update Chapter 4 Face Mesh

Author: Diraw

Chapter 4 Face Mesh/Basics.py

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+import cv2
+import mediapipe as mp
+import time
+
+# Open the video file
+cap = cv2.VideoCapture(
+    "E:\\Advance Computer Vision with Python\\main\\Chapter 3 Face Detection\\Videos\\4.mp4"
+)
+if not cap.isOpened():
+    print("Error: Could not open video.")
+    exit()
+
+pTime = 0
+
+# Initialize MediaPipe drawing tools and face mesh model
+mpDraw = mp.solutions.drawing_utils  # Import MediaPipe drawing utilities
+mpFaceMesh = (
+    mp.solutions.face_mesh
+)  # Import MediaPipe face mesh module for detecting and processing facial landmarks
+faceMesh = mpFaceMesh.FaceMesh(
+    max_num_faces=2
+)  # Initialize face mesh model, set to detect up to two faces
+drawSpec = mpDraw.DrawingSpec(
+    thickness=1, circle_radius=2
+)  # Create a drawing specification object for landmark and connection styles
+# thickness specifies line thickness, circle_radius specifies landmark point radius
+
+while True:
+    print("Reading video frame...")
+    success, img = cap.read()
+    print("Read success:", success)
+    if not success:
+        print("Finished processing video or error occurred.")
+        break
+
+    # Convert the image to RGB
+    imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    # Process the image to detect face mesh
+    results = faceMesh.process(imgRGB)
+
+    if results.multi_face_landmarks:
+        for faceLms in results.multi_face_landmarks:
+            # Draw the face mesh
+            mpDraw.draw_landmarks(
+                img, faceLms, mpFaceMesh.FACEMESH_TESSELATION, drawSpec, drawSpec
+            )
+            # mpDraw.draw_landmarks calls MediaPipe's drawing function to draw landmarks and connections on the image
+            # img: the image on which to draw the landmarks
+            # faceLms: the detected face landmarks
+            # mpFaceMesh.FACEMESH_TESSELATION: specifies the type of connections to draw, here it's face mesh tessellation
+            # drawSpec: defines the drawing style for landmarks and connections (e.g., thickness and circle radius)
+            # The last two drawSpec parameters define the drawing style for landmarks (keypoints) and connections
+            # You can choose to define them separately:
+            # drawSpecPoints = mpDraw.DrawingSpec(color=(0, 255, 0), thickness=1, circle_radius=2)  # Green
+            # drawSpecLines = mpDraw.DrawingSpec(color=(255, 0, 0), thickness=1)  # Blue
+            # Note that circle_radius in drawSpecLines has no effect; it only affects the drawing of landmarks, not connections
+
+            for id, lm in enumerate(faceLms.landmark):
+                # Get the image dimensions
+                ih, iw, ic = img.shape
+                x, y = int(lm.x * iw), int(lm.y * ih)
+                # Print each landmark's ID and coordinates
+                print(id, x, y)
+
+    # Calculate and display the frame rate
+    cTime = time.time()
+    fps = 1 / (cTime - pTime)
+    pTime = cTime
+    cv2.putText(
+        img, f"FPS: {int(fps)}", (20, 70), cv2.FONT_HERSHEY_PLAIN, 3, (255, 0, 0), 3
+    )
+
+    cv2.namedWindow("Image", cv2.WINDOW_NORMAL)  # Create a resizable window
+
+    # Display the image
+    cv2.imshow("Image", img)
+    cv2.waitKey(1)
+
+cap.release()
+cv2.destroyAllWindows()

