Save of the add_filter and add_filter_add working properly

ImageProcessing/API_call.py

@@ -1,23 +1,18 @@
 import requests
 
 # Define the API endpoint and parameters
-api_url = "https://api.removal.ai/3.0/remove"  # Replace with your API endpoint
-api_key = "93D96377-ED5E-7CC1-CD9D-05017285C46A"  # Replace with your API key (if required)
+api_url = "https://api.removal.ai/3.0/remove"  
+api_key = "93D96377-ED5E-7CC1-CD9D-05017285C46A" 
 
 # Define the file path to the image
-image_path = "photo.png"  # Replace with the path to your image file
+image_path = "photo.png" 
 
-# Headers
 headers = {
     "Rm-Token": api_key
 }
-
-# Files to upload
 files = {
-    "image_file": open(image_path, "rb")  # Open the image file in binary mode
+    "image_file": open(image_path, "rb")
 }
-
-# Additional form data
 data = {
     "get_file": "1"  # Ensures the processed file is returned
 }

ImageProcessing/Cascade.py

@@ -1,26 +1,50 @@
 import numpy as np
 import cv2 as cv
 from matplotlib import pyplot as plt
-imagepath = "photo.jpg"
+
+imagepath = "output_image.png"
+
 # Load Image
 im = cv.imread(imagepath)
 if im is None:
     print("Error: Image not found at", imagepath)
     exit()
-# Resize Image
-rwidth, rheight = 1080, 720
-rdim = (rwidth, rheight)
-Resized_Image = cv.resize(im, rdim, interpolation=cv.INTER_AREA)
+
+# Get original dimensions
+original_height, original_width = im.shape[:2]
+max_dim = 800
+
+if max(original_width, original_height) > max_dim: # Calculate scaling factor
+    if original_width > original_height:
+        scale = max_dim / original_width
+    else:
+        scale = max_dim / original_height
+else:
+    scale = 1  # No resizing needed if already within the limit
+
+new_width = int(original_width * scale) # Compute new dimensions
+new_height = int(original_height * scale)
+new_dim = (new_width, new_height)
+
+# Resize image with preserved aspect ratio
+Resized_Image = cv.resize(im, new_dim, interpolation=cv.INTER_AREA)
+
+print(f"Resized image dimensions: {new_width}x{new_height}")
+
 # Convert to Grayscale
 Gray_Img = cv.cvtColor(Resized_Image, cv.COLOR_BGR2GRAY)
+
 # Load Haar Cascade for Face Detection
 face_cascade = cv.CascadeClassifier(cv.data.haarcascades + 'haarcascade_frontalface_default.xml')
-# Detect Faces
-faces = face_cascade.detectMultiScale(Gray_Img, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
+faces = face_cascade.detectMultiScale(Gray_Img, scaleFactor=1.4, minNeighbors=5, minSize=(30, 30))
+
 if len(faces) == 0:
     print("No faces detected.")
     exit()
-# Loop through detected faces (if multiple) and process each
+
+
+contour_image = np.zeros_like(Resized_Image, dtype=np.uint8) # Create a blank image for contours
+
 for (x, y, w, h) in faces:
     # Expand the ROI to include more of the head
     expansion_factor = 0.3  # Increase the size by 30%
@@ -28,21 +52,51 @@ for (x, y, w, h) in faces:
     new_y = max(0, int(y - expansion_factor * h))
     new_w = min(Gray_Img.shape[1], int(w + 2 * expansion_factor * w))
     new_h = min(Gray_Img.shape[0], int(h + 2 * expansion_factor * h))
-    # Draw expanded rectangle around the detected face (optional for visualization)
-    cv.rectangle(Resized_Image, (new_x, new_y), (new_x + new_w, new_y + new_h), (255, 0, 0), 2)
-    # Extract ROI (Region of Interest)
-    face_roi = Gray_Img[new_y:new_y + new_h, new_x:new_x + new_w]
-    # Apply Canny Edge Detection on the expanded face ROI
-    edges = cv.Canny(face_roi, 100, 200)
+   
+    face_roi = Gray_Img[new_y:new_y + new_h, new_x:new_x + new_w] # Extract ROI
+    hist = cv.calcHist([face_roi], [0], None, [256], [0, 256])   # Calculate the histogram of the face ROI
+
+    non_black_pixels = face_roi[face_roi > 0]    # Exclude pure black pixels (intensity = 0)
+
+    # Calculate the median intensity of non-black pixels
+    if len(non_black_pixels) > 0:
+        median_intensity = np.median(non_black_pixels)
+    else:
+        print("All pixels are black, skipping...")
+        median_intensity = 0  # Fallback if no valid pixels exist
+
+    # Adjust thresholds based on the median intensity
+    lower = int(max(0, 0.66 * median_intensity))
+    upper = int(min(255, 1.66 * median_intensity))
+
+    # Apply Canny Edge Detection with the updated thresholds
+    edges = cv.Canny(face_roi, lower, upper)
+    print(median_intensity)
+
     # Find Contours in the expanded face ROI
     contours, _ = cv.findContours(edges, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
-    # Draw Contours on the original resized image
+
+    # Draw Contours on the original resized image and on the blank contour image
     for cnt in contours:
         # Offset the contour points to match the original image
         cnt[:, 0, 0] += new_x
         cnt[:, 0, 1] += new_y
-        cv.drawContours(Resized_Image, [cnt], -1, (0, 255, 0), 2)
+        cv.drawContours(Resized_Image, [cnt], -1, (0, 255, 0), 1)  # Draw on the resized image
+        cv.drawContours(contour_image, [cnt], -1, (0, 255, 0), 1)  # Draw on the blank contour image
+
+# Convert black background to transparent
+b, g, r = cv.split(contour_image)# Split the channels
+alpha = np.where((b == 0) & (g == 0) & (r == 0), 0, 255).astype(np.uint8)
+
+# Merge the channels back with alpha
+contour_image_with_alpha = cv.merge([b, g, r, alpha])
+
+# Save the image with transparent background
+cv.imwrite("contours_only.png", contour_image_with_alpha)
+
+print("Contours-only PNG saved as 'contours_only.png'")
+
 # Display Final Image with Face Contour
 cv.imshow("Face Contour", Resized_Image)
 cv.waitKey(0)
-cv.destroyAllWindows() 
+cv.destroyAllWindows()

