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terminated by signal SIGSEGV (Address boundary error) on python program with reccursive methods

I am trying to read all the touching pixels with the same color in a image.
For that I use reccursive functions. When I check one pixel, I look on the right, left, top and bottom if the pixel close to it is the same color. If it is I add it to an array otherwise I don't.
The code is as follow:
vimport tkinter as tk
from PIL import Image
import sys
sys.setrecursionlimit(200000)
## WINDOWS
# to launch in debug mode
imgToDraw = Image.open('assets-test\\smile-face.png')
# to launch normaly
# imgToDraw = Image.open('..\\assets-test\\smile-face.png')
## LINUX
# imgToDraw = Image.open('../assets-test/smile-face.png')
imgPixels = imgToDraw.load()
imgWidth = imgToDraw.size[0]
imgHeight = imgToDraw.size[1]
# an element is a part of the image, it's a bunch of pixels with approximately the same color
# and each pixel touch at least one other pixel of the same element
elements = [];
isPixelChecked = [[ False for y in range( imgWidth ) ] for x in range( imgHeight )]
# min tolerable difference between two colors to consider them the same
# the higher the value is the more colors will be considered the same
COLOR_TOLERANCE = 10
reccursionCount = 0
class Element:
def __init__(self, color):
self.pixels = [];
self.color = color;
def addPixel(self, pixel):
self.pixels.append(pixel);
class Pixel:
def __init__(self, x, y, color):
self.x = x # x position of the pixel
self.y = y # y position of the pixel
self.color = color # color is a tuple (r,g,b)
def cutImageInElements():
global element
completeElement(element.pixels)
def completeElement(elemPixels):
global reccursionCount
global isPixelChecked
reccursionCount += 1
nbPixels = len(elemPixels);
xIndex = elemPixels[nbPixels - 1].x
yIndex = elemPixels[nbPixels - 1].y
xRightIdx = elemPixels[nbPixels - 1].x + 1
xLeftIdx = elemPixels[nbPixels - 1].x - 1
yBottomIdx = elemPixels[nbPixels - 1].y + 1
yTopIdx = elemPixels[nbPixels - 1].y - 1
isPixelChecked[xIndex][yIndex] = True
if((xRightIdx < imgWidth) and isPixelChecked[xRightIdx][yIndex] == False):
if(isColorAlmostSame(imgPixels[elemPixels[0].x, elemPixels[0].y], imgPixels[xRightIdx, yIndex])):
pixelAppended = Pixel(xRightIdx, yIndex, imgPixels[xRightIdx, yIndex])
elemPixels.append(pixelAppended)
completeElement(elemPixels)
if((xLeftIdx >= 0) and isPixelChecked[xLeftIdx][yIndex] == False):
if(isColorAlmostSame(imgPixels[elemPixels[0].x, elemPixels[0].y], imgPixels[xLeftIdx, yIndex])):
pixelAppended = Pixel(xLeftIdx, yIndex, imgPixels[xLeftIdx, yIndex])
elemPixels.append(pixelAppended)
completeElement(elemPixels)
if((yBottomIdx < imgHeight) and isPixelChecked[xIndex][yBottomIdx] == False):
if(isColorAlmostSame(imgPixels[elemPixels[0].x, elemPixels[0].y], imgPixels[xIndex, yBottomIdx])):
pixelAppended = Pixel(xIndex, yBottomIdx, imgPixels[xIndex, yBottomIdx])
elemPixels.append(pixelAppended)
completeElement(elemPixels)
if((yTopIdx >= 0) and isPixelChecked[xIndex][yTopIdx] == False):
if(isColorAlmostSame(imgPixels[elemPixels[0].x, elemPixels[0].y], imgPixels[xIndex, yTopIdx])):
pixelAppended = Pixel(xIndex, yTopIdx, imgPixels[xIndex, yTopIdx])
elemPixels.append(pixelAppended)
completeElement(elemPixels)
def isColorAlmostSame(pixel1, pixel2):
redDiff = abs(pixel1[0] - pixel2[0])
greenDiff = abs(pixel1[1] - pixel2[1])
blueDiff = abs(pixel1[2] - pixel2[2])
if(redDiff < COLOR_TOLERANCE and greenDiff < COLOR_TOLERANCE and blueDiff < COLOR_TOLERANCE):
return True
else:
return False
def printPixelsArr(pixelsArr):
for x in range(0, len(pixelsArr)):
print(pixelsArr[x].x, pixelsArr[x].y, pixelsArr[x].color)
if __name__ == '__main__':
pixel = Pixel(0, 0, imgPixels[0, 0]);
element = Element(pixel.color);
element.addPixel(pixel);
cutImageInElements();
print("NbReccursive call: ", reccursionCount)
This code works for small images of size 100x100 but crashes with an image of 400x400 with the error "terminated by signal SIGSEGV (Address boundary error)" when I launch the program on wsl2. When I run the program on cmd or powershell it just crashes but with no error code/msg.
I cannot understand why it would work with some size of images and not others. I can only think that the memory runs out or something but in the task manager the program uses almost no memory.