Chapter 4 Face Mesh/FaceMeshModule.py

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+import cv2
+import mediapipe as mp
+import time
+
+
+class FaceMeshDetector:
+    def __init__(
+        self, staticMode=False, maxFaces=2, minDetectionCon=0.5, minTrackCon=0.5
+    ):
+        # Initialize parameters
+        self.staticMode = staticMode  # Use static mode or not
+        self.maxFaces = maxFaces  # Maximum number of faces to detect
+        self.minDetectionCon = minDetectionCon  # Minimum detection confidence
+        self.minTrackCon = minTrackCon  # Minimum tracking confidence
+
+        # Initialize MediaPipe drawing tools and face mesh model
+        self.mpDraw = mp.solutions.drawing_utils  # Drawing utilities
+        self.mpFaceMesh = mp.solutions.face_mesh  # Face mesh module
+        self.faceMesh = self.mpFaceMesh.FaceMesh(
+            static_image_mode=self.staticMode,
+            max_num_faces=self.maxFaces,
+            min_detection_confidence=self.minDetectionCon,
+            min_tracking_confidence=self.minTrackCon,
+        )
+
+        self.drawSpec = self.mpDraw.DrawingSpec(
+            thickness=1, circle_radius=2
+        )  # Drawing specifications
+
+    def findFaceMesh(self, img, draw=True):
+        # Convert the image to RGB
+        self.imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        # Process the image to detect face mesh
+        self.results = self.faceMesh.process(self.imgRGB)
+        faces = []  # Store detected facial landmarks
+        if self.results.multi_face_landmarks:
+            for faceLms in self.results.multi_face_landmarks:
+                if draw:
+                    # Draw the face mesh
+                    self.mpDraw.draw_landmarks(
+                        img,
+                        faceLms,
+                        self.mpFaceMesh.FACEMESH_TESSELATION,
+                        self.drawSpec,
+                        self.drawSpec,
+                    )
+                face = []  # Store landmarks for a single face
+                for id, lm in enumerate(faceLms.landmark):
+                    ih, iw, ic = img.shape  # Get image dimensions
+                    x, y = int(lm.x * iw), int(
+                        lm.y * ih
+                    )  # Convert normalized coordinates to pixel coordinates
+                    face.append([x, y])  # Add landmark coordinates
+                faces.append(face)  # Add to faces list
+        return img, faces  # Return image and facial landmarks
+
+
+def main():
+    # Open the video file
+    cap = cv2.VideoCapture(
+        "E:\\Advance Computer Vision with Python\\main\\Chapter 3 Face Detection\\Videos\\4.mp4"
+    )
+    pTime = 0  # Previous frame time
+    detector = FaceMeshDetector(maxFaces=2)  # Initialize face mesh detector
+    while True:
+        success, img = cap.read()
+        if not success:
+            break
+        img, faces = detector.findFaceMesh(img)  # Detect face mesh
+        if len(faces) != 0:
+            print(faces[0])  # Print landmarks of the first face
+        cTime = time.time()  # Current time
+        fps = 1 / (cTime - pTime)  # Calculate frames per second
+        pTime = cTime  # Update previous frame time
+        cv2.putText(
+            img, f"FPS: {int(fps)}", (20, 70), cv2.FONT_HERSHEY_PLAIN, 3, (0, 255, 0), 3
+        )  # Display FPS on the image
+        cv2.namedWindow("Image", cv2.WINDOW_NORMAL)  # Create a resizable window
+        cv2.imshow("Image", img)  # Show image
+        cv2.waitKey(1)  # Wait for keyboard input
+
+
+if __name__ == "__main__":
+    main()

Chapter 4 Face Mesh/README.md

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+# mp.solutions.face_mesh
+
+`mpFaceMesh = mp.solutions.face_mesh` imports the MediaPipe face mesh module for detecting and processing facial landmarks.
+
+`faceMesh = mpFaceMesh.FaceMesh(max_num_faces=2)` initializes the face mesh model, set to detect up to two faces.
+
+`results = faceMesh.process(imgRGB)` processes the image to detect face meshes.
+
+Parameters for `the mpFaceMesh.FaceMesh()` class include: `self.staticMode, self.maxFaces, self.minDetectionCon, self.minTrackCon`.
+
+- **staticMode**: Whether to treat each image as a static image. If True, face detection is performed on every frame. If False, it tracks the detected face, which is faster.
+- **maxFaces**: Maximum number of faces to detect.
+- **minDetectionCon**: Minimum confidence threshold for detection. Faces below this confidence are ignored.
+- **minTrackCon**: Minimum confidence threshold for tracking. Tracking below this confidence is ignored.
+
+# mp.solutions.drawing_utils
+
+`mpDraw = mp.solutions.drawing_utils` imports the MediaPipe drawing utilities module.
+
+`drawSpec = mpDraw.DrawingSpec(thickness=1, circle_radius=2)` creates a drawing specification object to define the style for landmarks and connections, where thickness specifies line thickness and circle_radius specifies the radius of landmark points.
+
+`mpDraw.draw_landmarks(img, faceLms, mpFaceMesh.FACEMESH_TESSELATION, drawSpec, drawSpec)` calls MediaPipe's drawing function to draw landmarks and connections on the image.
+
+- **img**: The image on which to draw landmarks.
+- **faceLms**: The detected face landmarks collection.
+- **mpFaceMesh.FACEMESH_TESSELATION**: Specifies the type of connections to draw; here, it's the face mesh tessellation.
+- **drawSpec**: Defines the drawing style for landmarks and connections (e.g., thickness and circle radius). The first drawSpec parameter defines the style for landmarks (keypoints), such as circle radius and color, while the second defines the style for connections, such as line thickness and color.
+
+# Issues in Source Code
+
+These bugs won't cause errors during execution but will appear during debugging.
+
+## 1、Parameter Name Change in New Version
+
+Before:
+
+![原来](./pics/原来.png)
+
+Now:
+
+![现在](./pics/现在.png)
+
+## 2、Positional Argument Needs to Be a Keyword Argument
+
+![关键字传参](./pics/关键字传参.png)

Chapter 4 Face Mesh/Videos/1.mp4

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+1,https://www.pexels.com/zh-cn/video/3796263/
+2,https://www.pexels.com/zh-cn/video/4340125/
+3,https://www.pexels.com/zh-cn/video/5137640/
+4,https://www.pexels.com/zh-cn/video/4761954/