ImageProcessing/Tensorflow.py

@@ -1,30 +0,0 @@
-import tensorflow as tf
-import numpy as np
-import cv2
-import os
-# Charger le modèle Mask R-CNN
-model = tf.saved_model.load('frozen_inference_graph.pb')
-# Charger l'image
-image = cv2.imread('photo.jpg')
-# Prétraiter l'image
-image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-input_tensor = tf.convert_to_tensor(image_rgb)
-input_tensor = input_tensor[tf.newaxis,...]  # Ajouter une dimension batch
-# Effectuer la détection
-detections = model(input_tensor)
-# Extraire les masques des objets détectés
-masks = detections['detection_masks'][0].numpy()
-boxes = detections['detection_boxes'][0].numpy()
-class_ids = detections['detection_classes'][0].numpy()
-# Sélectionner uniquement les personnes (classe 1 dans COCO)
-for i in range(len(masks)):
-    if class_ids[i] == 1:  # Personne
-        mask = masks[i]
-        mask = mask > 0.5  # Seuil pour la segmentation binaire
-        # Appliquer le masque sur l'image d'origine
-        mask = np.uint8(mask * 255)  # Convertir en format compatible OpenCV
-        result = cv2.bitwise_and(image, image, mask=mask)
-        # Afficher l'image segmentée
-        cv2.imshow("Segmented Image", result)
-        cv2.waitKey(0)
-        cv2.destroyAllWindows()

ImageProcessing/Untitled-1.py

@@ -0,0 +1 @@
+add_filter.py

ImageProcessing/add_filter.py

@@ -0,0 +1,81 @@
+import os
+import cv2
+import mediapipe as mp
+import numpy as np
+
+mp_face_detection = mp.solutions.face_detection
+mp_face_mesh = mp.solutions.face_mesh
+mp_drawing = mp.solutions.drawing_utils
+
+filter_image_path = "ImagePNG\MArio.png"
+filter_image = cv2.imread(filter_image_path, cv2.IMREAD_UNCHANGED)
+
+def add_filter(image, filter_image, landmarks):
+    # Use of eyes as reference points
+    left_eye = landmarks[33]
+    right_eye = landmarks[263]
+    
+    # Distance between both eyes --> filter size
+    eye_dist = np.linalg.norm(np.array(left_eye) - np.array(right_eye))
+    
+    # Filter size
+    filter_width = int(eye_dist * 2)  # Adjust the factor for desired size
+    filter_height = int(filter_width * filter_image.shape[0] / filter_image.shape[1])
+    resized_filter = cv2.resize(filter_image, (filter_width, filter_height))
+
+    # Filter position on the face
+    center_x = int((left_eye[0] + right_eye[0]) / 2)
+    center_y = int((left_eye[1] + right_eye[1]) / 2)
+    x = int(center_x - filter_width / 2)
+    y = int(center_y - filter_height / 2)
+
+    # Extract the alpha channel (transparency) from the filter image
+    alpha_channel = resized_filter[:, :, 3] / 255.0  # Normalize alpha to range [0, 1]
+    filter_rgb = resized_filter[:, :, :3]  # Extract the RGB channels
+
+    # Overlay the filter onto the image, using the alpha channel as a mask
+    for i in range(resized_filter.shape[0]):
+        for j in range(resized_filter.shape[1]):
+            if alpha_channel[i, j] > 0:  # Check if the pixel is not fully transparent
+                # Blend the pixels: (1 - alpha) * original + alpha * filter
+                for c in range(3):
+                    image[y + i, x + j, c] = (1 - alpha_channel[i, j]) * image[y + i, x + j, c] + alpha_channel[i, j] * filter_rgb[i, j, c]
+
+    return image
+
+input_image_path = "ImagePNG\Felipe.jpg"
+input_image = cv2.imread(input_image_path)
+
+# RGB for Mediapipe
+rgb_image = cv2.cvtColor(input_image, cv2.COLOR_BGR2RGB)
+
+# FaceMesh init
+with mp_face_mesh.FaceMesh(min_detection_confidence=0.5, min_tracking_confidence=0.5) as face_mesh:
+    # Face detection + key points
+    results = face_mesh.process(rgb_image)
+    
+    if results.multi_face_landmarks:
+        for face_landmarks in results.multi_face_landmarks:
+            # key point
+            landmarks = [(lm.x * input_image.shape[1], lm.y * input_image.shape[0]) for lm in face_landmarks.landmark]
+
+            # filter to be added (glasses)
+            input_image = add_filter(input_image, filter_image, landmarks)
+
+
+# Define the folder path
+folder_path = "OutputImage"
+
+# Extract the filter name from the filter image path
+filter_name = os.path.splitext(os.path.basename(filter_image_path))[0]
+
+# Define the full path to save the image with the filter name included
+file_path = os.path.join(folder_path, f"{filter_name}_output_image_.jpg")
+
+# Save the image
+cv2.imwrite(file_path, input_image)
+
+# Display result
+cv2.imshow("Image with filter", input_image)
+cv2.waitKey(0)
+cv2.destroyAllWindows()