Not sure why that's failing, but that much recursion in Python isn't a great idea. I'd suggest reading about tail recursion that other languages use to make some recursive algorithms consume constant stack space. Note that your algorithm is not tail recursive, so this optimisation wouldn't help even if Python supported it.
I hacked together the following flood fill implementation. It uses Numpy so that it's only 10x slower than Pillow's ImageDraw.floodfill.
import numpy as np
def floodfill(im, row, col, threshold):
similar = np.mean(np.abs(im - im[row, col]), 2) < threshold
mask = np.zeros_like(similar)
mask[row, col] = 1
m2 = mask.copy()
while True:
m2[:,:] = mask
m2[1:,:] |= mask[:-1]
m2[:-1,:] |= mask[1:]
m2[:,1:] |= mask[:,:-1]
m2[:,:-1] |= mask[:,1:]
m2 &= similar
if np.all(m2 == mask):
return mask
mask[:,:] = m2
As an example of using this, you could do;
import requests
from io import BytesIO
res = requests.get("https://picsum.photos/300")
res.raise_for_status()
src = Image.open(BytesIO(res.content))
mask = floodfill(np.array(src, int), 10, 10, 40)
where the random image I got and the output mask are:

csv file uploaded to s3 using boto3 is empty in s3

I have two csv files that i am uploading from an ec2 instance to the s3 bucket along with a few other files. All the other files are being uploaded just fine but my csv files, though it is uploaded, there seems ot be no data inside it even though the local copy of the file on the instance is showing the data. im not sure why its saying 0 bytes on the bucket.
the csv file is part of another larger program. here is the code.
from boto3.session import Session
import botocore
import boto3
import zipfile
import darknet
import os
import cv2
import glob
import csv
import numpy as np
global lat_start, lon_start
import shutil
#HELPER FUNCTION DEFINITIONS
ACCESS_KEY = '*********'
SECRET_KEY = '******D'
def image_detection(image_path, network, class_names, class_colors, thresh):
# Darknet doesn't accept numpy images.
# Create one with image we reuse for each detect
width = darknet.network_width(network)
height = darknet.network_height(network)
darknet_image = darknet.make_image(width, height, 3)
image = cv2.imread(image_path)
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image_resized = cv2.resize(image_rgb, (width, height),interpolation=cv2.INTER_LINEAR)
darknet.copy_image_from_bytes(darknet_image, image_resized.tobytes())
detections = darknet.detect_image(network, class_names, darknet_image, thresh=thresh)
darknet.free_image(darknet_image)
image = darknet.draw_boxes(detections, image_resized, class_colors)
return cv2.cvtColor(image, cv2.COLOR_BGR2RGB), detections
def discretize_line(lat_start, lon_start, d_element, d, bearing):
# d_element -> how many element we need in a line secment
# global lat_start, lon_start
R = 6371.0*1000.0
# -1 because in case of 10 elements/points we also want len(lat_array) the same
dstep = d/(d_element-1) #0.6524896365354135 #2.0 # meters
dist_list = np.ones(int(d/dstep))*dstep
# print(dist_list)
brg = np.radians(bearing)
# if d%dstep != 0:
# dist_list = np.append(dist_list, d%dstep)
# This will append lat and lon into array which contains
# small segments of distance
lat_array = np.array([np.radians(lat_start)]) # rads
lon_array = np.array([np.radians(lon_start)]) # rads
# lat_array = np.array([])
# lon_array = np.array([])
for i, dist in enumerate(dist_list):
## last element make the waypoint shifted, so we break it
if i >= (d_element):
break
lat1 = lat_array[i]
lon1 = lon_array[i]
# print(dist)
Ad = dist/R
lat2 = np.arcsin(np.sin(lat1)*np.cos(Ad) + np.cos(lat1)*np.sin(Ad)*np.cos(brg))
lon2 = lon1 + np.arctan2( (np.sin(brg)*np.sin(Ad)*np.cos(lat1)) , (np.cos(Ad) - np.sin(lat1)*np.sin(lat2)))
lat_array = np.append(lat_array, lat2)
lon_array = np.append(lon_array, lon2)
# print(i)
return lat_array, lon_array
def get_distance_bearing(lat1, lon1, lat2, lon2):
# global lat_start, lon_start
R = 6371.0*1000.0
lat_start = np.radians(lat1)
lon_start = np.radians(lon1)
lat_end = np.radians(lat2)
lon_end = np.radians(lon2)
dLat = lat_end - lat_start
dLon = lon_end - lon_start
a = np.sin(dLat/2.0)*np.sin(dLat/2.0) + np.cos(lat_start)*np.cos(lat_end)*np.sin(dLon/2.0)*np.sin(dLon/2.0)
c = 2.0*np.arctan2(np.sqrt(a),np.sqrt(1-a))
d = c*R
y = np.sin(dLon)*np.cos(lat_end)
