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Update app.py
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app.py
CHANGED
@@ -1,36 +1,31 @@
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import spaces
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import base64
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import cv2
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import numpy as np
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import gradio as gr
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from PIL import Image
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from io import BytesIO
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@spaces.GPU
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def crop_face(base64_image):
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try:
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# Decode the base64 image
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img_data = base64.b64decode(base64_image)
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np_arr = np.frombuffer(img_data, np.uint8)
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image = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
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if image is None:
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return "
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# Load the pre-trained face detector
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face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
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# Convert the image to grayscale
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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# Detect faces in the image
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faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
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# If no faces are detected, return message
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if len(faces) == 0:
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return "No faces
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# Crop the first face
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x, y, w, h = faces[0]
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face_crop = image[y:y+h, x:x+w]
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@@ -44,21 +39,18 @@ def crop_face(base64_image):
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return f"An error occurred: {str(e)}"
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def image_to_base64(image):
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buffered = io.BytesIO()
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image.save(buffered, format="JPEG")
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# Encode bytes to Base64 string
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img_str = base64.b64encode(buffered.getvalue()).decode()
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return img_str
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# Define the Gradio interface using the updated syntax
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base64_converter_interface = gr.Interface(
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fn=image_to_base64,
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inputs=gr.Image(type="pil"),
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outputs=gr.Textbox(),
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title="Image to Base64 Encoder",
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description="Upload an image
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)
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face_crop_interface = gr.Interface(
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@@ -70,4 +62,4 @@ face_crop_interface = gr.Interface(
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)
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if __name__ == "__main__":
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gr.TabbedInterface([base64_converter_interface, face_crop_interface], ["Convert to Base64","Crop Face"]).launch()
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import base64
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import numpy as np
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import cv2
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import gradio as gr
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from PIL import Image
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from io import BytesIO
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def crop_face(base64_image):
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try:
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# Decode the base64 image to an OpenCV format
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img_data = base64.b64decode(base64_image)
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np_arr = np.frombuffer(img_data, np.uint8)
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image = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
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if image is None:
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return "Image decoding failed. Check the input format."
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# Load the pre-trained face detector
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face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
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# Convert the image to grayscale for face detection
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
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if len(faces) == 0:
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return "No faces detected in the image."
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# Crop the first detected face
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x, y, w, h = faces[0]
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face_crop = image[y:y+h, x:x+w]
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return f"An error occurred: {str(e)}"
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def image_to_base64(image):
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buffered = BytesIO()
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image.save(buffered, format="JPEG")
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img_str = base64.b64encode(buffered.getvalue()).decode()
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return img_str
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# Define the Gradio interfaces
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base64_converter_interface = gr.Interface(
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fn=image_to_base64,
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inputs=gr.Image(type="pil"),
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outputs=gr.Textbox(),
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title="Image to Base64 Encoder",
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description="Upload an image to convert it to a base64 encoded string."
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)
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face_crop_interface = gr.Interface(
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)
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if __name__ == "__main__":
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gr.TabbedInterface([base64_converter_interface, face_crop_interface], ["Convert to Base64", "Crop Face"]).launch(share=True)
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