Developed Image Processing and Analysis Pipeline

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Developed Image Processing and Analysis Pipeline

  • Day: 2026-02-14
  • Time: 01:30 to 01:40
  • Project: Dev
  • Workspace: WP 2: Operational
  • Status: Completed
  • Priority: MEDIUM
  • Assignee: Matías Nehuen Iglesias
  • Tags: Python, Image Processing, Dataframe, Face Detection, Opencv

Description

Session Goal

The session aimed to develop a comprehensive image processing and analysis pipeline using Python, focusing on retrieving image file paths, performing face detection, and analyzing image metrics.

Key Activities

  • Image File Path Retrieval: Implemented code to import necessary libraries and retrieve sorted paths of JPEG images from a specified directory.
  • Image Counting: Retrieved and counted image file paths using libraries like glob, numpy, and pandas.
  • Face Detection: Set up a face detector with OpenCV and defined functions to compute metrics such as blur, brightness, contrast, and face detection statistics.
  • Data Normalization and Scoring: Normalized specific columns in a DataFrame and calculated a composite score based on weighted factors for face analysis.
  • DataFrame Analysis: Sorted and analyzed DataFrame columns, providing unique value counts for ‘faces’ and sorting by the ‘score’ column.

Achievements

  • Successfully retrieved and processed image file paths.
  • Implemented face detection and metric calculation using OpenCV.
  • Normalized data and calculated composite scores for enhanced analysis.

Pending Tasks

  • Further optimization of the image processing pipeline for performance improvements.
  • Integration of additional image metrics for comprehensive analysis.

Evidence

  • source_file=2026-02-14.sessions.jsonl, line_number=0, event_count=0, session_id=ea0169006ea4c6b1af5ebeb796431c1d032d6469e98b116670235337185f799b
  • event_ids: []

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