x = np.cos(lat_start)*np.sin(lat_end) - np.sin(lat_start)*np.cos(lat_end)*np.cos(dLon)
bearing = np.degrees(np.arctan2(y,x))
return d, bearing
def upload_to_aws(local_file, bucket, s3_file):
s3 = boto3.client('s3', aws_access_key_id=ACCESS_KEY,
aws_secret_access_key=SECRET_KEY)
try:
s3.upload_file(local_file, bucket, s3_file)
print("Upload Successful")
return True
except FileNotFoundError:
print("The file was not found")
return False
except NoCredentialsError:
print("Credentials not available")
return False
##END OF FUNCTION DEFINITIONS ##
#Unzip the zip file and its contents
print("unzipping")
path_to_zip_file = "/home/ubuntu/pano/Zip/Videos.zip"
with zipfile.ZipFile(path_to_zip_file, 'r') as zip_ref:
zip_ref.extractall("/home/ubuntu/pano/Video")
print("Finished Unzipping")
#End of Unzip
# CSV open and declaration##
data_file_path = "/home/ubuntu/pano/stack/quantity.csv"
data_file = open(data_file_path, "w+")
dataCSVWriter = csv.writer(data_file, delimiter=',',quotechar='|', quoting=csv.QUOTE_MINIMAL)
dataCSVWriter.writerow(['lat', 'lon', 'Quantity'])
#CSV for lane thumbnail
thumbnail_data_file_path = "/home/ubuntu/pano/stack/lane_thumbnail.csv"
thumbnail_data_file = open(thumbnail_data_file_path, "w+")
thumbnail_dataCSVWriter = csv.writer(thumbnail_data_file, delimiter=',',quotechar='|', quoting=csv.QUOTE_MINIMAL)
thumbnail_dataCSVWriter.writerow(['lat', 'lon'])
#Define start and end point lists
#start_point_list = [(35.841454251754755, 139.52427014959153),(35.84147944801779, 139.52420150963678)]
start_point_list = [(36.12083710338884, 139.21630320454503),(36.12080527337101, 139.2164926108044)]
#end_point_list = [(35.84151350159559, 139.52424466860762),(35.84144222040454, 139.52422739581436)]
end_point_list = [(36.12083735438514, 139.2164757318577),(36.12081575161991, 139.21630345327617)]
wp_lat_array = np.array([])
wp_lon_array = np.array([])
##Split th eline into points and it is stored in lat array lon array
"""for i in range(len(start_point_list)):
## input two points and find a slicing waypoint between it
distance, bearing_deg = get_distance_bearing(start_point_list[i][0], start_point_list[i][1], end_point_list[i][0], end_point_list[i][1])
print(distance)
lat_array, lon_array = discretize_line(start_point_list[i][0], start_point_list[i][1], float(d_element[i]), distance, bearing_deg)"""
#Initialize the detector variables and paths
quantity_bottles_frame = []
config_file = "/home/ubuntu/darknet_bottle_example/yolov4_bottle_can.cfg"
data_file = "/home/ubuntu/darknet_bottle_example/obj_bottle_can.data"
weights = "/home/ubuntu/darknet_bottle_example/yolov4_bottle_can_best.weights"
network, class_names, class_colors = darknet.load_network(
config_file,
data_file,
weights,
batch_size=1
)
image_dir = "/home/ubuntu/pano/Frames"
#1.Split into frames
path = "/home/ubuntu/pano/Video/Panorama/Videos"
j = 0
"""Order of events
1. Split into frames
2. Rotate images if needed
3. Running through detctor
4. Calculate count and draw bounding boxes
5. Store these images in respective directoies
6. Take start point of lane and end point and split into many coordinates in between based on number of frames
7. Write to csv file
8. Stack the images per lane
9. Empty the Frames folder after every lane
10. Upload stacked images and csv to cloud """
# Parameter to change is fps in the ffmpeg command. Change accoprding to need based on reference
for filename in os.listdir(path):
if (filename.endswith(".mp4")): #or .avi, .mpeg, whatever.
j += 1
path1 = path + filename
print(path1)
os.system("ffmpeg -i /home/ubuntu/pano/Video/Panorama/Videos/{0} -vf fps=0.07 /home/ubuntu/pano/Frames/{1}-%3d.jpg".format(filename,j))
#2. Rotate images if needed
frames_path = "/home/ubuntu/pano/Frames/*.jpg"
list_images = glob.glob(frames_path)
list_sorted = sorted(list_images)
#for image in list_sorted:
#read the image
# temp = cv2.imread(image)
# image1 = cv2.rotate(temp, cv2.ROTATE_90_COUNTERCLOCKWISE)
# cv2.imwrite("{0}".format(image), image1)
## according to how many partial panorama we have in each lane
d_element =[len(list_images)]
print(f"Now detecting objects in lane {j}")
#3. Running through detctor
frame_number = 1
for image in sorted(os.listdir(image_dir)):
#Path to the input images for the detector i.e Frames
quantity_frame = 0
image_name = f"{image}"
ext = '.jpg'
input_image_name = image_name
image_path = os.path.join(image_dir, input_image_name)
