.orientation.rotate(rotation)
def out_of_bounds(self):
if self.x >= (size_x - (self.radius)):
return 'E'
elif self.y >= (size_y - (self.radius)):
return 'S'
elif self.x <= (0 + (self.radius)):
return 'W'
elif self.y <= (0 + (self.radius)):
return 'N'
else:
return 'X'
# -------- Main Program Loop -----------
# generate critters
flock = [Critter()]
# generate heat map
heatMap = HeatMap(60, 40)
heatMap.make_map()
# set some settings
last_spawn = time.clock()
run_time = time.perf_counter()
frame_count = 0
max_time = 0
ms_elapsed = 1
avg_fps = [1]
# Loop until the user clicks the close button.
done = False
while not done:
# --- Main event loop only processes one event
frame_count = frame_count + 1
for event in pygame.event.get():
if event.type == pygame.QUIT:
done = True
# --- Game logic should go here
#check if it's time to make another critter
if time.clock() - last_spawn > SPAWN_TIME:
flock.append(Critter())
last_spawn = time.clock()
if len(flock) >= FLOCK_LIMIT:
#if we're over the flock limit, cull the herd
counter = FLOCK_LIMIT
for critter in flock[0:len(flock)-FLOCK_LIMIT]:
#this code allows a critter to be "dying" for a while, to play an animation
if critter.condition == 0:
critter.condition = 1
elif not critter.still_alive(ms_elapsed):
critter.delete_me = True
counter = 0
#delete all the critters that have finished dying
while counter < len(flock):
if flock[counter].delete_me:
del flock[counter]
else:
counter = counter+1
#----loop on all critters once, doing all functions for each critter
for critter in flock:
if critter.condition == 0:
critter.avoid_others(flock)
if critter.condition == 0:
heatMap.register(critter.x, critter.y)
critter.update_location(ms_elapsed)
critter.update_orientation(ms_elapsed)
if diagnostic:
critter.print_self()
#----alternately, loop for each function. Speed seems to be similar either way
#for critter in flock:
# if critter.condition == 0:
# critter.update_location(ms_elapsed)
#for critter in flock:
# if critter.condition == 0:
# critter.update_orientation(ms_elapsed)
# --- Screen-clearing code goes here
# Here, we clear the screen to white. Don't put other drawing commands
screen.fill(WHITE)
# --- Drawing code should go here
#draw the heat_map
heatMap.hm_draw_self()
for critter in flock:
critter.c_draw_self()
#draw the framerate
myfont = pygame.font.SysFont("monospace", 15)
#average the framerate over 60 frames
temp = sum(avg_fps)/float(len(avg_fps))
text = str(round(((1/temp)*1000),0))+"FPS | "+str(len(flock))+" Critters"
label = myfont.render(text, 1, (0, 0, 0))
screen.blit(label, (5, 5))
# --- Go ahead and update the screen with what we've drawn.
pygame.display.update()
# --- Limit to 60 frames per second
#only run for 30 seconds
if time.perf_counter()-run_time >= 30:
done = True
#limit to 60fps
#add this frame's time to the list
avg_fps.append(ms_elapsed)
#remove any old frames
while len(avg_fps) > 60:
del avg_fps[0]
ms_elapsed = clock.tick(FRAMERATE)
#track longest frame
if ms_elapsed > max_time:
max_time = ms_elapsed
#print some stats once the program is finished
print("Count:", frame_count)
print("Max time since last flip:", str(max_time)+"ms")
print("Total Time:", str(int(time.perf_counter()-run_time))+"s")
print("Average time for a flip:", str(int(((time.perf_counter()-run_time)/frame_count)*1000))+"ms")
# Close the window and quit.
pygame.quit()
One thing you can already do to improve the performance is to use pygame.math.Vector2 instead of your Vector class, because it's implemented in C and therefore faster. Before I switched to pygame's vector class, I could have ~50 critters on the screen before the frame rate dropped below 60, and after the change up to ~100.
pygame.math.Vector2 doesn't have that argument method, so you need to extract it from the class and turn it into a function:
def argument(vec):
""" Returns the argument of the vector, the angle clockwise from +y."""
arg_in_rad = math.acos(Vector(0,1)*vec/vec.length())
arg_in_deg = math.degrees(arg_in_rad)
if vec.x < 0:
return 360 - arg_in_deg
else:
return arg_in_deg
And change .norm() to .length() everywhere in the program.
Also, define the font object (myfont) before the while loop. That's only a minor improvement, but every frame counts.
Another change that made a significant improvement was to streamline my collision-detection algorithm.
Formerly, I had been looping through every critter in the flock, and measuring the distance between it and every other critter in the flock. If that distance was small enough, I do something. That's n^2 checks, which is not awesome.
I'd thought about using a quadtree, but it didn't seem efficient to rebalance the whole tree every frame, because it will change every time a critter moves.
Well, I finally actually tried it, and it turns out that building a brand-new quadtree at the beginning of each frame is actually plenty fast. Once I have the tree, I pass it to the avoidance function where I just extract an intersection of any of the critters in that tree within a bounding box I care about. Then I just iterate on those neighbors to measure distances and update directions and whatnot.
Now I'm up to 150 or so critters before I start dropping frames (up from 40).
So the moral of the story is, trust evidence instead of intuition.
I'm using R to loop through the columns of a data frame and make a graph of the resulting analysis. I don't get any errors when the script runs, but it generates a pdf that cannot be opened.
If I run the content of the script, it works fine. I wondered if there is a problem with how quickly it is looping through, so I tried to force it to pause. This did not seem to make a difference. I'm interested in any suggestions that people have, and I'm also quite new to R so suggestions as to how I can improve the approach are welcome too. Thanks.
for (i in 2:22) {
# Organise data
pop_den_z = subset(pop_den, pop_den[i] != "0") # Remove zeros
y = pop_den_z[,i] # Get y col
x = pop_den_z[,1] # get x col
y = log(y) # Log transform
# Regression
lm.0 = lm(formula = y ~ x) # make linear model
inter = summary(lm.0)$coefficients[1,1] # Get intercept
slop = summary(lm.0)$coefficients[2,1] # Get slope
# Write to File
a = c(i, inter, slop)
write(a, file = "C:/pop_den_coef.txt", ncolumns = 3, append = TRUE, sep = ",")
## Setup pdf
string = paste("C:/LEED/results/Images/R_graphs/Pop_den", paste(i-2), "City.pdf")
pdf(string, height = 6, width = 9)
p <- qplot(
x, y,
xlab = "Radius [km]",
ylab = "Population Density [log(people/km)]",
xlim = x_range,
main = "Analysis of Cities"
)
# geom_abline(intercept,slope)
p + geom_abline(intercept = inter, slope = slop, colour = "red", size = 1)
Sys.sleep(5)
### close the PDF file
dev.off()
}
The line should be
print(p + geom_abline(intercept = inter, slope = slop, colour = "red", size = 1))
In pdf devices, ggplot (and lattice) only writes to file when explicitly printed.