This content originally appeared on DEV Community and was authored by Matt Permentier
In the ever-evolving world of fitness apps, FitSnap stands out as a groundbreaking solution. FitSnap goes beyond the usual features of calorie counters and workout trackers by integrating advanced technologies such as OpenCV, Pytesseract, and Spacy. These technologies enable users to manually enter workouts as well as take a photo of a workout plan and have it automatically digitized. In this blog post, we delve into the technical aspects of FitSnap and explore how these technologies work together to provide a seamless and efficient user experience.
The Technology Behind FitSnap
OpenCV for Image Segmentation
OpenCV (Open Source Computer Vision Library) is an open-source library focused on real-time computer vision and image processing. In FitSnap, OpenCV is used to detect and segment workout plans in photos. The process starts with preprocessing the image to reduce noise and highlight key features. This includes steps like grayscale conversion, thresholding, and contour detection.
import cv2
# Load the image in grayscale
image = cv2.imread('workout_schedule.jpg', cv2.IMREAD_GRAYSCALE)
# Apply thresholding to isolate the text
_, thresholded = cv2.threshold(image, 150, 255, cv2.THRESH_BINARY_INV)
# Find contours in the image
contours, _ = cv2.findContours(thresholded, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# Draw rectangles around the detected contours
for contour in contours:
x, y, w, h = cv2.boundingRect(contour)
cv2.rectangle(image, (x, y), (x + w, y + h), (255, 0, 0), 2)
cv2.imshow('Detected Schedule', image)
cv2.waitKey(0)
cv2.destroyAllWindows()
These steps help isolate the relevant parts of the image that contain text, which is the first step in the text extraction process.
Pytesseract for Optical Character Recognition (OCR)
After isolating the text in the image with OpenCV, FitSnap uses Pytesseract to read the text and convert it into editable digital text. Pytesseract is a wrapper for the Tesseract-OCR Engine, known for its accuracy and robustness.
import pytesseract
from PIL import Image
# Use Pytesseract to extract text from the image
text = pytesseract.image_to_string(Image.fromarray(thresholded), lang='eng')
print("Scanned Text:")
print(text)
Pytesseract utilizes various OCR algorithms such as adaptive thresholding, character recognition, and spell correction to ensure accurate text extraction.
Spacy for Natural Language Processing (NLP)
Once the text is converted into digital form, Spacy is used to further analyze and interpret this text. Spacy is a powerful NLP library that helps extract key data such as exercise names, weights, and repetitions.
import spacy
# Load the Spacy English model
nlp = spacy.load('en_core_web_sm')
# Process the extracted text with Spacy
doc = nlp(text)
# Extract relevant information
exercises = []
for ent in doc.ents:
if ent.label_ == 'WORKOUT':
exercises.append(ent.text)
print("Identified Exercises:")
print(exercises)
Spacy uses advanced linguistic models and techniques such as tokenization, part-of-speech tagging, named entity recognition (NER), and dependency parsing to understand the context and meaning of the text.
Integrating the Technologies
Bringing together OpenCV, Pytesseract, and Spacy within FitSnap requires careful integration of different pipelines. The process can be summarized as follows:
- Image Preprocessing: Use OpenCV to preprocess the image and isolate the relevant text segments.
- Text Extraction: Apply Pytesseract to convert the isolated text into editable digital text.
- Text Analysis: Use Spacy to analyze the extracted text and identify and structure the key data.
- Data Entry: Implement an interface to automatically input the analyzed data into the app, eliminating the need for manual entry.
Conclusion
FitSnap is more than just a fitness app. It is a technologically advanced tool that leverages the latest developments in computer vision and natural language processing to enhance the user experience. By combining OpenCV, Pytesseract, and Spacy, FitSnap offers an innovative solution for anyone looking to achieve their fitness goals without wasting time on manual data entry.
With this technological foundation, FitSnap is poised to revolutionize the fitness industry and provide users with a more efficient and accurate way to track their workouts. Stay fit, stay smart – with FitSnap.
Want to dive deeper into how I set up this model? Check out my GitHub page for a detailed explanation and the complete code.
Here, you'll find all the technical details, instructions for training the model, and implementation examples.
This content originally appeared on DEV Community and was authored by Matt Permentier
Matt Permentier | Sciencx (2024-06-24T18:19:56+00:00) FitSnap: image-to-workout. Retrieved from https://www.scien.cx/2024/06/24/fitsnap-image-to-workout/
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