print(image_path)
#Path to output images to be stored after running through detector
output_dir = f"/home/ubuntu/pano/lane{j}"
output_name = "yolo_" + image_name
output_path = os.path.join(output_dir, output_name)
# image = load_images(image_path)
dn_frame_width = 416
dn_frame_height = 416
frame = cv2.imread(image_path)
frame_width = frame.shape[1]
frame_height = frame.shape[0]
#### Passing the image to darknet
image, detections = image_detection(image_path, network, class_names, class_colors, thresh=0.05)
#cv2.imwrite(f'/home/ubuntu/temp/Inference{frame_number}.jpg', image)
#cv2.imwrite(f'/home/ubuntu/temp/orignal_detect{frame_number}.jpg', frame)
###Based on the detections, running them through a loop to draw bounding box and also incrememnt count of object in the frame
#4. Calculate count and draw bounding boxes
for i in range(len(detections)):
xc_percent = detections[i][2][0]/dn_frame_width
yc_percent = detections[i][2][1]/dn_frame_height
w_percent = detections[i][2][2]/dn_frame_width
h_percent = detections[i][2][3]/dn_frame_height
xc = xc_percent*frame_width
yc = yc_percent*frame_height
w = w_percent*frame_width
h = h_percent*frame_height
xmin = xc - w/2.0
ymin = yc - h/2.0
xmax = xc + w/2.0
ymax = yc + h/2.0
#If object is detected, increase the count of the object in the frame
if detections[i][0] == "bottle":
cv2.rectangle(frame, (int(xmin),int(ymin)),(int(xmax),int(ymax)),(0,0,255),2)
cv2.putText(frame, "bottle", (int(xmin), int(ymin-10)), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0,0,255), 2)
quantity_frame += 1
elif detections[i][0] == "can":
cv2.rectangle(frame, (int(xmin),int(ymin)),(int(xmax),int(ymax)),(255,0,0),2)
cv2.putText(frame, "can", (int(xmin), int(ymin-10)), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255,0,0), 2)
else:
print(f"{image} has no objects ")
print(f"Quantity in frame {frame_number} = {quantity_frame}")
#5. Store these images in respective directoies
cv2.imwrite(output_path, frame)
quantity_bottles_frame.append(quantity_frame)
frame_number += 1
###Split the points into equidistant points between start point and end point
##6. Take start point of lane and end point and split into many coordinates in between based on number of frames
distance, bearing_deg = get_distance_bearing(start_point_list[j-1][0], start_point_list[j-1][1], end_point_list[j-1][0], end_point_list[j-1][1])
print(distance)
lat_array, lon_array = discretize_line(start_point_list[j-1][0], start_point_list[j-1][1], float(d_element[0]), distance, bearing_deg)
lat_csv = []
lon_csv = []
##Convery those points into degrees
for lat,lon in zip(lat_array, lon_array):
lat_degrees = "{:}".format(np.degrees(lat))
lon_degrees = "{:}".format(np.degrees(lon))
lat_csv.append(lat_degrees)
lon_csv.append(lon_degrees)
#lat_csv = "{:}".format(np.degrees(lat))
#lon_csv = "{:}".format(np.degrees(lon))
##7.Write each row in the csv file
for k in range(d_element[0]):
dataCSVWriter.writerow([lat_csv[k], lon_csv[k], quantity_bottles_frame[k]])
#if k != d_element[0]-1:
# dataCSVWriter.writerow([lat_csv[k], lon_csv[k], quantity_bottles_frame[k], "-", "-" ])
if k ==d_element[0]-1:
print(lat_csv[int(d_element[0]/2)])
thumbnail_dataCSVWriter.writerow([ lat_csv[int(d_element[0]/2)],lon_csv[int(d_element[0]/2)]])
#####8.STACKING THE IMAGES ######
images = []
stacking_input = f"/home/ubuntu/pano/lane{j}/*.jpg"
list_images = glob.glob(stacking_input)
#print(list_images)
stacking_input_reverse = sorted(list_images, reverse = True)
print(stacking_input_reverse)
for image in stacking_input_reverse:
img = cv2.imread(image)
images.append(img)
final_image = cv2.hconcat(images)
image_name = f"cloud_lane{j}_stack.jpg"
stacking_output = f"/home/ubuntu/pano/stack"
output_path = os.path.join(stacking_output, image_name)
cv2.imwrite(output_path, final_image)
##### 9. DELETE FRAMES AFTER ONE ITERATION OF LOOP #####
for f in os.listdir(image_dir):
del_path = "/home/ubuntu/pano/Frames/" + f
os.remove(del_path)
else:
continue
#Close csv file
#data_file.close()
#thumbnail_data_file.close()
### 10. Upload to s3 bucket ####
stack_path = "/home/ubuntu/pano/stack"
for file in sorted(os.listdir(stack_path)):
print(f"Uploading {file}")
uploaded = upload_to_aws(f'/home/ubuntu/pano/stack/{file}', 'fbt-pano-test', f'{file}')
Do i need to close the csv file in any way? Or does s3 not support csv upload through boto3?

