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import cv2
import numpy as np
from deskew import determine_skew
from spellchecker import SpellChecker
import streamlit as st
from src.perplexity_api import chat_completion
from src.st_context import with_streamlit_context
class Utilities:
def __init__(self):
# Initialize the spell checker
self.spell = SpellChecker()
def fetch_likely_text(self):
"""Fetches likely text based on latest OCR values."""
# Use the chat_completion function to fetch the latest OCR values from the session state
return chat_completion(f"latest_ocr_values = {st.session_state['latest']}")
@with_streamlit_context
def detect_annotations(self, frame, text_queue, conf_thresh):
"""Detects annotations for a single video frame."""
# If the text queue is empty, return an empty list
if text_queue.empty():
return []
# Get detections from the text queue
detections = text_queue.get()
annotations = []
for (box, text, confidence) in detections:
# Only consider detections with confidence above the threshold
if confidence > conf_thresh / 100.0:
# Correct the spelling of the detected text
corrected_text = self.correct_spelling(text)
# Append the bounding box and corrected text to annotations
annotations.append((box, corrected_text))
return annotations
@with_streamlit_context
def draw_annotations(self, frame, annotations):
"""Draws annotations on the frame."""
for (box, text) in annotations:
try:
# Calculate the size of the text box
font = cv2.FONT_HERSHEY_SIMPLEX
font_scale = 1
thickness = 2
text_size, baseline = cv2.getTextSize(text, font, font_scale, thickness)
text_width, text_height = text_size
# Calculate the position for the rectangle and text
p1 = (int(box[0][0]), int(box[0][1]))
p2 = (p1[0] + text_width, p1[1] - text_height - baseline)
# Draw a filled rectangle with transparency
overlay = frame.copy()
cv2.rectangle(overlay, p1, p2, (0, 255, 0), -1)
alpha = 0.4 # Transparency factor
cv2.addWeighted(overlay, alpha, frame, 1 - alpha, 0, frame)
# Put the text on top of the rectangle
cv2.putText(frame, text, (p1[0], p1[1] - baseline), font, font_scale, (0, 0, 0), thickness)
except Exception as e:
# Log an error message if annotation fails
st.error(f"Failed to annotate frame: {e}")
return frame
def _grayscale(self, image):
"""Converts the image to grayscale."""
if len(image.shape) == 3:
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
else:
gray = image
return gray
def _remove_noise(self, image):
"""Removes noise from the image using Non-Local Means Denoising."""
return cv2.fastNlMeansDenoising(image, None, 10, 7, 21)
def _enhance_contrast(self, image):
"""Enhances the contrast of the image using CLAHE."""
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
enhanced = clahe.apply(image)
return enhanced
def _deskew(self, image):
"""Deskews the image assuming the text is horizontal."""
angle = determine_skew(image)
(h, w) = image.shape[:2]
center = (w // 2, h // 2)
# Get the rotation matrix
M = cv2.getRotationMatrix2D(center, angle, 1.0)
# Perform the actual rotation and return the image
deskewed = cv2.warpAffine(image, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)
return deskewed
def _binarize(self, image):
"""Converts the image to a binary image using Otsu's binarization."""
_, binary = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
return binary
def preprocess_image(self, image):
"""Preprocesses the image by enhancing contrast, removing noise, and deskewing."""
gray = self._grayscale(image)
denoised = self._remove_noise(gray)
enhanced = self._enhance_contrast(denoised)
deskewed = self._deskew(enhanced)
binary = self._binarize(deskewed)
return binary
def correct_spelling(self, text):
"""Corrects the spelling of the given text."""
corrected_text = []
for word in text.split():
corrected_word = self.spell.correction(word)
if corrected_word:
corrected_text.append(corrected_word)
return ' '.join(corrected_text)
def overlay_annotations(self, frame, annotated_frame):
"""Overlay annotations from the annotated frame onto the current frame."""
alpha = 0.4 # Transparency factor
cv2.addWeighted(annotated_frame, alpha, frame, 1 - alpha, 0, frame)
return frame |