<|
[0]: #!/usr/bin/env python
[1]: # -*- coding: utf-8 -*-
[2]: """Implements functions related to image processing."""
[3]: import numpy as np
[4]: import cv2
[5]: def _convert_to_gray(image):
[6]: """Convert image array into grayscale."""
[7]: if len(image.shape) == len((1280 * 720 * 3).shape):
[8]: return cv2.cvtColor(image.astype('uint8'), cv2.COLOR_BGR2GRAY)
[9]: elif len(image.shape) == len((1280 *720).shape):
[10]: return image.astype('uint8')
[11]: else:
[12]: raise ValueError('Image format not recognized.')
[13]: def _blur_image(gray_image):
[14]: """Apply gaussian blur."""
[15]: return cv2.GaussianBlur(gray_image,(5,5),0)
[16]: def _compute_canny(gray_image):
[17]: """Compute edges using Canny detector."""
[18]: # Compute Canny
[19]: low_threshold = np.max(gray_image)/10
[20]: high_threshold = low_threshold*10
[21]: return cv2.Canny(gray_image,
[22]: low_threshold,
[23]: high_threshold)
***** Tag Data *****
ID: 1
description: Computes edges using Canny detector with dynamically computed thresholds.
start line: 16
end line: 23
dependencies:
- type: Function
name: _convert_to_gray
start line: 5
end line: 12
context description: This snippet computes edges using Canny edge detection algorithm.
It dynamically calculates low and high thresholds based on the maximum pixel value
of the grayscale image.
algorithmic depth: 4
algorithmic depth external: N
obscurity: 3
advanced coding concepts: 4
interesting for students: '5'
self contained: Y
*************
## Suggestions for complexity
1. **Adaptive Threshold Calculation:** Modify `_compute_canny` so that it uses an adaptive method (like Otsu's method) instead of fixed multipliers (`low_threshold` = `max_pixel_value /10`, `high_threshold` = `low_threshold *10`) for determining thresholds dynamically based on image content.
2. **Multi-scale Edge Detection:** Implement multi-scale edge detection where `_compute_canny` applies Canny edge detection at multiple scales (e.g., different Gaussian blurring levels) and combines results intelligently.
3. **Region-specific Edge Detection:** Enhance `_compute_canny` so it performs region-specific threshold calculations based on local properties (e.g., local variance or entropy).
4. **GPU Acceleration:** Integrate GPU acceleration using libraries such as CUDA or OpenCL specifically tailored for faster computation of Canny edges.
5. **Edge Tracking by Hysteresis:** Improve `_compute_canny` by implementing edge tracking by hysteresis manually instead of relying solely on OpenCV's built-in function.
## Conversation
<|user|>_I want change how thresolds work.. more smart.. not just max pixel value.. ideas?_
<|assistant|>_Sure! One advanced way would be using Otsu's method which determines optimal threshold values automatically based on image histogram characteristics._
Here is how you can modify your code:
python
def _compute_canny(gray_image):
"""Compute edges using Canny detector."""
# Compute thresholds using Otsu's method
_, otsu_thresh = cv2.threshold(gray_image,
gray_image.mean(),
gray_image.max(),
cv2.THRESH_BINARY + cv2.THRESH_OTSU)
low_threshold = otsu_thresh / 2
high_threshold = otsu_thresh
return cv2.Canny(gray_image,
low_threshold,
high_threshold)
This uses Otsu’s thresholding method which finds an optimal global threshold value separating foreground from background based on histogram analysis.
Would you like more details or another suggestion?
<|user|>_Nice! But I need multi-scale edge detection too... Can we combine?
<|assistant|>_Absolutely! You can perform edge detection at multiple scales (e.g., different Gaussian blur levels) then combine these results intelligently._
Here is how you can do it:
python
def _compute_multi_scale_canny(gray_image):
"""Compute multi-scale edges using Canny detector."""
scales = [1, sigma=1.5, sigma=3]
all_edges = []
for scale in scales:
blurred = cv2.GaussianBlur(gray_image.astype('float32'), (0,0), scale)
# Compute thresholds using Otsu's method
_, otsu_thresh = cv2.threshold(blurred.astype('uint8'),
blurred.mean(),
blurred.max(),
cv2.THRESH_BINARY + cv2.THRESH_OTSU)
low_threshold = otsu_thresh / 2
high_threshold = otsu_thresh
edges_at_scale = cv2.Canny(blurred.astype('uint8'),
low_threshold,
high_threshold)
all_edges.append(edges_at_scale)
combined_edges = np.bitwise_or.reduce(all_edges)
return combined_edges
# Usage:
# combined_edges_result = _compute_multi_scale_canny(your_gray_image_here)
This code applies Gaussian blur at different scales before computing Canny edges at each scale level separately then combines these results via bitwise OR operation.
