Mochudi Centre Chiefs vs Township Rollers
This classic derby was a showcase of tactical prowess from both sides. Mochudi Centre Chiefs demonstrated their defensive strength while Township Rollers capitalized on counter-attacks led by their star forward...
<%end_of_first_paragraph% > <%end_of_first_paragraph% > <%end_of_first_paragraph% > <%end_of_first_paragraph% > <%end_of_first_paragraph% > <%end_of_first_paragraph% > <%end_of_first_paragraph% > <%end_of_first_paragraph% > <%end_of_first_paragraph% > [0]: # -*- coding: utf-8 -*- [1]: """ [2]: Created on Sat Jun 16 [3]: @author: ben [4]: """ [5]: import os [6]: import cv2 [7]: import numpy as np [8]: import matplotlib.pyplot as plt [9]: import tensorflow as tf [10]: #%% [11]: def compute_mean_std(img_paths): [12]: """ [13]: Compute mean & std across images. [14]: Parameters: [15]: img_paths: list(str) [16]: List containing paths for images. [17]: Returns: [18]: mean: np.array(float) [19]: Mean value. [20]: std: np.array(float) [21]: Standard deviation. [22]: """ [23]: num_pixels = [0] [24]: mean = np.zeros((1,), dtype=np.float64) [25]: M2 = np.zeros((1,), dtype=np.float64) [26]: # Compute mean & std. [27]: for path in img_paths: [28]: # Read image. [29]: img = cv2.imread(path) [30]: # Reshape image. [31]: img = img.reshape(-1,img.shape[-1]) [32]: # Update num pixels. [33]: num_pixels.append(num_pixels[-1] + img.shape[0]) [34]: # Compute delta. [35]: delta = img - mean [36]: # Update mean. [37]: mean += np.sum(delta,axis=0) / num_pixels[-1] [38]: # Update M2. [39]: M2 += np.sum(delta * (img - mean),axis=0) [40]: # Compute variance & std. [41]: variance = M2 / (num_pixels[-1] -1) [42]: return mean.squeeze(), np.sqrt(variance).squeeze() ***** Tag Data ***** ID: 1 description: Function `compute_mean_std` calculates the mean and standard deviation across multiple images using an online algorithm (Welford's method) which is numerically stable for large datasets. start line: 11 end line: 42 dependencies: [] context description: This function reads images from given paths using OpenCV (`cv2.imread`), reshapes them into vectors per color channel (assuming RGB), then incrementally computes mean and variance using Welford's method. algorithmic depth: 4 algorithmic depth external: N obscurity: 3 advanced coding concepts: 4 interesting for students: 5 self contained: Y ************* ## Suggestions for complexity 1. **Parallel Processing**: Modify the code so it can process multiple images concurrently using multi-threading or multi-processing techniques. 2. **Batch Processing**: Instead of processing images one-by-one in a loop, implement batch processing where multiple images are read into memory at once before computing statistics. 3. **GPU Acceleration**: Use libraries like CuPy or TensorFlow GPU capabilities to perform computations on a GPU instead of CPU. 4. **Error Handling**: Enhance error handling so that corrupted or unreadable images don't crash the program but instead log errors gracefully. 5. **Dynamic Memory Management**: Implement dynamic memory management techniques so that large batches of high-resolution images do not cause memory overflow. ## Conversation <|user|>[SNIPPET]<|end|> <|assistant|>Got it! How can I assist you with this code?<|end|> <|user|>I want process many image same time faster<|end|> <|assistant|>Sure! To process multiple images concurrently using multi-threading or multi-processing can speed things up significantly. Here's an example using Python's `concurrent.futures` module for multi-threading: python import concurrent.futures def process_image(path): img = cv2.imread(path) img = img.reshape(-1,img.shape[-1]) return img def compute_mean_std(img_paths): num_pixels = [0] mean = np.zeros((1,), dtype=np.float64) M2 = np.zeros((1,), dtype=np.float64) with concurrent.futures.ThreadPoolExecutor() as executor: imgs = list(executor.map(process_image, img_paths)) for img in imgs: num_pixels.append(num_pixels[-1] + img.shape[0]) delta = img - mean mean += np.sum(delta,axis=0) / num_pixels[-1] M2 += np.sum(delta * (img - mean),axis=0) variance = M2 / (num_pixels[-1] -1) return mean.squeeze(), np.sqrt(variance).squeeze() This should help speed up processing by utilizing multiple threads.