I found it. Turns out, the csv files werent closed at the end. So i moved the upload to s3 part to another program. now python closes the csv files at the end of this program automatically. and so when the upload program runs next, it gets uploaded properly.

PinoroEnviro+ TypeError: argument should be integer or bytes-like object, not 'str'

I have been trying to understand the error reported when I run an example supplied with some hardware i purchased.
I have tried googling around but every answer I get is a bit beyond my comprehension. I think what is going wrong is that the script, or one of the imported scripts is written for Python 2 and i am trying to run it in python 3.
When I try and run it in Python 2 i get a whole host of other problems so I have been trying to make it work with 3.
The hardware I purchased is the Enviro+ sensor suite for the raspberry pi sold by Pimoroni
Hardware Link
Github Library
Pimoroni Tutorial
#!/usr/bin/env python
import time
import colorsys
import os
import sys
import ST7735
import ltr559
from bme280 import BME280
from pms5003 import PMS5003
from enviroplus import gas
from subprocess import PIPE, Popen
from PIL import Image
from PIL import ImageDraw
from PIL import ImageFont
print("""all-in-one.py - Displays readings from all of Enviro plus' sensors
Press Ctrl+C to exit!
""")
# BME280 temperature/pressure/humidity sensor
bme280 = BME280()
# PMS5003 particulate sensor
pms5003 = PMS5003()
# Create ST7735 LCD display class
st7735 = ST7735.ST7735(
port=0,
cs=1,
dc=9,
backlight=12,
rotation=270,
spi_speed_hz=10000000
)
# Initialize display
st7735.begin()
WIDTH = st7735.width
HEIGHT = st7735.height
# Set up canvas and font
img = Image.new('RGB', (WIDTH, HEIGHT), color=(0, 0, 0))
draw = ImageDraw.Draw(img)
path = os.path.dirname(os.path.realpath(__file__))
font = ImageFont.truetype(path + "/fonts/Asap/Asap-Bold.ttf", 20)
message = ""
# The position of the top bar
top_pos = 25
# Displays data and text on the 0.96" LCD
def display_text(variable, data, unit):
# Maintain length of list
values[variable] = values[variable][1:] + [data]
# Scale the values for the variable between 0 and 1
colours = [(v - min(values[variable]) + 1) / (max(values[variable])
- min(values[variable]) + 1) for v in values[variable]]
# Format the variable name and value
message = "{}: {:.1f} {}".format(variable[:4], data, unit)
print(message)
draw.rectangle((0, 0, WIDTH, HEIGHT), (255, 255, 255))
for i in range(len(colours)):
# Convert the values to colours from red to blue
colour = (1.0 - colours[i]) * 0.6
r, g, b = [int(x * 255.0) for x in colorsys.hsv_to_rgb(colour,
1.0, 1.0)]
# Draw a 1-pixel wide rectangle of colour
draw.rectangle((i, top_pos, i+1, HEIGHT), (r, g, b))
# Draw a line graph in black
line_y = HEIGHT - (top_pos + (colours[i] * (HEIGHT - top_pos)))\
+ top_pos
draw.rectangle((i, line_y, i+1, line_y+1), (0, 0, 0))
# Write the text at the top in black
draw.text((0, 0), message, font=font, fill=(0, 0, 0))
st7735.display(img)
# Get the temperature of the CPU for compensation
def get_cpu_temperature():
