Added an implementation to add different virtual backgrounds

Virtual_Background/README.md

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+# Awesome Face Operations: Virtual Background
+Virtual background involve removing the background
+from an image or video stream, and replacing it
+with an image/video of our choice.
+
+
+This is a  real-time filter that creates a virtual
+background of our choice on a live feed using using 
+Mediapipe's selfie segmentation model.
+
+How it works:
+
+1. The selfie segmentation model is loaded from Mediapipe.
+
+2. Video capture is started from the webcam.
+
+3. The frame size is obtained, and the background image is loaded.
+
+4. A Gaussian blur is applied to the background image.
+
+5. Each frame from the video stream is processed:
+
+   a. The segmentation mask is extracted using selfie segmentation.
+
+   b. A condition mask is created based on the threshold value.
+
+   c. The background is applied to the frame using the mask.
+
+   d. The frames per second (FPS) are counted and displayed.
+
+6. The final result (`output_image`) with the virtual background is displayed using `cv2.imshow`.
+
+7. The loop ends and the windows are closed when 'q' is pressed.
+
+
+## Sample
+Note: The filter was applied to stock footage for the sample gif
+
+![output](output.gif)
+
+## Getting Started
+
+* Clone this repository.
+```bash
+  git clone https://github.com/akshitagupta15june/Face-X.git
+```
+* Navigate to the required directory.
+```bash
+   cd Awesome-face-operations/Virtual_Background
+```
+* Install the Python dependencies.
+
+```bash
+  pip install -r requirements.txt
+```
+* Run the script.
+```bash
+  python bg.py
+```
+
+Note: Press 'q' to quit the filter
+## Author
+
+[Abir-Thakur](https://github.com/Inferno2211)
+

Virtual_Background/bg.py

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+import cv2
+import mediapipe as mp
+import numpy as np
+import time 
+
+print("All modules imported!")
+#Set variables
+threshold=0.5
+b_amt=15
+fps=0
+
+#Load image segmentation model
+mp_selfie_segmentation = mp.solutions.selfie_segmentation
+selfie_segmentation = mp_selfie_segmentation.SelfieSegmentation(model_selection=0)
+
+print("Starting video capture...")
+#Start video capture from webcam
+cap = cv2.VideoCapture(0)
+
+#Find out frame size
+width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+
+#Load and resize background
+background_img = cv2.imread("bg.jpg")
+background_img =  cv2.resize(background_img,(width,height))
+
+#Apply gaussian blur to background
+bg_image = cv2.GaussianBlur(background_img, (b_amt,b_amt), 0) #[:,:,::-1]
+
+print("Capture started!")
+start_time =  time.time()
+frame_count=0
+while True:
+    #Read frame from video stream
+    ret, frame= cap.read()
+
+    #Create a mask for person and background
+    results = selfie_segmentation.process(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+    segmentation_mask = results.segmentation_mask
+
+    #Process mask using threshold value
+    condition = np.stack((segmentation_mask,) * 3, axis=-1)  > threshold
+
+    #Use the mask to apply background to frame
+    output_image = np.where(condition, frame, bg_image)
+
+    #Count and display FPS
+    frame_count += 1
+    elapsed_time = time.time() - start_time
+    if elapsed_time >= 1.0:  # Update FPS every second
+        fps = frame_count / elapsed_time
+        print(f"FPS: {round(fps, 2)}")
+        start_time = time.time()
+        frame_count = 0
+    cv2.putText(output_image, f"FPS: {round(fps, 2)}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
+
+    #Display final result
+    cv2.imshow("Virtual background",output_image)
+
+    # Exit the loop if 'q' is pressed
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+
+# Release the video capture and close all windows
+cap.release()
+cv2.destroyAllWindows()

Virtual_Background/requirements.txt

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+opencv-python==4.7.0.72
+mediapipe==0.10.1
+numpy==1.23.5