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Face Detection Camera.py

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+import cv2
+import numpy as np
+
+# Function to determine eye color based on HSV values
+def get_eye_color(hue, value):
+    if value < 50:
+        return "black"  # If the brightness value is low, the eye is considered black.
+    elif hue >= 100 and hue <= 140:
+        return "blue"
+    elif hue >= 40 and hue <= 80:
+        return "green"
+    elif hue >= 10 and hue <= 25:
+        return "brown"
+    elif hue >= 25 and hue <= 35:
+        return "Hazel"
+    elif hue >= 85 and hue <= 135:
+        return "Gray"
+    elif hue >= 30 and hue <= 50:
+        return "Amber"
+    elif hue >= 140 and hue <= 160:
+        return "Violet"
+    else:
+        return "Undefined"  # If the hue does not match any known colors.
+
+def detect_face_and_eyes_from_camera():
+    # Open the camera (the default camera is 0, but you can use other numbers for multiple cameras)
+    cap = cv2.VideoCapture(0)
+
+    if not cap.isOpened():
+        print("Unable to access the camera.")
+        return
+
+    while True:
+        # Capture each frame from the camera
+        ret, image = cap.read()
+
+        if not ret:
+            print("Failed to capture image from the camera.")
+            break
+
+        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+        # Load Haar Cascade files for face and eye detection
+        face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+        eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_eye.xml')
+
+        # Detect faces in the image
+        faces = face_cascade.detectMultiScale(gray, 1.3, 5)
+
+        # Detect eyes within each detected face
+        for (x, y, w, h) in faces:
+            # Draw a rectangle around the face
+            cv2.rectangle(image, (x, y), (x + w, y + h), (255, 0, 0), 2)
+
+            # Extract the face region to detect eyes within it
+            face_region = gray[y:y + h, x:x + w]
+
+            # Detect eyes within the face region
+            eyes = eye_cascade.detectMultiScale(face_region)
+
+            for (ex, ey, ew, eh) in eyes:
+                # Draw a rectangle around each eye
+                cv2.rectangle(image, (x + ex, y + ey), (x + ex + ew, y + ey + eh), (0, 255, 0), 2)
+
+                # Extract the eye region
+                eye = image[y + ey:y + ey + eh, x + ex:x + ex + ew]
+
+                # Resize the image to improve performance
+                eye_resized = cv2.resize(eye, (50, 50))
+
+                # Convert the image to HSV to analyze the color
+                hsv_eye = cv2.cvtColor(eye_resized, cv2.COLOR_BGR2HSV)
+
+                # Calculate the average hue and value for the dominant color
+                avg_hue = np.mean(hsv_eye[:, :, 0])  # Average Hue value
+                avg_value = np.mean(hsv_eye[:, :, 2])  # Average brightness (Value)
+
+                # Determine the eye color based on the Hue value
+                eye_color = get_eye_color(avg_hue, avg_value)
+
+                # Display the color name on the image
+                font = cv2.FONT_HERSHEY_SIMPLEX
+                cv2.putText(image, eye_color, (x + ex, y + ey - 10), font, 0.9, (255, 255, 255), 2, cv2.LINE_AA)
+
+        # Show the image with labeled eye colors
+        cv2.imshow('Camera Feed', image)
+
+        # Exit the loop when the "q" key is pressed
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+
+    # Release the camera and close the windows when done
+    cap.release()
+    cv2.destroyAllWindows()
+
+# Call the function to start detecting faces and eyes from the camera
+detect_face_and_eyes_from_camera()