process = Popen(['vcgencmd', 'measure_temp'], stdout=PIPE)
output, _error = process.communicate()
return float(output[output.index('=') + 1:output.rindex("'")])
# Tuning factor for compensation. Decrease this number to adjust the
# temperature down, and increase to adjust up
factor = 0.8
cpu_temps = [0] * 5
delay = 0.5 # Debounce the proximity tap
mode = 0 # The starting mode
last_page = 0
light = 1
# Create a values dict to store the data
variables = ["temperature",
"pressure",
"humidity",
"light",
"oxidised",
"reduced",
"nh3",
"pm1",
"pm25",
"pm10"]
values = {}
for v in variables:
values[v] = [1] * WIDTH
# The main loop
try:
while True:
proximity = ltr559.get_proximity()
# If the proximity crosses the threshold, toggle the mode
if proximity > 1500 and time.time() - last_page > delay:
mode += 1
mode %= len(variables)
last_page = time.time()
# One mode for each variable
if mode == 0:
variable = "temperature"
unit = "C"
cpu_temp = get_cpu_temperature()
# Smooth out with some averaging to decrease jitter
cpu_temps = cpu_temps[1:] + [cpu_temp]
avg_cpu_temp = sum(cpu_temps) / float(len(cpu_temps))
raw_temp = bme280.get_temperature()
data = raw_temp - ((avg_cpu_temp - raw_temp) / factor)
display_text(variable, data, unit)
if mode == 1:
variable = "pressure"
unit = "hPa"
data = bme280.get_pressure()
display_text(variable, data, unit)
if mode == 2:
variable = "humidity"
unit = "%"
data = bme280.get_humidity()
display_text(variable, data, unit)
if mode == 3:
variable = "light"
unit = "Lux"
if proximity < 10:
data = ltr559.get_lux()
else:
data = 1
display_text(variable, data, unit)
if mode == 4:
variable = "oxidised"
unit = "kO"
data = gas.read_all()
data = data.oxidising / 1000
display_text(variable, data, unit)
if mode == 5:
variable = "reduced"
unit = "kO"
data = gas.read_all()
data = data.reducing / 1000
display_text(variable, data, unit)
if mode == 6:
variable = "nh3"
unit = "kO"
data = gas.read_all()
data = data.nh3 / 1000
display_text(variable, data, unit)
if mode == 7:
variable = "pm1"
unit = "ug/m3"
data = pms5003.read()
data = data.pm_ug_per_m3(1.0)
display_text(variable, data, unit)
if mode == 8:
variable = "pm25"
unit = "ug/m3"
data = pms5003.read()
data = data.pm_ug_per_m3(2.5)
display_text(variable, data, unit)
if mode == 9:
variable = "pm10"
unit = "ug/m3"
data = pms5003.read()
data = data.pm_ug_per_m3(10)
display_text(variable, data, unit)
# Exit cleanly
except KeyboardInterrupt:
sys.exit(0)
When I try and run the code i get the following results:
Traceback (most recent call last):
File "all-in-one.py", line 135, in <module>
cpu_temp = get_cpu_temperature()
File "all-in-one.py", line 89, in get_cpu_temperature
return float(output[output.index('=') + 1:output.rindex("'")])
TypeError: argument should be integer or bytes-like object, not 'str'
Please forgive me if I have not filled this help request out correctly - i am very new to forums (I hardly ever post in them, although i read them a lot for help), and i am also very new to Python and Linux.
Any help and support from the community would be massively appreciated - thank you in advance...
SW