ImageProcessing/add_filter_hat.py

@@ -0,0 +1,85 @@
+import os
+import cv2
+import mediapipe as mp
+import numpy as np
+
+# Mediapipe initialization
+mp_face_detection = mp.solutions.face_detection
+mp_face_mesh = mp.solutions.face_mesh
+
+# Load filter image (transparent PNG)
+filter_image_path = "ImagePNG/MArio.png"
+filter_image = cv2.imread(filter_image_path, cv2.IMREAD_UNCHANGED)
+
+def add_filter(image, filter_image, bbox, scale_factor=1.2):
+    """
+    Add a filter image to a face image at a specified bounding box position,
+    scaling it dynamically based on the face size.
+    """
+    x_min, y_min, box_width, box_height = bbox
+
+    # Scale the filter based on the face height and a scaling factor
+    filter_width = int(box_width * scale_factor)
+    filter_height = int(filter_width * filter_image.shape[0] / filter_image.shape[1])
+    resized_filter = cv2.resize(filter_image, (filter_width, filter_height))
+
+    # Position filter above the head
+    x = int(x_min - (filter_width - box_width) / 2)
+    y = int(y_min - filter_height * 0.7)  # Slight vertical offset above the face
+
+    # Extract alpha channel (transparency) from the filter
+    alpha_channel = resized_filter[:, :, 3] / 255.0  # Normalize to range [0, 1]
+    filter_rgb = resized_filter[:, :, :3]
+
+    # Overlay the filter on the image using alpha blending
+    for i in range(filter_height):
+        for j in range(filter_width):
+            if 0 <= y + i < image.shape[0] and 0 <= x + j < image.shape[1]:
+                alpha = alpha_channel[i, j]
+                if alpha > 0:  # Apply only non-transparent pixels
+                    image[y + i, x + j] = (1 - alpha) * image[y + i, x + j] + alpha * filter_rgb[i, j]
+
+    return image
+
+# Load input image
+input_image_path = "ImagePNG/output.png"
+input_image = cv2.imread(input_image_path)
+
+# Convert to RGB for Mediapipe
+rgb_image = cv2.cvtColor(input_image, cv2.COLOR_BGR2RGB)
+
+# Use Mediapipe for face detection
+with mp_face_detection.FaceDetection(model_selection=1, min_detection_confidence=0.5) as face_detection:
+    results = face_detection.process(rgb_image)
+
+    if results.detections:
+        for detection in results.detections:
+            bbox = detection.location_data.relative_bounding_box
+            h, w, _ = input_image.shape
+            # Convert relative bounding box to absolute dimensions
+            x_min = int(bbox.xmin * w)
+            y_min = int(bbox.ymin * h)
+            box_width = int(bbox.width * w)
+            box_height = int(bbox.height * h)
+
+            # Adjust the scale factor based on face height
+            # Larger faces get proportionally larger hats
+            face_height_ratio = box_height / h  # Ratio of face height to image height
+            dynamic_scale_factor = 2.75 + face_height_ratio  # Base size + adjustment
+
+            # Add filter to the image with dynamic scaling
+            input_image = add_filter(input_image, filter_image, (x_min, y_min, box_width, box_height), scale_factor=dynamic_scale_factor)
+
+# Define output folder and save path
+output_folder = "OutputImage"
+os.makedirs(output_folder, exist_ok=True)  # Ensure the folder exists
+filter_name = os.path.splitext(os.path.basename(filter_image_path))[0]
+output_path = os.path.join(output_folder, f"{filter_name}_output_image_dynamic.jpg")
+
+# Save the output image
+cv2.imwrite(output_path, input_image)
+
+# Display result
+cv2.imshow("Image with Filter", input_image)
+cv2.waitKey(0)
+cv2.destroyAllWindows()

ImageProcessing/add_filter_multiple.py

@@ -0,0 +1,87 @@
+import cv2
+import mediapipe as mp
+import numpy as np
+
+mp_face_mesh = mp.solutions.face_mesh
+
+# List of filters
+filter_images = {
+    1: "C:\Users\doria\Documents\ECAM\Année 4\IT & Robotics Lab\GrpC_Identikit\ImageProcessing\ImagePNG\Chien1.png",  # Replace with your filter image paths
+    2: "C:\Users\doria\Documents\ECAM\Année 4\IT & Robotics Lab\GrpC_Identikit\ImageProcessing\ImagePNG\MoustacheMario.png",
+    3: "C:\Users\doria\Documents\ECAM\Année 4\IT & Robotics Lab\GrpC_Identikit\ImageProcessing\ImagePNG\MArio.png"
+}
+
+def add_filter(image, filter_image, landmarks):
+    # Use eyes as reference points
+    left_eye = landmarks[33]
+    right_eye = landmarks[263]
+    
+    # Distance between both eyes --> filter size
+    eye_dist = np.linalg.norm(np.array(left_eye) - np.array(right_eye))
+    
+    # Adjust the factor for a smaller filter size
+    scaling_factor = 2.75
+    filter_width = int(eye_dist * scaling_factor)
+    filter_height = int(filter_width * filter_image.shape[0] / filter_image.shape[1])
+    resized_filter = cv2.resize(filter_image, (filter_width, filter_height))
+    
+    # Filter position on the face
+    center_x = int((left_eye[0] + right_eye[0]) / 2)
+    center_y = int((left_eye[1] + right_eye[1]) / 2)
+    x = int(center_x - filter_width / 2)
+    y = int(center_y - filter_height / 2)
+
+    # Extract the alpha channel (transparency) from the filter image
+    alpha_channel = resized_filter[:, :, 3] / 255.0  # Normalize alpha to range [0, 1]
+    filter_rgb = resized_filter[:, :, :3]  # Extract the RGB channels
+
+    # Overlay the filter onto the image, using the alpha channel as a mask
+    for i in range(resized_filter.shape[0]):
+        for j in range(resized_filter.shape[1]):
+            if alpha_channel[i, j] > 0:  # Check if the pixel is not fully transparent
+                # Blend the pixels: (1 - alpha) * original + alpha * filter
+                for c in range(3):
+                    image[y + i, x + j, c] = (1 - alpha_channel[i, j]) * image[y + i, x + j, c] + alpha_channel[i, j] * filter_rgb[i, j, c]
+
+    return image
+
+def apply_filter_by_choice(choice, input_image_path):
+    # Validate the filter choice
+    if choice not in filter_images:
+        print(f"Filter {choice} does not exist. Please choose a valid filter number.")
+        return
+    
+    # Load the input image and filter
+    input_image = cv2.imread(input_image_path)
+    filter_image_path = filter_images[choice]
+    filter_image = cv2.imread(filter_image_path, cv2.IMREAD_UNCHANGED)
+
+    # RGB for Mediapipe
+    rgb_image = cv2.cvtColor(input_image, cv2.COLOR_BGR2RGB)
+
+    # FaceMesh init
+    with mp_face_mesh.FaceMesh(min_detection_confidence=0.5, min_tracking_confidence=0.5) as face_mesh:
+        # Face detection + key points
+        results = face_mesh.process(rgb_image)
+        
+        if results.multi_face_landmarks:
+            for face_landmarks in results.multi_face_landmarks:
+                # Key points
+                landmarks = [(lm.x * input_image.shape[1], lm.y * input_image.shape[0]) for lm in face_landmarks.landmark]
+
+                # Apply the filter
+                input_image = add_filter(input_image, filter_image, landmarks)
+
+    # Display result
+    cv2.imshow("Image with Filter", input_image)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+
+    # Save the image
+    output_path = f"output_image_filter_{choice}.jpg"
+    cv2.imwrite(output_path, input_image)
+    print(f"Saved filtered image to {output_path}")
+
+# Example usage:
+filter_choice = int(input("Enter the filter number (1, 2, or 3): "))
+apply_filter_by_choice(filter_choice, "Dorianvide.png")