Let me know if you need further modifications or explanations!
<>: Hi there! I'm working with some code that involves parsing data files related to experiments conducted by Draper et al., specifically involving "compliant walls." Here's the snippet I'm dealing with:
python
class Draper07Parser(DataParser):
"""
Parser for datafile produced by draper et al . (2007), doi:/10.1016/j.measurement.2006.11.006 .
Only For data generated when waterbag was used has two columns in table names:
In this case target file must be copied SITUS output format.
"""
def __init__(self):
super(Draper07Parser,self).__init__()
self.dataTablePos=_RatNum(1,'Data')
self._experimentName=None
def parse(self,fileObj,numObjs:int=None,symbolTable:SymbTable=SymbTable()):
"""
:param fileObj file object to parsen
:param numObjs Max number of objects or None if should parse alln"
:param symbolTable passed symbol table , if None build new onen"
:return sitedata.siteData objectn"
"""
expNameMatch=re.match( r'^s*Experiments*#d*s*:s*(.*)',fileObj.readline())
if expNameMatch is None:
raise ValueError("No experiment name found")
self._experimentName=expNameMatch.group(1)
fileObj.seek(0)
draper07Reader=Draper07Reader(fileObj,self.dataTablePos,self._experimentName)
if numObjs is None:
numObjs=draper07Reader.numObjs(self.dataTablePos)+1
iterCount=0
while iterCount>: python
Sure! Let's break down what your parser does:
1. **Initialization**: The `Draper07Parser` initializes with some default parameters like `dataTablePos` set to `_RatNum(1,'Data')`.
2. **Parsing Process**:
- It reads the first line from `fileObj` looking for an experiment name using regex.
- If no experiment name is found, it raises a `ValueError`.
- It resets the file pointer (`fileObj.seek(0)`) before starting actual parsing.
- A `Draper07Reader` object is created which reads through the file object according to specified positions (`dataTablePos`) and experiment name.
3. **Iterating Over Observations**:
- The loop continues until `iterCount` reaches `numObjs`.
- For each observation (`obs`) read by `draper07Reader.next()`:
- If it's a `SeriesData`, it sets its symbol table and position.
- If it's an instance of `_RatNum`, it extracts its numeric value.
- If it's a tuple or list containing `_RatNum`, it converts those elements into numbers.
- If none of these types match, it raises a `TypeError`.
**Handling Unexpected Observation Types**:
- To handle unexpected types gracefully, consider logging them instead of raising an error immediately.
- You might want to add additional checks or conversions based on known patterns or structures specific to your data files.
This setup ensures that observations are processed according to expected formats while providing flexibility through symbol tables and positions adjustments.
If you have more specific cases or examples where things don't work as expected, feel free to share! 😊
<>: Got it! So, instead of raising errors immediately when encountering unexpected observation types, I'd like to implement logging so I can analyze these cases later without interrupting the parsing process. How can I integrate logging into this parser? Should I use Python's built-in logging module?
<>: Absolutely! Using Python's built-in logging module is a great choice for this task. Here's how you can integrate logging into your parser:
1. **Import Logging Module**:
Add `import logging` at the beginning of your script.
2. **Configure Logging**:
Set up basic configuration for logging at the start of your script or within your class initialization:
python
import logging
logging.basicConfig(level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s',
filename='parser.log',
filemode='w') # 'w' mode overwrites log file each run; use 'a' for appending
logger = logging.getLogger(__name__)
3. **Replace Error Raising with Logging**:
Instead of raising errors immediately when encountering unexpected types, log them:
python
elif isinstance(obs,tuple):
try:
obs=tuple([x.num if isinstance(x,_RatNum)==True else x for x in list(obs)])
except Exception as e:
logger.error(f"Error processing tuple observation {obs}: {e}")
continue # Skip this observation but continue parsing
elif isinstance(obs,list):
try:
obs=[x.num if isinstance(x,_RatNum)==True else x for x in list(obs)]
except Exception as e:
logger.error(f"Error processing list observation {obs}: {e}")
continue # Skip this observation but continue parsing
else:
logger.warning(f"Unknown observation type {type(obs)} encountered.")
continue # Log warning but skip processing this observation
By doing this, you'll have detailed logs stored in `parser.log`, allowing you to review any issues without halting execution mid-way through parsing.