Face Detection and Eye Color and skin color.py

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+import cv2
+import numpy as np
+
+# Function to determine the eye color based on Hue and Value in the HSV color space
+def get_eye_color(hue, value):
+    if value < 50:
+        return "black"  # If the brightness is low, the eye is considered black.
+    elif hue >= 100 and hue <= 140:
+        return "blue"
+    elif hue >= 40 and hue <= 80:
+        return "green"
+    elif hue >= 10 and hue <= 25:
+        return "brown"
+    elif hue >= 25 and hue <= 35:
+        return "Hazel"
+    elif hue >= 85 and hue <= 135:
+        return "Gray"
+    elif hue >= 30 and hue <= 50:
+        return "Amber"
+    elif hue >= 140 and hue <= 160:
+        return "Violet"
+    else:
+        return "Undefined"  # If the color doesn't match known color ranges
+
+# Function to determine the skin tone based on Hue, Saturation, and Value in the HSV color space
+def get_skin_tone(hue, saturation, value):
+    if saturation < 40:
+        return "Pale"  # If saturation is low, the skin is considered pale
+    elif hue >= 0 and hue <= 25:
+        return "Light skin"
+    elif hue >= 25 and hue <= 40:
+        return "Medium light skin"
+    elif hue >= 40 and hue <= 60:
+        return "Medium skin"
+    elif hue >= 60 and hue <= 90:
+        return "Medium dark skin"
+    elif hue >= 90 and hue <= 140:
+        return "Dark skin"
+    else:
+        return "Unknown skin tone"  # If the color doesn't match known skin tones
+
+# Function to detect face and eyes, then classify their colors and skin tone
+def detect_face_and_eyes(image_path):
+    # Load the image
+    image = cv2.imread(image_path)
+    if image is None:
+        print("Image not found or failed to load.")
+        return
+
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+    # Load Haar Cascade files for face and eye detection
+    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+    if face_cascade.empty():
+        print("Failed to load Haar Cascade file for face detection.")
+        return
+
+    eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_eye.xml')
+    if eye_cascade.empty():
+        print("Failed to load Haar Cascade file for eye detection.")
+        return
+
+    # Detect faces in the image
+    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
+
+    # For each detected face, detect eyes and classify their color
+    for (x, y, w, h) in faces:
+        cv2.rectangle(image, (x, y), (x + w, y + h), (255, 0, 0), 2)  # Draw a rectangle around the face
+        face_region = image[y:y + h, x:x + w]
+
+        # Detect eyes within the face region
+        eyes = eye_cascade.detectMultiScale(face_region)
+
+        # Ensure only two eyes are detected
+        eyes_detected = 0  # Counter for detected eyes
+        for (ex, ey, ew, eh) in eyes:
+            if eyes_detected < 2:  # If fewer than two eyes are detected
+                cv2.rectangle(image, (x + ex, y + ey), (x + ex + ew, y + ey + eh), (0, 255, 0), 2)  # Draw a rectangle around the eye
+                eye = image[y + ey:y + ey + eh, x + ex:x + ex + ew]  # Extract the eye region
+                eye_resized = cv2.resize(eye, (50, 50))  # Resize the eye image for better processing speed
+                hsv_eye = cv2.cvtColor(eye_resized, cv2.COLOR_BGR2HSV)  # Convert the eye image to HSV
+                avg_hue = np.mean(hsv_eye[:, :, 0])  # Get the average Hue value
+                avg_value = np.mean(hsv_eye[:, :, 2])  # Get the average Value (brightness)
+                eye_color = get_eye_color(avg_hue, avg_value)  # Determine the eye color
+
+                # Display the eye color on the image
+                font = cv2.FONT_HERSHEY_SIMPLEX
+                cv2.putText(image, eye_color, (x + ex, y + ey - 10), font, 0.9, (255, 255, 255), 2, cv2.LINE_AA)
+                eyes_detected += 1  # Increment the eye counter
+
+        # Extract the skin region (area below the face, roughly the neck and lower face)
+        skin_region = image[y + int(h/4):y + h, x:x + w]
+        hsv_skin = cv2.cvtColor(skin_region, cv2.COLOR_BGR2HSV)  # Convert the skin region to HSV
+        avg_hue_skin = np.mean(hsv_skin[:, :, 0])  # Get the average Hue of the skin
+        avg_saturation_skin = np.mean(hsv_skin[:, :, 1])  # Get the average Saturation of the skin
+        avg_value_skin = np.mean(hsv_skin[:, :, 2])  # Get the average Value of the skin
+        skin_tone = get_skin_tone(avg_hue_skin, avg_saturation_skin, avg_value_skin)  # Determine the skin tone
+
+        # Display the skin tone on the image
+        cv2.putText(image, skin_tone, (x, y - 10), font, 0.9, (255, 255, 255), 2, cv2.LINE_AA)
+
+    # Display the image with face, eye colors, and skin tone
+    cv2.imshow('Image', image)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+
+# Call the function with the image path
+detect_face_and_eyes('C:/Users/XPRISTO/Desktop/advanced programing/projekt/pp2.png')