ImageProcessing/red_dots.py

@@ -0,0 +1,118 @@
+import cv2
+import mediapipe as mp
+import numpy as np
+
+# Initialize MediaPipe Face Detection and Drawing utilities
+mp_face_detection = mp.solutions.face_detection
+mp_drawing = mp.solutions.drawing_utils
+mp_face_mesh = mp.solutions.face_mesh
+
+# Initialize variables
+face_detection = mp_face_detection.FaceDetection(min_detection_confidence=0.2)
+face_mesh = mp_face_mesh.FaceMesh(min_detection_confidence=0.2, min_tracking_confidence=0.5)
+
+# Initialize camera
+cap = cv2.VideoCapture(0)
+
+# Variables for button states
+greyscale = False
+sunglasses_on = False
+saved_image = None
+
+# Function to overlay sunglasses
+def overlay_sunglasses(image, face_landmarks, sunglasses_img):
+    if len(face_landmarks) > 0:
+        # Coordinates for the eyes based on face mesh landmarks
+        left_eye = face_landmarks[33]
+        right_eye = face_landmarks[263]
+
+        # Calculate the center between the eyes for positioning sunglasses
+        eye_center_x = int((left_eye[0] + right_eye[0]) / 2)
+        eye_center_y = int((left_eye[1] + right_eye[1]) / 2)
+
+        # Calculate the scaling factor for sunglasses based on the distance between the eyes
+        eye_distance = np.linalg.norm(np.array(left_eye) - np.array(right_eye))
+        scale_factor = eye_distance / sunglasses_img.shape[1]
+
+        # Resize sunglasses based on scale factor
+        sunglasses_resized = cv2.resize(sunglasses_img, None, fx=scale_factor, fy=scale_factor)
+
+        # Determine the region of interest (ROI) for sunglasses
+        start_x = int(eye_center_x - sunglasses_resized.shape[1] / 2)
+        start_y = int(eye_center_y - sunglasses_resized.shape[0] / 2)
+
+        # Overlay sunglasses on the face
+        for i in range(sunglasses_resized.shape[0]):
+            for j in range(sunglasses_resized.shape[1]):
+                if sunglasses_resized[i, j][3] > 0:  # If not transparent
+                    image[start_y + i, start_x + j] = sunglasses_resized[i, j][0:3]  # Apply RGB channels
+
+    return image
+
+# Function to apply greyscale filter
+def toggle_greyscale(image, greyscale):
+    if greyscale:
+        return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    else:
+        return image
+
+# Load sunglasses image with transparency (PNG)
+sunglasses_img = cv2.imread("sunglasses.png", cv2.IMREAD_UNCHANGED)
+
+while cap.isOpened():
+    ret, frame = cap.read()
+
+    if not ret:
+        break
+
+    # Flip the frame horizontally for a mirror effect
+    frame = cv2.flip(frame, 1)
+
+    # Convert to RGB for MediaPipe processing
+    rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+    results_detection = face_detection.process(rgb_frame)
+    results_mesh = face_mesh.process(rgb_frame)
+
+    # Draw face detection bounding boxes
+    if results_detection.detections:
+        for detection in results_detection.detections:
+            mp_drawing.draw_detection(frame, detection)
+
+    # Draw face mesh landmarks
+    if results_mesh.multi_face_landmarks:
+        for face_landmarks in results_mesh.multi_face_landmarks:
+            mp_drawing.draw_landmarks(frame, face_landmarks, mp_face_mesh.FACEMESH_CONTOURS)
+
+    # Apply greyscale filter if enabled
+    frame = toggle_greyscale(frame, greyscale)
+
+    # Display the image
+    cv2.imshow('Face Capture Controls', frame)
+
+    key = cv2.waitKey(1) & 0xFF
+
+    # Save Image
+    if key == ord('s'):  # Press 's' to save image
+        saved_image = frame.copy()
+        cv2.imwrite("captured_image.png", saved_image)
+        print("Image Saved!")
+
+    # Retake Image
+    elif key == ord('r'):  # Press 'r' to retake image
+        saved_image = None
+        print("Image Retaken!")
+
+    # Toggle Greyscale
+    elif key == ord('g'):  # Press 'g' to toggle greyscale
+        greyscale = not greyscale
+        print(f"Greyscale: {'Enabled' if greyscale else 'Disabled'}")
+
+
+    # Kill Switch
+    elif key == ord('q'):  # Press 'q' to quit
+        break
+
+# Release camera and close all windows
+cap.release()
+cv2.destroyAllWindows()
+

ImageProcessing/remove_bg.py

@@ -0,0 +1,19 @@
+
+# Importing Required Modules 
+from rembg import remove 
+from PIL import Image 
+  
+# Store path of the image in the variable input_path 
+input_path =  'C:/Users/doria/Documents/ECAM/Année 4/IT & Robotics Lab/GrpC_Identikit/ImageProcessing/ImageJPG/Démon.png' 
+  
+# Store path of the output image in the variable output_path 
+output_path = 'C:/Users/doria/Documents/ECAM/Année 4/IT & Robotics Lab/GrpC_Identikit/ImageProcessing/Code\Demon.png' 
+  
+# Processing the image 
+input = Image.open(input_path) 
+  
+# Removing the background from the given Image 
+output = remove(input) 
+  
+#Saving the image in the given path 
+output.save(output_path)