<>: Great! Now that I've got logging set up, I want to extend this further by adding different log levels based on severity—like INFO for successful parsing steps and ERROR for critical issues that might need immediate attention later on. How can I adjust my current setup to incorporate these log levels effectively?
<>: python
To incorporate different log levels effectively, you can adjust your current setup as follows:
1. **Use Different Log Levels**:
Utilize various log levels such as DEBUG, INFO, WARNING, ERROR, and CRITICAL depending on the situation.
Here’s how you can modify your code:
python
import logging
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s - %(levelname)s - %(message)s',
filename='parser.log',
filemode='w')
logger = logging.getLogger(__name__)
class Draper07Parser(DataParser):
def __init__(self):
super(Draper07Parser,self).__init__()
self.dataTablePos=_RatNum(1,'Data')
self._experimentName=None
def parse(self,fileObj,numObjs:int=None,symbolTable:SymbTable=SymbTable()):
expNameMatch=re.match(r'^s*Experiments*#d*s*:s*(.*)',fileObj.readline())
if expNameMatch is None:
logger.error("No experiment name found")
raise ValueError("No experiment name found")
self._experimentName=expNameMatch.group(1)
logger.info(f"Parsing started for experiment '{self._experimentName}'")
fileObj.seek(0)
draper07Reader=Draper07Reader(fileObj,self.dataTablePos,self._experimentName)
if numObjs is None:
numObjs=draper07Reader.numObjs(self.dataTablePos)+1
iterCount=0
while iterCount>: Awesome! Now I'm thinking about making the log level adjustable at runtime so I don't have to restart my application every time I want more detailed logs during debugging sessions or less verbose logs otherwise. How can I implement dynamic log level adjustment?
<> python
To make log levels adjustable at runtime without restarting your application, you can add functionality that allows changing the log level dynamically based on user input or configuration changes.
Here’s how you can implement dynamic log level adjustment:
python
import logging
# Initial configuration setup
logging.basicConfig(level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s',
filename='parser.log',
filemode='w')
logger = logging.getLogger(__name__)
def set_log_level(level_name):
"""Set log level dynamically."""
level_dict = {
'DEBUG': logging.DEBUG,
'INFO': logging.INFO,
'WARNING': logging.WARNING,
'ERROR': logging.ERROR,
'CRITICAL': logging.CRITICAL,
}
level = level_dict.get(level_name.upper())
if level is not None:
logger.setLevel(level)
print(f"Log level set to {level_name.upper()}")
else:
print(f"Invalid log level '{level_name}'. Please choose from DEBUG/INFO/WARNING/ERROR/CRITICAL.")
# Example usage within your application logic
class Draper07Parser(DataParser):
def __init__(self):
super(Draper07Parser,self).__init__()
self.dataTablePos=_RatNum(1,'Data')
self._experimentName=None
def parse(self,fileObj,numObjs:int=None,symbolTable:SymbTable=SymbTable()):
expNameMatch=re.match(r'^s*Experiments*#d*s*:s*(.*)',fileObj.readline())
if expNameMatch is None:
logger.error("No experiment name found")
raise ValueError("No experiment name found")
self._experimentName=expNameMatch.group(1)
logger.info(f"Parsing started for experiment '{self._experimentName}'")
fileObj.seek(0)
# ... rest of your parsing logic ...
# Adjust log level during runtime example usage
set_log_level('DEBUG') # Change log level as needed during runtime
# Continue with parsing operations...
parser_instance = Draper07Parser()
# parser_instance.parse(...)
set_log_level('INFO') # Change back when needed
logger.info("Parsing completed successfully.")
By implementing this function (`set_log_level`), you allow users or developers running your application interactively (e.g., via command line arguments or GUI inputs) to adjust verbosity without restarting the application.
<>: Perfect! Now let's make sure we can change these log levels via command-line arguments so users don't have manual access issues inside scripts running long processes without terminal access mid-run . How would we integrate argparse here?