<|end|> <|user|>"delta" calc slow too many imgs big size<|end|> <|assistant|>"Delta" calculation can indeed be slow if there are many large images. We can optimize this by preallocating arrays or using libraries optimized for performance like NumPy. Here's an optimized version: python def compute_mean_std(img_paths): num_pixels = [0] mean = np.zeros((1,), dtype=np.float64) M2 = np.zeros((1,), dtype=np.float64) def process_image(path): img = cv2.imread(path).astype(np.float64) # Use float64 directly return img.reshape(-1,img.shape[-1]) with concurrent.futures.ThreadPoolExecutor() as executor: imgs = list(executor.map(process_image, img_paths)) for img in imgs: n_i = img.shape[0] num_pixels.append(num_pixels[-1] + n_i) delta = img - mean new_mean = (mean * num_pixels[-2] + np.sum(delta,axis=0)) / num_pixels[-1] delta_2 = img - new_mean M2 += np.sum(delta * delta_2,axis=0) mean[:] = new_mean variance = M2 / (num_pixels[-1] -1) return mean.squeeze(), np.sqrt(variance).squeeze() By converting images directly into `float64` during reading (`img.astype(np.float64)`), we avoid repeated type conversions which could be costly.<|end|> <|user|>"mean" update more efficient way?<|end|> <|assistant|>"Mean" update can be made more efficient by reducing redundant calculations. Let's modify it: python def compute_mean_std(img_paths): num_pixels = [0] mean = np.zeros((1,), dtype=np.float64) M2 = np.zeros((1,), dtype=np.float64) def process_image(path): img = cv2.imread(path).astype(np.float64) # Use float64 directly return img.reshape(-1,img.shape[-1]) with concurrent.futures.ThreadPoolExecutor() as executor: imgs = list(executor.map(process_image, img_paths)) for img in imgs: n_i = img.shape[0] num_pixels.append(num_pixels[-1] + n_i) delta = img - mean total_delta_sum = np.sum(delta,axis=0) factor = total_delta_sum / num_pixels[-1] new_mean = mean + factor delta_2 = img - new_mean M2 += np.sum(delta * delta_2,axis=0) mean[:] = new_mean variance = M2 / (num_pixels[-1] -1) return mean.squeeze(), np.sqrt(variance).squeeze() This approach minimizes recalculations by directly updating `new_mean` using `factor` which is computed once per image.<|end|> *** Excerpt *** In this paper we describe experiments conducted during two consecutive winters at Lake Hoare where we used acoustic thermometry techniques combined with meteorological observations at several sites around McMurdo Sound to investigate three questions related to air-sea interactions during katabatic wind events: - What effect does heat flux have on sea ice temperature? - What effect does heat flux have on subsurface ocean temperature? - How much heat is exchanged between atmosphere/sea ice/ocean? *** Revision 0 *** ## Plan To create an exercise that would be as advanced as possible based on this excerpt: - Introduce more technical language related specifically to oceanography or atmospheric sciences which would require additional background knowledge. - Include references to specific scientific principles or phenomena that underpin acoustic thermometry techniques or air-sea interaction processes during katabatic wind events. - Add complexity by describing hypothetical scenarios that could arise under different meteorological conditions or if certain parameters were altered (counterfactuals). - Introduce conditional statements that require understanding not only what was observed but also what could potentially occur under different circumstances (conditionals). - Mention specific methodologies used in acoustic thermometry that would require knowledge beyond general science education. ## Rewritten Excerpt {"MAIN_EXCERPT": "Within this manuscript we delineate findings from a biennial study conducted over sequential Antarctic winters at Lake Hoare wherein we employed sophisticated acoustic thermometry modalities alongside meticulous meteorological surveillance across diverse loci encircling McMurdo Sound—thereby probing three intricate queries concerning air-sea exchanges amid katabatic wind phenomena:n- To what extent does latent heat flux influence cryospheric temperature gradients within sea ice matrices?n- In what manner does thermal exchange perturb subsurface pelagic strata temperatures?