ImageProcessing/roboticsworkshopimageprocessing.py

@@ -1,65 +0,0 @@
-import numpy as np
-import cv2 as cv
-from matplotlib import pyplot as plt
-
-imagepath = "E:\\ECAM\\2022-23\\Pathway Discovery Workshops\\images-20230626\\ecam.png"
-
-# Load Image
-im = cv.imread(imagepath)
-if im is None:
-    print("Error: Image not found at", imagepath)
-    exit()
-
-# Get Dimensions
-dimensions = im.shape
-height, width, channels = dimensions
-print('Image Dimension :', dimensions)
-print('Image Height :', height)
-print('Image Width :', width)
-print('Number of Channels :', channels)
-
-# Resize Image
-rwidth, rheight = 700, 700
-rdim = (rwidth, rheight)
-Resized_Image = cv.resize(im, rdim, interpolation=cv.INTER_AREA)
-cv.imshow("Resized Image", Resized_Image)
-cv.waitKey(0)
-cv.destroyAllWindows()
-
-# Convert to Grayscale
-Gray_Img = cv.cvtColor(Resized_Image, cv.COLOR_BGR2GRAY)
-cv.imshow("Grayscale Image", Gray_Img)
-cv.waitKey(0)
-cv.destroyAllWindows()
-
-# Threshold Image
-ret, Thresh_Img = cv.threshold(Gray_Img, 100, 255, 0)
-cv.imshow("Threshold Image", Thresh_Img)
-cv.waitKey(0)
-cv.destroyAllWindows()
-
-# Plot Histogram
-hist = cv.calcHist([Gray_Img], [0], None, [256], [0, 256])
-plt.figure()
-plt.title("Grayscale Histogram")
-plt.xlabel("Bins")
-plt.ylabel("# of Pixels")
-plt.plot(hist)
-plt.xlim([0, 256])
-plt.show()
-
-# Find and Draw Contours on Resized Image
-contours, hierarchy = cv.findContours(Thresh_Img, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
-contoured_image = Resized_Image.copy()
-cv.drawContours(contoured_image, contours, -1, (0, 255, 0), 3)
-cv.imshow("Contours on Resized Image", contoured_image)
-cv.waitKey(0)
-cv.destroyAllWindows()
-
-# Draw Contours on Threshold Image
-contoured_thresh = cv.cvtColor(Thresh_Img, cv.COLOR_GRAY2BGR)
-cv.drawContours(contoured_thresh, contours, -1, (0, 255, 0), 3)
-cv.imshow("Contours on Threshold Image", contoured_thresh)
-cv.waitKey(0)
-cv.destroyAllWindows()
-

README.md

@@ -1,3 +1,8 @@
 # GrpC_Identikit
 
-This repository is used in IT & Robotics LAB for the Identikit project that aims to draw a face picture using a robot.
+This repository is used in IT & Robotics LAB for the Identikit project that aims to draw a face picture using a robot.
+This Project has different objectives :
+
+- Having a drawing of the user's face
+- Process the image, with background removal, and contouring
+- Having additional features, such as a user interface, and filters on the user's face

Saving/API_call.py

@@ -0,0 +1,38 @@
+import requests
+
+# Define the API endpoint and parameters
+api_url = "https://api.removal.ai/3.0/remove"  
+api_key = "93D96377-ED5E-7CC1-CD9D-05017285C46A" 
+
+# Define the file path to the image
+image_path = "photo.png" 
+
+headers = {
+    "Rm-Token": api_key
+}
+files = {
+    "image_file": open(image_path, "rb")
+}
+data = {
+    "get_file": "1"  # Ensures the processed file is returned
+}
+
+try:
+    # Make the POST request
+    response = requests.post(api_url, headers=headers, files=files, data=data)
+
+    # Save the output file if the request is successful
+    if response.status_code == 200:
+        with open("transparent_image.png", "wb") as output_file:
+            output_file.write(response.content)
+        print("Transparent image saved as 'transparent_image.png'")
+    else:
+        print(f"Error: Received status code {response.status_code}")
+        print("Response:", response.text)
+
+except requests.exceptions.RequestException as e:
+    print("An error occurred:", e)
+finally:
+    # Close the file
+    files["image_file"].close()
+

Saving/Cascade.py

@@ -0,0 +1,102 @@
+import numpy as np
+import cv2 as cv
+from matplotlib import pyplot as plt
+
+imagepath = "output_image.png"
+
+# Load Image
+im = cv.imread(imagepath)
+if im is None:
+    print("Error: Image not found at", imagepath)
+    exit()
+
+# Get original dimensions
+original_height, original_width = im.shape[:2]
+max_dim = 800
+
+if max(original_width, original_height) > max_dim: # Calculate scaling factor
+    if original_width > original_height:
+        scale = max_dim / original_width
+    else:
+        scale = max_dim / original_height
+else:
+    scale = 1  # No resizing needed if already within the limit
+
+new_width = int(original_width * scale) # Compute new dimensions
+new_height = int(original_height * scale)
+new_dim = (new_width, new_height)
+
+# Resize image with preserved aspect ratio
+Resized_Image = cv.resize(im, new_dim, interpolation=cv.INTER_AREA)
+
+print(f"Resized image dimensions: {new_width}x{new_height}")
+
+# Convert to Grayscale
+Gray_Img = cv.cvtColor(Resized_Image, cv.COLOR_BGR2GRAY)
+
+# Load Haar Cascade for Face Detection
+face_cascade = cv.CascadeClassifier(cv.data.haarcascades + 'haarcascade_frontalface_default.xml')
+faces = face_cascade.detectMultiScale(Gray_Img, scaleFactor=1.4, minNeighbors=5, minSize=(30, 30))
+
+if len(faces) == 0:
+    print("No faces detected.")
+    exit()
+
+
+contour_image = np.zeros_like(Resized_Image, dtype=np.uint8) # Create a blank image for contours
+
+for (x, y, w, h) in faces:
+    # Expand the ROI to include more of the head
+    expansion_factor = 0.3  # Increase the size by 30%
+    new_x = max(0, int(x - expansion_factor * w))  # Ensure ROI doesn't go out of bounds
+    new_y = max(0, int(y - expansion_factor * h))
+    new_w = min(Gray_Img.shape[1], int(w + 2 * expansion_factor * w))
+    new_h = min(Gray_Img.shape[0], int(h + 2 * expansion_factor * h))
+   
+    face_roi = Gray_Img[new_y:new_y + new_h, new_x:new_x + new_w] # Extract ROI
+    hist = cv.calcHist([face_roi], [0], None, [256], [0, 256])   # Calculate the histogram of the face ROI
+
+    non_black_pixels = face_roi[face_roi > 0]    # Exclude pure black pixels (intensity = 0)
+
+    # Calculate the median intensity of non-black pixels
+    if len(non_black_pixels) > 0:
+        median_intensity = np.median(non_black_pixels)
+    else:
+        print("All pixels are black, skipping...")
+        median_intensity = 0  # Fallback if no valid pixels exist
+
+    # Adjust thresholds based on the median intensity
+    lower = int(max(0, 0.66 * median_intensity))
+    upper = int(min(255, 1.66 * median_intensity))
+
+    # Apply Canny Edge Detection with the updated thresholds
+    edges = cv.Canny(face_roi, lower, upper)
+    print(median_intensity)
+
+    # Find Contours in the expanded face ROI
+    contours, _ = cv.findContours(edges, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
+
+    # Draw Contours on the original resized image and on the blank contour image
+    for cnt in contours:
+        # Offset the contour points to match the original image
+        cnt[:, 0, 0] += new_x
+        cnt[:, 0, 1] += new_y
+        cv.drawContours(Resized_Image, [cnt], -1, (0, 255, 0), 1)  # Draw on the resized image
+        cv.drawContours(contour_image, [cnt], -1, (0, 255, 0), 1)  # Draw on the blank contour image
+
+# Convert black background to transparent
+b, g, r = cv.split(contour_image)# Split the channels
+alpha = np.where((b == 0) & (g == 0) & (r == 0), 0, 255).astype(np.uint8)
+
+# Merge the channels back with alpha
+contour_image_with_alpha = cv.merge([b, g, r, alpha])
+
+# Save the image with transparent background
+cv.imwrite("contours_only.png", contour_image_with_alpha)
+
+print("Contours-only PNG saved as 'contours_only.png'")
+
+# Display Final Image with Face Contour
+cv.imshow("Face Contour", Resized_Image)
+cv.waitKey(0)
+cv.destroyAllWindows()