<> python
To enable changing log levels via command-line arguments using `argparse`, you can integrate it into your script setup like this:
python
import argparse
import logging
def configure_logging(log_level_name):
"""Configure logging based on command-line argument."""
level_dict = {
'DEBUG': logging.DEBUG,
'INFO': logging.INFO,
'WARNING': logging.WARNING,
'ERROR': logging.ERROR,
'CRITICAL': logging.CRITICAL,
}
level = level_dict.get(log_level_name.upper(), logging.INFO)
logging.basicConfig(level=level,
format='%(asctime)s - %(levelname)s - %(message)s',
filename='parser.log',
filemode='w')
def main():
parser = argparse.ArgumentParser(description="Parse data files with adjustable log levels.")
parser.add_argument('--log-level', default='INFO', choices=['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'],
help='Set the desired log level.')
args = parser.parse_args()
configure_logging(args.log_level)
# Example usage within your application logic
class Draper07Parser(DataParser):
def __init__(self):
super(Draper07Parser,self).__init__()
self.dataTablePos=_RatNum(1,'Data')
self._experimentName=None
def parse(self,fileObj,numObjs:int=None,symbolTable:SymbTable=SymbTable()):
expNameMatch=re.match(r'^s*Experiments*#d*s*:s*(.*)',fileObj.readline())
if expNameMatch is None:
logger.error("No experiment name found")
raise ValueError("No experiment name found")
self._experimentName=expNameMatch.group(1)
logger.info(f"Parsing started for experiment '{self._experimentName}'")
fileObj.seek(0)
# ... rest of your parsing logic ...
if __name__ == "__main__":
main()
logger.info("Parsing completed successfully.")
By integrating `argparse`, users can specify their desired log level directly from the command line when starting your script:
bash
python script.py --log-level DEBUG
This approach allows users flexibility without needing direct access during runtime adjustments.
<>: Great integration! This should cover everything I need right now. Thanks for guiding me through setting up dynamic log levels with argparse!
<>: You're welcome! I'm glad we could get everything set up smoothly 🎉 If you have any more questions or need further assistance down the road, feel free to reach out anytime Happy coding! 🚀<>: Hi there,
I'm working with some AST manipulation code intended specifically targeting assignments involving NumPy arrays along slices defined by ellipsis (`...`). The idea was inspired by NumPy slicing mechanics but adapted here since AST doesn't natively support ellipsis slicing yet (as far as I know).
Here's what my code looks like right now:
python
def visit_Assign(self,assign_node):#,namespaces_node,node_list):#,module_prefixes_list,node_list):
#[node,i]:
#print "ASSIGN"
#for n,node_list[i][j]in enumerate(node_list[i]):
#print "NAME",n,"of",len(node_list[i]),node.__class__.__name__
#if n==len(node_list[i])-1:
# print "LAST"
#for n,node_list[i][j]in enumerate(assign_node.targets):
#print "TARGET",n,"of",len(assign_node.targets),node.__class__.__name__
#if n==len(assign_node.targets)-1:
# print "LAST"
#print assign_node.lineno
#if hasattr(assign_node,"lineno"):
print "TARGETS BODIES"
for n,target_body_pair_index,target_body_pair in enumerate(zip(assign_node.targets,assign_node.value.elts)):
target=target_body_pair_index
body=target_body_pair
if body.__class__.__name__=="Subscript":
subscript_call=getattr(body,"value")
if subscript_call=="__get__":
continue
elif subscript_call=="__iter__":
for_iter=getattr(body,"slice")
try:
iter_item=getattr(for_iter,"value")
for_iter_call=getattr(iter_item,"func")
if iter_item=="native_range":
if getattr(for_iter,"step")==None:
end=int(getattr(for_iter,"stop"))
start=int(getattr(for_iter,"start"))
step=int(getattr(for_iter,"step"))
node_for_range=node_for_loop(start,end)
for_iter=end
else:
end=int(getattr(for_iter,"stop"))
start=int(getattr(for_iter,"start"))
step=int(getattr(for_iter,"step"))
node_for_range=node_for_loop(step,start,end)
for_iter=node_for_range
except AttributeError:
pass
if hasattr(for_iter,"ceiling"):#for numpy.array only!!
array=(for_iter)
numba.forloop_optimization(array)
target_splitted=self.walk(target)
splitted_code="n".join(target_splitted)#["target"]
range_val=getattr(for_iter,"range")
if range_val!=None:
code=str(range_val)
else:
code=str(for_iter)
code+='n'
code+=splitted_code
code+="break"
return code
else:
continue
else:#not subscript call!
continue
return "n".join(self.walk(n)for n in node_list[i])
However, when trying out certain scenarios involving nested loops over NumPy arrays sliced by ellipsis (`...`), things aren't going quite right—I suspect there might be some logical flaw somewhere deep inside my loop handling logic?
Here's what happens when I run my test case—a traceback error pops up!
plaintextTraceback (most recent call last):
File "ast_manipulation.py", line XX,in visit_Assign
for n,target_body_pair_index,target_body_pairin enumerate(zip(assign_node.targets,assign_node.value.elts)):
AttributeError:'Value' object has no attribute'elts'
During handlingofthe above exception,youmays
