description update

app.py

@@ -318,7 +318,7 @@ st.write("### Workflow Overview")
 st.image("FlowChart.png", caption="Workflow Overview", use_column_width=True)
 
 st.write("### Detailed Workflow")
-st.write("1. **Hough Circle Transform:** The Hough Circle Transform is a technique used to detect circles in an image. It works by transforming the image into a parameter space, identifying circles based on their radius and center coordinates. This method is effective for locating circular objects, such as a coin, within the image.")
+st.write("1. **Florence-2 Model:** Florence-2 is an advanced vision foundation model that uses a prompt-based approach to handle a wide range of vision and vision-language tasks.We utilize this model to detect the scale within the image and mark a bounding box which we can use to find the approximate full measurement of scale.")
 st.write("2. **Pixel Per Metric Ratio:** The Pixel Per Metric Ratio is used to convert pixel measurements into real-world units. By comparing the pixel length obtained from image analysis (i.e., Hough Circle) with the known real-world measurement of the reference object (coin), we get the ratio. This ratio then allows us to accurately scale and size estimation of objects within the image.")
 st.write("3. **Background Removal:** Removing the background first ensures that only the relevant subject is highlighted. We start by converting the image to grayscale and applying thresholding to distinguish the subject from the background. Erosion and dilation then clean up the image, improving the detection of specific features like individual fingers.")
 st.write("4. **Contour Detection:** We use Contour Detection to find the largest contour, which allows us to outline or draw a boundary around the subject (i.e., hand). This highlights the object's shape and edges, improving the precision of the subject.")