According to Python 3 whitepages on subprocess.communicate(), the type of output and _error can be either strings (what you want) OR bytes. If you were getting strings back, you wouldn't have this problem, but the TypeError message you're getting is exactly what you get when you try to call index() on a bytes object with a string argument.
Demonstrably:
>>> output = "temperature = '88 C'".encode('utf-8') #this is of type bytes
>>> output
b"temperature = '88 C'"
>>> output.index('=')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: argument should be integer or bytes-like object, not 'str'
>>> output.index(ord('='))
12
So you should replace output.index('=') with output.index(ord('=')) and output.rindex("'") with output.rindex(ord("'")).
EDIT
I realized this much later, but you can circumvent using ord() by prepending your search string with a b.
output.index(b'=')

Python: Fastest way to take and save screenshots

I've been struggling to come up with a script that allows me to take screenshots of my desktop more than once per every second. I'm using Win10.
PIL:
from PIL import ImageGrab
import time
while True:
im = ImageGrab.grab()
fname = "dropfolder/%s.png" %int(time.time())
im.save(fname,'PNG')
Results 1.01 seconds per image.
PyScreeze (https://github.com/asweigart/pyscreeze):
import pyscreeze
import time
while True:
fname = "dropfolder/%s.png" %int(time.time())
x = pyscreeze.screenshot(fname)
Results 1.00 seconds per image.
Win32:
import win32gui
import win32ui
import win32con
import time
w=1920 #res
h=1080 #res
while True:
wDC = win32gui.GetWindowDC(0)
dcObj=win32ui.CreateDCFromHandle(wDC)
cDC=dcObj.CreateCompatibleDC()
dataBitMap = win32ui.CreateBitmap()
dataBitMap.CreateCompatibleBitmap(dcObj, w, h)
cDC.SelectObject(dataBitMap)
cDC.BitBlt((0,0),(w, h) , dcObj, (0,0), win32con.SRCCOPY)
fname = "dropfolder/%s.png" %int(time.time())
dataBitMap.SaveBitmapFile(cDC, fname)
dcObj.DeleteDC()
cDC.DeleteDC()
win32gui.ReleaseDC(0, wDC)
win32gui.DeleteObject(dataBitMap.GetHandle())
Results 1.01 seconds per image.
Then I stumbled into thread (Fastest way to take a screenshot with python on windows) where it was suggested that gtk would yield phenomenal results.
However using gtk:
import gtk
import time
img_width = gtk.gdk.screen_width()
img_height = gtk.gdk.screen_height()
while True:
screengrab = gtk.gdk.Pixbuf(
gtk.gdk.COLORSPACE_RGB,
False,
8,
img_width,
img_height
)
fname = "dropfolder/%s.png" %int(time.time())
screengrab.get_from_drawable(
gtk.gdk.get_default_root_window(),
gtk.gdk.colormap_get_system(),
0, 0, 0, 0,
img_width,
img_height
).save(fname, 'png')
Results 2.34 seconds per image.
It seems to me like I'm doing something wrong, because people have been getting great results with gtk.
Any advices how to speed up the process?
Thanks!

Your first solution should be giving you more than one picture per second. The problem though is that you will be overwriting any pictures that occur within the same second, i.e. they will all have the same filename. To get around this you could create filenames that include 10ths of a second as follows:
from PIL import ImageGrab
from datetime import datetime
while True:
im = ImageGrab.grab()
dt = datetime.now()
fname = "pic_{}.{}.png".format(dt.strftime("%H%M_%S"), dt.microsecond // 100000)
im.save(fname, 'png')
On my machine, this gave the following output:
pic_1143_24.5.png
pic_1143_24.9.png
pic_1143_25.3.png
pic_1143_25.7.png
pic_1143_26.0.png
pic_1143_26.4.png
pic_1143_26.8.png
pic_1143_27.2.png

