add files

app.py

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+import cv2
+import cvzone
+import numpy as np
+import os
+import gradio as gr
+
+# Load the YuNet model
+model_path = 'face_detection_yunet_2023mar.onnx'
+face_detector = cv2.FaceDetectorYN.create(model_path, "", (320, 320))
+
+# Initialize the glass number
+num = 1
+overlay = cv2.imread(f'glasses/glass{num}.png', cv2.IMREAD_UNCHANGED)
+
+# Count glasses files
+def count_files_in_directory(directory):
+    file_count = 0
+    for root, dirs, files in os.walk(directory):
+        file_count += len(files)
+    return file_count
+
+directory_path = 'glasses'
+total_glass_num = count_files_in_directory(directory_path)
+
+# Change glasses
+def change_glasses(action):
+    global num, overlay
+    if action == 'next':
+        num += 1
+        if num > total_glass_num:
+            num = 1
+        overlay = cv2.imread(f'glasses/glass{num}.png', cv2.IMREAD_UNCHANGED)
+    return overlay
+
+# Process frame for overlay
+def process_frame(frame):
+    global overlay
+    height, width = frame.shape[:2]
+    face_detector.setInputSize((width, height))
+    _, faces = face_detector.detect(frame)
+
+    if faces is not None:
+        for face in faces:
+            x, y, w, h = face[:4].astype(int)
+            face_landmarks = face[4:14].reshape(5, 2).astype(int)  # Facial landmarks
+
+            # Get the nose position (assuming landmark index 2 is the nose)
+            nose_x, nose_y = face_landmarks[2].astype(int)
+
+            # Resize the overlay
+            overlay_resize = cv2.resize(overlay, (int(w * 1.15), int(h * 0.8)))
+
+            # Ensure the frame is a NumPy array and writable
+            frame = np.array(frame)
+
+            # Calculate the position to center the glasses on the nose
+            overlay_x = nose_x - overlay_resize.shape[1] // 2
+            overlay_y = y  # Adjust this if needed for better vertical alignment
+
+            # Overlay the glasses
+            try:
+                frame = cvzone.overlayPNG(frame, overlay_resize, [overlay_x, overlay_y])
+            except Exception as e:
+                print(f"Error overlaying glasses: {e}")
+
+    return frame
+
+# # Display the frame
+# def display_frame():
+#     cap = cv2.VideoCapture(0)
+#     while True:
+#         ret, frame = cap.read()
+#         if not ret:
+#             break
+
+#         frame = process_frame(frame)
+#         cv2.imshow('SnapLens', frame)
+
+#         k = cv2.waitKey(10)
+#         if k == ord('q'):
+#             break
+
+#     cap.release()
+#     cv2.destroyAllWindows()
+
+# display_frame()
+
+# Gradio webcam input
+def webcam_input(frame):
+    frame = process_frame(frame)
+    return frame
+
+# Gradio Interface
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+            input_img = gr.Image(label="Input", sources="webcam", streaming=True)
+    input_img.stream(webcam_input, [input_img], [input_img], time_limit=15, stream_every=0.1, concurrency_limit=30)
+if __name__ == "__main__":
+    demo.launch(share=True)

face_detection_yunet_2023mar.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8f2383e4dd3cfbb4553ea8718107fc0423210dc964f9f4280604804ed2552fa4
+size 232589

requirements.txt

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+gradio
+cvzone
+opencv-python
+numpy