Saving/Untitled-1.py

@@ -0,0 +1 @@
+add_filter.py

Saving/add_filter.py

@@ -0,0 +1,81 @@
+import os
+import cv2
+import mediapipe as mp
+import numpy as np
+
+mp_face_detection = mp.solutions.face_detection
+mp_face_mesh = mp.solutions.face_mesh
+mp_drawing = mp.solutions.drawing_utils
+
+filter_image_path = "ImagePNG\MArio.png"
+filter_image = cv2.imread(filter_image_path, cv2.IMREAD_UNCHANGED)
+
+def add_filter(image, filter_image, landmarks):
+    # Use of eyes as reference points
+    left_eye = landmarks[33]
+    right_eye = landmarks[263]
+    
+    # Distance between both eyes --> filter size
+    eye_dist = np.linalg.norm(np.array(left_eye) - np.array(right_eye))
+    
+    # Filter size
+    filter_width = int(eye_dist * 2)  # Adjust the factor for desired size
+    filter_height = int(filter_width * filter_image.shape[0] / filter_image.shape[1])
+    resized_filter = cv2.resize(filter_image, (filter_width, filter_height))
+
+    # Filter position on the face
+    center_x = int((left_eye[0] + right_eye[0]) / 2)
+    center_y = int((left_eye[1] + right_eye[1]) / 2)
+    x = int(center_x - filter_width / 2)
+    y = int(center_y - filter_height / 2)
+
+    # Extract the alpha channel (transparency) from the filter image
+    alpha_channel = resized_filter[:, :, 3] / 255.0  # Normalize alpha to range [0, 1]
+    filter_rgb = resized_filter[:, :, :3]  # Extract the RGB channels
+
+    # Overlay the filter onto the image, using the alpha channel as a mask
+    for i in range(resized_filter.shape[0]):
+        for j in range(resized_filter.shape[1]):
+            if alpha_channel[i, j] > 0:  # Check if the pixel is not fully transparent
+                # Blend the pixels: (1 - alpha) * original + alpha * filter
+                for c in range(3):
+                    image[y + i, x + j, c] = (1 - alpha_channel[i, j]) * image[y + i, x + j, c] + alpha_channel[i, j] * filter_rgb[i, j, c]
+
+    return image
+
+input_image_path = "ImagePNG\Dorian.png"
+input_image = cv2.imread(input_image_path)
+
+# RGB for Mediapipe
+rgb_image = cv2.cvtColor(input_image, cv2.COLOR_BGR2RGB)
+
+# FaceMesh init
+with mp_face_mesh.FaceMesh(min_detection_confidence=0.5, min_tracking_confidence=0.5) as face_mesh:
+    # Face detection + key points
+    results = face_mesh.process(rgb_image)
+    
+    if results.multi_face_landmarks:
+        for face_landmarks in results.multi_face_landmarks:
+            # key point
+            landmarks = [(lm.x * input_image.shape[1], lm.y * input_image.shape[0]) for lm in face_landmarks.landmark]
+
+            # filter to be added (glasses)
+            input_image = add_filter(input_image, filter_image, landmarks)
+
+
+# Define the folder path
+folder_path = "OutputImage"
+
+# Extract the filter name from the filter image path
+filter_name = os.path.splitext(os.path.basename(filter_image_path))[0]
+
+# Define the full path to save the image with the filter name included
+file_path = os.path.join(folder_path, f"{filter_name}_output_image_.jpg")
+
+# Save the image
+cv2.imwrite(file_path, input_image)
+
+# Display result
+cv2.imshow("Image with filter", input_image)
+cv2.waitKey(0)
+cv2.destroyAllWindows()

