Testing ip-adapter-face-full-v15

.gitattributes

@@ -53,3 +53,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+shape_predictor_5_face_landmarks.dat filter=lfs diff=lfs merge=lfs -text

crop_head_dlib5.py

@@ -0,0 +1,99 @@
+import cv2
+import dlib
+import numpy as np
+from PIL import Image, ImageOps
+
+#https://gist.github.com/Norod/757e63802b0b28fbdab9d98b2e646ac2
+
+MODEL_PATH = "shape_predictor_5_face_landmarks.dat" # You need to download this file from http://dlib.net/files/shape_predictor_5_face_landmarks.dat.bz2
+detector = dlib.get_frontal_face_detector() # Initialize dlib's face detector model
+
+def get_face_landmarks(image_path):
+    # Load the image
+    image = cv2.imread(image_path)
+    try:
+        image = ImageOps.exif_transpose(image)
+    except:
+        print("exif problem, not rotating")
+
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+    # Initialize dlib's facial landmarks predictor
+    predictor = dlib.shape_predictor("shape_predictor_5_face_landmarks.dat")  
+
+    # Detect faces in the image
+    faces = detector(gray)
+
+    if len(faces) > 0:
+        # Assume the first face is the target, you can modify this based on your requirements
+        shape = predictor(gray, faces[0])
+        landmarks = np.array([[p.x, p.y] for p in shape.parts()])
+        return landmarks
+    else:
+        return None
+
+def calculate_roll_and_yaw(landmarks):
+    # Calculate the roll angle using the angle between the eyes
+    roll_angle = np.degrees(np.arctan2(landmarks[1, 1] - landmarks[0, 1], landmarks[1, 0] - landmarks[0, 0]))
+
+    # Calculate the yaw angle using the angle between the eyes and the tip of the nose
+    yaw_angle = np.degrees(np.arctan2(landmarks[1, 1] - landmarks[2, 1], landmarks[1, 0] - landmarks[2, 0]))
+
+    return roll_angle, yaw_angle
+
+def detect_and_crop_head(input_image, factor=3.0):
+    # Get facial landmarks
+    landmarks = get_face_landmarks(input_image)
+
+    if landmarks is not None:
+        # Calculate the center of the face using the mean of the landmarks
+        center_x = int(np.mean(landmarks[:, 0]))
+        center_y = int(np.mean(landmarks[:, 1]))
+
+        # Calculate the size of the cropped region
+        size = int(max(np.max(landmarks[:, 0]) - np.min(landmarks[:, 0]),
+                       np.max(landmarks[:, 1]) - np.min(landmarks[:, 1])) * factor)
+
+        # Calculate the new coordinates for a 1:1 aspect ratio
+        x_new = max(0, center_x - size // 2)
+        y_new = max(0, center_y - size // 2)
+
+        # Calculate roll and yaw angles
+        roll_angle, yaw_angle = calculate_roll_and_yaw(landmarks)
+
+        # Adjust the center coordinates based on the yaw and roll angles
+        shift_x = int(size * 0.4 * np.sin(np.radians(yaw_angle)))
+        shift_y = int(size * 0.4 * np.sin(np.radians(roll_angle)))
+
+        #print(f'Roll angle: {roll_angle:.2f}, Yaw angle: {yaw_angle:.2f} shift_x: {shift_x}, shift_y: {shift_y}')
+
+        center_x += shift_x
+        center_y += shift_y
+
+        # Calculate the new coordinates for a 1:1 aspect ratio
+        x_new = max(0, center_x - size // 2)
+        y_new = max(0, center_y - size // 2)
+
+        # Read the input image using PIL
+        image = Image.open(input_image)
+
+        # Crop the head region with a 1:1 aspect ratio
+        cropped_head = np.array(image.crop((x_new, y_new, x_new + size, y_new + size)))
+
+        # Convert the cropped head back to PIL format
+        cropped_head_pil = Image.fromarray(cropped_head)
+
+        # Return the cropped head image
+        return cropped_head_pil
+    else:
+        return None
+
+if __name__ == '__main__':
+    input_image_path = 'input.jpg'
+    output_image_path = 'output.jpg'
+
+    # Detect and crop the head
+    cropped_head = detect_and_crop_head(input_image_path, factor=3.0)
+
+    # Save the cropped head image
+    cropped_head.save(output_image_path)

shape_predictor_5_face_landmarks.dat

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c4b1e9804792707d3a405c2c16a80a20269e6675021f64a41d30fffafbc41888
+size 9150489