In case anyone cares in 2022: You can try my newly created project DXcam: I think for raw speed it's the fastest out there (in python, and without going too deep into the rabbit hole). It's originally created for a deep learning pipeline for FPS games where the higher FPS you get the better. Plus I (am trying to) design it to be user-friendly:
For a screenshot just do
import dxcam
camera = dxcam.create()
frame = camera.grab() # full screen
frame = camera.grab(region=(left, top, right, bottom)) # region
For screen capturing:
camera.start(target_fps=60) # threaded
for i in range(1000):
image = camera.get_latest_frame() # Will block until new frame available
camera.stop()
I copied the part of the benchmarks section from the readme:
DXcam
python-mss
D3DShot
Average FPS
238.79
75.87
118.36
Std Dev
1.25
0.5447
0.3224
The benchmarks is conducted through 5 trials on my 240hz monitor with a constant 240hz rendering rate synced w/the monitor (using blurbuster ufo test).
You can read more about the details here: https://github.com/ra1nty/DXcam

This solution uses d3dshot.
def d3dgrab(rect=(0, 0, 0, 0), spath=r".\\pictures\\cache\\", sname="", title=""):
""" take a screenshot by rect. """
sname = sname if sname else time.strftime("%Y%m%d%H%M%S000.jpg", time.localtime())
while os.path.isfile("%s%s" % (spath, sname)):
sname = "%s%03d%s" % (sname[:-7], int(sname[-7:-4]) + 1, sname[-4:])
xlen = win32api.GetSystemMetrics(win32con.SM_CXSCREEN)
ylen = win32api.GetSystemMetrics(win32con.SM_CYSCREEN)
assert 0 <= rect[0] <= xlen and 0 <= rect[2] <= xlen, ValueError("Illegal value of X coordination in rect: %s" % rect)
assert 0 <= rect[1] <= ylen and 0 <= rect[3] <= ylen, ValueError("Illegal value of Y coordinatoin in rect: %s" % rect)
if title:
hdl = win32gui.FindWindow(None, title)
if hdl != win32gui.GetForegroundWindow():
win32gui.SetForegroundWindow(hdl)
rect = win32gui.GetWindowRect(hdl)
elif not sum(rect):
rect = (0, 0, xlen, ylen)
d = d3dshot.create(capture_output="numpy")
return d.screenshot_to_disk(directory=spath, file_name=sname, region=rect)
I think it can be helped
sname = sname if sname else time.strftime("%Y%m%d%H%M%S000.jpg", time.localtime())
while os.path.isfile("%s%s" % (spath, sname)):
sname = "%s%03d%s" % (sname[:-7], int(sname[-7:-4]) + 1, sname[-4:])
And it's fastest way to take screenshot I found.

NoneType error .convert appear

i would like to do some program by capture image from webcam, then cropped it. after crop, i do some image processing and from the process it will run my robots. Here the full program:
import cv2
from cv2 import *
import numpy as np
import pylab
import pymorph
import mahotas
from matplotlib import pyplot
from PIL import Image
# initialize the camera
cam = VideoCapture(0) # 0 -> index of camera
s, img = cam.read()
# frame captured without any errors
if s:
imwrite("img.jpg",img) #save image
#Crop Image
imageFile = "img.jpg"
im1 = Image.open(imageFile)
def imgCrop(im):
box = (0, 199, 640, 200)
region = im.crop(box)
region.save('crop.jpg')
cImg = imgCrop(im1)
#thresholding
def greyImg(im):
gray = im.convert('L')
bw = gray.point(lambda x: 0 if x<128 else 255, '1')
bw.save("bw.jpg")
tImg = greyImg(cImg )
#direction
def find_centroid(im, rez):
width, height = im.size
XX, YY, count = 0, 0, 0
for x in xrange(0, width, rez):
for y in xrange(0, height, rez):
if im.getpixel((x, y)) == 255:
XX += x
YY += y
count += 1
return XX/count, YY/count
print find_centroid(tImg, 1)
def robo_direct():
cen = find_centroid(im, 1)
diff = cen[0] - 320
if diff > 10:
print 'right'
if diff < -10:
print 'left'
else:
print 'straight'
print robo_direct()
The error was come out like this:
File "compile.py", line 32, in greyImg
gray = im.convert('L')
AttributeError: 'NoneType' object has no attribute 'convert'

That is because im is a None object.
Try again the code with:
print im is None
And you'll see. I don't know about threshold, but obviously you are creating the im object the wrong way.

Your function imgCrop(im1) has no return statement and as such returns None. And then your greyImg(im) function also has no return statement and also will return None.
To fix that add return statements to both functions that for the first return region and the second return bw.
Also your robo_direct() function should return and not print the direction so that the call to it in the statement print robo_direct() would print the direction.