Saving/add_filter_hat.py

@@ -0,0 +1,85 @@
+import os
+import cv2
+import mediapipe as mp
+import numpy as np
+
+# Mediapipe initialization
+mp_face_detection = mp.solutions.face_detection
+mp_face_mesh = mp.solutions.face_mesh
+
+# Load filter image (transparent PNG)
+filter_image_path = "ImagePNG/MArio.png"
+filter_image = cv2.imread(filter_image_path, cv2.IMREAD_UNCHANGED)
+
+def add_filter(image, filter_image, bbox, scale_factor=1.2):
+    """
+    Add a filter image to a face image at a specified bounding box position,
+    scaling it dynamically based on the face size.
+    """
+    x_min, y_min, box_width, box_height = bbox
+
+    # Scale the filter based on the face height and a scaling factor
+    filter_width = int(box_width * scale_factor)
+    filter_height = int(filter_width * filter_image.shape[0] / filter_image.shape[1])
+    resized_filter = cv2.resize(filter_image, (filter_width, filter_height))
+
+    # Position filter above the head
+    x = int(x_min - (filter_width - box_width) / 2)
+    y = int(y_min - filter_height * 0.7)  # Slight vertical offset above the face
+
+    # Extract alpha channel (transparency) from the filter
+    alpha_channel = resized_filter[:, :, 3] / 255.0  # Normalize to range [0, 1]
+    filter_rgb = resized_filter[:, :, :3]
+
+    # Overlay the filter on the image using alpha blending
+    for i in range(filter_height):
+        for j in range(filter_width):
+            if 0 <= y + i < image.shape[0] and 0 <= x + j < image.shape[1]:
+                alpha = alpha_channel[i, j]
+                if alpha > 0:  # Apply only non-transparent pixels
+                    image[y + i, x + j] = (1 - alpha) * image[y + i, x + j] + alpha * filter_rgb[i, j]
+
+    return image
+
+# Load input image
+input_image_path = "ImagePNG/output.png"
+input_image = cv2.imread(input_image_path)
+
+# Convert to RGB for Mediapipe
+rgb_image = cv2.cvtColor(input_image, cv2.COLOR_BGR2RGB)
+
+# Use Mediapipe for face detection
+with mp_face_detection.FaceDetection(model_selection=1, min_detection_confidence=0.5) as face_detection:
+    results = face_detection.process(rgb_image)
+
+    if results.detections:
+        for detection in results.detections:
+            bbox = detection.location_data.relative_bounding_box
+            h, w, _ = input_image.shape
+            # Convert relative bounding box to absolute dimensions
+            x_min = int(bbox.xmin * w)
+            y_min = int(bbox.ymin * h)
+            box_width = int(bbox.width * w)
+            box_height = int(bbox.height * h)
+
+            # Adjust the scale factor based on face height
+            # Larger faces get proportionally larger hats
+            face_height_ratio = box_height / h  # Ratio of face height to image height
+            dynamic_scale_factor = 2.75 + face_height_ratio  # Base size + adjustment
+
+            # Add filter to the image with dynamic scaling
+            input_image = add_filter(input_image, filter_image, (x_min, y_min, box_width, box_height), scale_factor=dynamic_scale_factor)
+
+# Define output folder and save path
+output_folder = "OutputImage"
+os.makedirs(output_folder, exist_ok=True)  # Ensure the folder exists
+filter_name = os.path.splitext(os.path.basename(filter_image_path))[0]
+output_path = os.path.join(output_folder, f"{filter_name}_output_image_dynamic.jpg")
+
+# Save the output image
+cv2.imwrite(output_path, input_image)
+
+# Display result
+cv2.imshow("Image with Filter", input_image)
+cv2.waitKey(0)
+cv2.destroyAllWindows()

Saving/add_filter_multiple.py

@@ -0,0 +1,87 @@
+import cv2
+import mediapipe as mp
+import numpy as np
+
+mp_face_mesh = mp.solutions.face_mesh
+
+# List of filters
+filter_images = {
+    1: "C:\Users\doria\Documents\ECAM\Année 4\IT & Robotics Lab\GrpC_Identikit\ImageProcessing\ImagePNG\Chien1.png",  # Replace with your filter image paths
+    2: "C:\Users\doria\Documents\ECAM\Année 4\IT & Robotics Lab\GrpC_Identikit\ImageProcessing\ImagePNG\MoustacheMario.png",
+    3: "C:\Users\doria\Documents\ECAM\Année 4\IT & Robotics Lab\GrpC_Identikit\ImageProcessing\ImagePNG\MArio.png"
+}
+
+def add_filter(image, filter_image, landmarks):
+    # Use eyes as reference points
+    left_eye = landmarks[33]
+    right_eye = landmarks[263]
+    
+    # Distance between both eyes --> filter size
+    eye_dist = np.linalg.norm(np.array(left_eye) - np.array(right_eye))
+    
+    # Adjust the factor for a smaller filter size
+    scaling_factor = 2.75
+    filter_width = int(eye_dist * scaling_factor)
+    filter_height = int(filter_width * filter_image.shape[0] / filter_image.shape[1])
+    resized_filter = cv2.resize(filter_image, (filter_width, filter_height))
+    
+    # Filter position on the face
+    center_x = int((left_eye[0] + right_eye[0]) / 2)
+    center_y = int((left_eye[1] + right_eye[1]) / 2)
+    x = int(center_x - filter_width / 2)
+    y = int(center_y - filter_height / 2)
+
+    # Extract the alpha channel (transparency) from the filter image
+    alpha_channel = resized_filter[:, :, 3] / 255.0  # Normalize alpha to range [0, 1]
+    filter_rgb = resized_filter[:, :, :3]  # Extract the RGB channels
+
+    # Overlay the filter onto the image, using the alpha channel as a mask
+    for i in range(resized_filter.shape[0]):
+        for j in range(resized_filter.shape[1]):
+            if alpha_channel[i, j] > 0:  # Check if the pixel is not fully transparent
+                # Blend the pixels: (1 - alpha) * original + alpha * filter
+                for c in range(3):
+                    image[y + i, x + j, c] = (1 - alpha_channel[i, j]) * image[y + i, x + j, c] + alpha_channel[i, j] * filter_rgb[i, j, c]
+
+    return image
+
+def apply_filter_by_choice(choice, input_image_path):
+    # Validate the filter choice
+    if choice not in filter_images:
+        print(f"Filter {choice} does not exist. Please choose a valid filter number.")
+        return
+    
+    # Load the input image and filter
+    input_image = cv2.imread(input_image_path)
+    filter_image_path = filter_images[choice]
+    filter_image = cv2.imread(filter_image_path, cv2.IMREAD_UNCHANGED)
+
+    # RGB for Mediapipe
+    rgb_image = cv2.cvtColor(input_image, cv2.COLOR_BGR2RGB)
+
+    # FaceMesh init
+    with mp_face_mesh.FaceMesh(min_detection_confidence=0.5, min_tracking_confidence=0.5) as face_mesh:
+        # Face detection + key points
+        results = face_mesh.process(rgb_image)
+        
+        if results.multi_face_landmarks:
+            for face_landmarks in results.multi_face_landmarks:
+                # Key points
+                landmarks = [(lm.x * input_image.shape[1], lm.y * input_image.shape[0]) for lm in face_landmarks.landmark]
+
+                # Apply the filter
+                input_image = add_filter(input_image, filter_image, landmarks)
+
+    # Display result
+    cv2.imshow("Image with Filter", input_image)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+
+    # Save the image
+    output_path = f"output_image_filter_{choice}.jpg"
+    cv2.imwrite(output_path, input_image)
+    print(f"Saved filtered image to {output_path}")
+
+# Example usage:
+filter_choice = int(input("Enter the filter number (1, 2, or 3): "))
+apply_filter_by_choice(filter_choice, "Dorianvide.png")

