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import gradio as gr | |
import mediapipe as mp | |
import numpy as np | |
import cv2 | |
title = "Mishigify Me" | |
description = " Demo for adding some mishig to this" | |
article = "<p style='text-align: center'><a href='https://google.github.io/mediapipe/solutions/face_detection.html' target='_blank'>Mediapipe Face Detection</a> | <a href='https://github.com/google/mediapipe' target='_blank'>Github Repo</a></p>" | |
mp_face_detection = mp.solutions.face_detection | |
mp_drawing = mp.solutions.drawing_utils | |
def draw_mishigs(image, results): | |
height, width, _ = image.shape | |
output_img = image.copy() | |
if results.detections: | |
for detection in results.detections: | |
face_coordinates = np.array([[detection.location_data.relative_keypoints[i].x*width, | |
detection.location_data.relative_keypoints[i].y*height] | |
for i in [0,1,3]], dtype=np.float32) | |
M = cv2.getAffineTransform(huggingface_landmarks, face_coordinates) | |
transformed_huggingface = cv2.warpAffine(huggingface_image, M, (width, height)) | |
transformed_huggingface_mask = transformed_huggingface[:,:,3] != 0 | |
output_img[transformed_huggingface_mask] = transformed_huggingface[transformed_huggingface_mask,:3] | |
return output_img | |
def mishig_me(image): | |
with mp_face_detection.FaceDetection( | |
model_selection=1, | |
min_detection_confidence=0.01) as face_detection: | |
# Convert the BGR image to RGB and process it with MediaPipe Face Mesh. | |
results = face_detection.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) | |
return draw_mishigs(image, results) | |
# Load hugging face logo and landmark coordinates | |
huggingface_image = cv2.imread("images/mishig-2 (1).png", cv2.IMREAD_UNCHANGED) | |
huggingface_image = cv2.cvtColor(huggingface_image, cv2.COLOR_BGRA2RGBA) | |
huggingface_landmarks = np.array([[747,697],[1289,697],[1022,1116]], dtype=np.float32) | |
gr.Interface(mishig_me, | |
inputs=gr.Image(label="Input Image"), | |
outputs=gr.Image(label="Output Image"), | |
title=title, | |
examples=[["images/people.jpg"]], | |
description=description, | |
article=article).launch() | |