Face Detection and Eye Color.py

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+import cv2
+import numpy as np
+
+# Function to determine eye color based on HSV values
+def get_eye_color(hue, value):
+    if value < 50:
+        return "black"  # If the brightness value is low, the eye is considered black.
+    elif hue >= 100 and hue <= 140:
+        return "blue"
+    elif hue >= 40 and hue <= 80:
+        return "green"
+    elif hue >= 10 and hue <= 25:
+        return "brown"
+    elif hue >= 25 and hue <= 35:
+        return "Hazel"
+    elif hue >= 85 and hue <= 135:
+        return "Gray"
+    elif hue >= 30 and hue <= 50:
+        return "Amber"
+    elif hue >= 140 and hue <= 160:
+        return "Violet"
+    else:
+        return "Undefined"  # If the hue does not match any known colors.
+
+def detect_face_and_eyes(image_path):
+    # Load the image
+    image = cv2.imread(image_path)
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+    # Load Haar Cascade files for detecting faces and eyes
+    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+    eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_eye.xml')
+
+    # Detect faces in the image
+    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
+
+    # Detect eyes within each detected face
+    for (x, y, w, h) in faces:
+        # Draw a rectangle around the face
+        cv2.rectangle(image, (x, y), (x + w, y + h), (255, 0, 0), 2)
+
+        # Extract the face region to detect eyes within it
+        face_region = gray[y:y + h, x:x + w]
+
+        # Detect eyes within the face region
+        eyes = eye_cascade.detectMultiScale(face_region)
+
+        for (ex, ey, ew, eh) in eyes:
+            # Draw a rectangle around each eye
+            cv2.rectangle(image, (x + ex, y + ey), (x + ex + ew, y + ey + eh), (0, 255, 0), 2)
+
+            # Extract the eye region
+            eye = image[y + ey:y + ey + eh, x + ex:x + ex + ew]
+
+            # Resize the eye region to improve processing speed
+            eye_resized = cv2.resize(eye, (50, 50))
+
+            # Convert the eye region to HSV color space for color analysis
+            hsv_eye = cv2.cvtColor(eye_resized, cv2.COLOR_BGR2HSV)
+
+            # Calculate the average hue and value of the eye region
+            avg_hue = np.mean(hsv_eye[:, :, 0])  # Average hue value
+            avg_value = np.mean(hsv_eye[:, :, 2])  # Average brightness (value)
+
+            # Determine the eye color based on the average hue and value
+            eye_color = get_eye_color(avg_hue, avg_value)
+
+            # Display the eye color name on the image
+            font = cv2.FONT_HERSHEY_SIMPLEX
+            cv2.putText(image, eye_color, (x + ex, y + ey - 10), font, 0.9, (255, 255, 255), 2, cv2.LINE_AA)
+
+            # Show the image with the labeled eye color
+            cv2.imshow('Image', image)
+            cv2.waitKey(0)
+            cv2.destroyAllWindows()
+
+# Call the function with the image path
+detect_face_and_eyes('C:/Users/XPRISTO/Desktop/project-2/photo4.jpg')