Saving/opencv dnn.py

@@ -0,0 +1,34 @@
+import cv2 as cv
+import numpy as np
+# Chargement du modèle Mask R-CNN
+net = cv.dnn.readNetFromTensorflow('frozen_inference_graph.pb', 'mask_rcnn_inception_v2_coco_2018_01_28.pbtxt')
+# Charger l'image
+imagepath = "photo.jpg"
+image = cv.imread(imagepath)
+h, w = image.shape[:2]
+# Prétraiter l'image pour Mask R-CNN
+blob = cv.dnn.blobFromImage(image, 1.0, (w, h), (104.0, 177.0, 123.0), swapRB=True, crop=False)
+net.setInput(blob)
+# Obtenir les sorties du modèle
+output_layers = net.getUnconnectedOutLayersNames()
+detections = net.forward(output_layers)
+# Appliquer la segmentation pour la personne
+mask_image = image.copy()
+for detection in detections:
+    for obj in detection:
+        scores = obj[5:]
+        class_id = np.argmax(scores)
+        confidence = scores[class_id]
+        if class_id == 0 and confidence > 0.5:  # Class 0 corresponds to "person"
+            # Coordonner la boîte englobante
+            box = obj[0:4] * np.array([w, h, w, h])
+            (x, y, x2, y2) = box.astype("int")
+            # Créer un masque de la personne
+            mask = np.zeros((h, w), dtype=np.uint8)
+            mask[y:y2, x:x2] = 255  # Définir la zone de la personne
+            # Appliquer le masque sur l'image originale
+            result = cv.bitwise_and(image, image, mask=mask)
+            # Montrer l'image avec la personne segmentée et l'arrière-plan supprimé
+            cv.imshow("Segmented Image", result)
+            cv.waitKey(0)
+            cv.destroyAllWindows()

Saving/red_dots.py

@@ -0,0 +1,118 @@
+import cv2
+import mediapipe as mp
+import numpy as np
+
+# Initialize MediaPipe Face Detection and Drawing utilities
+mp_face_detection = mp.solutions.face_detection
+mp_drawing = mp.solutions.drawing_utils
+mp_face_mesh = mp.solutions.face_mesh
+
+# Initialize variables
+face_detection = mp_face_detection.FaceDetection(min_detection_confidence=0.2)
+face_mesh = mp_face_mesh.FaceMesh(min_detection_confidence=0.2, min_tracking_confidence=0.5)
+
+# Initialize camera
+cap = cv2.VideoCapture(0)
+
+# Variables for button states
+greyscale = False
+sunglasses_on = False
+saved_image = None
+
+# Function to overlay sunglasses
+def overlay_sunglasses(image, face_landmarks, sunglasses_img):
+    if len(face_landmarks) > 0:
+        # Coordinates for the eyes based on face mesh landmarks
+        left_eye = face_landmarks[33]
+        right_eye = face_landmarks[263]
+
+        # Calculate the center between the eyes for positioning sunglasses
+        eye_center_x = int((left_eye[0] + right_eye[0]) / 2)
+        eye_center_y = int((left_eye[1] + right_eye[1]) / 2)
+
+        # Calculate the scaling factor for sunglasses based on the distance between the eyes
+        eye_distance = np.linalg.norm(np.array(left_eye) - np.array(right_eye))
+        scale_factor = eye_distance / sunglasses_img.shape[1]
+
+        # Resize sunglasses based on scale factor
+        sunglasses_resized = cv2.resize(sunglasses_img, None, fx=scale_factor, fy=scale_factor)
+
+        # Determine the region of interest (ROI) for sunglasses
+        start_x = int(eye_center_x - sunglasses_resized.shape[1] / 2)
+        start_y = int(eye_center_y - sunglasses_resized.shape[0] / 2)
+
+        # Overlay sunglasses on the face
+        for i in range(sunglasses_resized.shape[0]):
+            for j in range(sunglasses_resized.shape[1]):
+                if sunglasses_resized[i, j][3] > 0:  # If not transparent
+                    image[start_y + i, start_x + j] = sunglasses_resized[i, j][0:3]  # Apply RGB channels
+
+    return image
+
+# Function to apply greyscale filter
+def toggle_greyscale(image, greyscale):
+    if greyscale:
+        return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    else:
+        return image
+
+# Load sunglasses image with transparency (PNG)
+sunglasses_img = cv2.imread("sunglasses.png", cv2.IMREAD_UNCHANGED)
+
+while cap.isOpened():
+    ret, frame = cap.read()
+
+    if not ret:
+        break
+
+    # Flip the frame horizontally for a mirror effect
+    frame = cv2.flip(frame, 1)
+
+    # Convert to RGB for MediaPipe processing
+    rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+    results_detection = face_detection.process(rgb_frame)
+    results_mesh = face_mesh.process(rgb_frame)
+
+    # Draw face detection bounding boxes
+    if results_detection.detections:
+        for detection in results_detection.detections:
+            mp_drawing.draw_detection(frame, detection)
+
+    # Draw face mesh landmarks
+    if results_mesh.multi_face_landmarks:
+        for face_landmarks in results_mesh.multi_face_landmarks:
+            mp_drawing.draw_landmarks(frame, face_landmarks, mp_face_mesh.FACEMESH_CONTOURS)
+
+    # Apply greyscale filter if enabled
+    frame = toggle_greyscale(frame, greyscale)
+
+    # Display the image
+    cv2.imshow('Face Capture Controls', frame)
+
+    key = cv2.waitKey(1) & 0xFF
+
+    # Save Image
+    if key == ord('s'):  # Press 's' to save image
+        saved_image = frame.copy()
+        cv2.imwrite("captured_image.png", saved_image)
+        print("Image Saved!")
+
+    # Retake Image
+    elif key == ord('r'):  # Press 'r' to retake image
+        saved_image = None
+        print("Image Retaken!")
+
+    # Toggle Greyscale
+    elif key == ord('g'):  # Press 'g' to toggle greyscale
+        greyscale = not greyscale
+        print(f"Greyscale: {'Enabled' if greyscale else 'Disabled'}")
+
+
+    # Kill Switch
+    elif key == ord('q'):  # Press 'q' to quit
+        break
+
+# Release camera and close all windows
+cap.release()
+cv2.destroyAllWindows()
+

Saving/remove_bg.py

@@ -0,0 +1,19 @@
+
+# Importing Required Modules 
+from rembg import remove 
+from PIL import Image 
+  
+# Store path of the image in the variable input_path 
+input_path =  'ImageJPG/Felipe.png' 
+  
+# Store path of the output image in the variable output_path 
+output_path = 'ImagePNG/Felipe.png' 
+  
+# Processing the image 
+input = Image.open(input_path) 
+  
+# Removing the background from the given Image 
+output = remove(input) 
+  
+#Saving the image in the given path 
+output.save(output_path)