user-agent commited on
Commit
1e3d31f
·
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1 Parent(s): 3269a67

Update app.py

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Files changed (1) hide show
  1. app.py +11 -19
app.py CHANGED
@@ -1,36 +1,31 @@
1
- import spaces
2
  import base64
3
- import cv2
4
  import numpy as np
 
5
  import gradio as gr
6
  from PIL import Image
7
  from io import BytesIO
8
 
9
- @spaces.GPU
10
  def crop_face(base64_image):
11
  try:
12
- # Decode the base64 image
13
  img_data = base64.b64decode(base64_image)
14
  np_arr = np.frombuffer(img_data, np.uint8)
15
  image = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
16
 
17
  if image is None:
18
- return "Could not decode the image or no data in buffer"
19
-
20
  # Load the pre-trained face detector
21
  face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
22
 
23
- # Convert the image to grayscale
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  gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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-
26
- # Detect faces in the image
27
  faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
28
 
29
- # If no faces are detected, return message
30
  if len(faces) == 0:
31
- return "No faces found"
32
 
33
- # Crop the first face found
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  x, y, w, h = faces[0]
35
  face_crop = image[y:y+h, x:x+w]
36
 
@@ -44,21 +39,18 @@ def crop_face(base64_image):
44
  return f"An error occurred: {str(e)}"
45
 
46
  def image_to_base64(image):
47
- # Convert PIL Image to bytes
48
- buffered = io.BytesIO()
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  image.save(buffered, format="JPEG")
50
- # Encode bytes to Base64 string
51
  img_str = base64.b64encode(buffered.getvalue()).decode()
52
  return img_str
53
 
54
-
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- # Define the Gradio interface using the updated syntax
56
  base64_converter_interface = gr.Interface(
57
  fn=image_to_base64,
58
  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 and convert it to a Base64 encoded string."
62
  )
63
 
64
  face_crop_interface = gr.Interface(
@@ -70,4 +62,4 @@ face_crop_interface = gr.Interface(
70
  )
71
 
72
  if __name__ == "__main__":
73
- gr.TabbedInterface([base64_converter_interface, face_crop_interface], ["Convert to Base64","Crop Face"]).launch()
 
 
1
  import base64
 
2
  import numpy as np
3
+ import cv2
4
  import gradio as gr
5
  from PIL import Image
6
  from io import BytesIO
7
 
 
8
  def crop_face(base64_image):
9
  try:
10
+ # Decode the base64 image to an OpenCV format
11
  img_data = base64.b64decode(base64_image)
12
  np_arr = np.frombuffer(img_data, np.uint8)
13
  image = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
14
 
15
  if image is None:
16
+ return "Image decoding failed. Check the input format."
17
+
18
  # Load the pre-trained face detector
19
  face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
20
 
21
+ # Convert the image to grayscale for face detection
22
  gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
 
 
23
  faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
24
 
 
25
  if len(faces) == 0:
26
+ return "No faces detected in the image."
27
 
28
+ # Crop the first detected face
29
  x, y, w, h = faces[0]
30
  face_crop = image[y:y+h, x:x+w]
31
 
 
39
  return f"An error occurred: {str(e)}"
40
 
41
  def image_to_base64(image):
42
+ buffered = BytesIO()
 
43
  image.save(buffered, format="JPEG")
 
44
  img_str = base64.b64encode(buffered.getvalue()).decode()
45
  return img_str
46
 
47
+ # Define the Gradio interfaces
 
48
  base64_converter_interface = gr.Interface(
49
  fn=image_to_base64,
50
  inputs=gr.Image(type="pil"),
51
  outputs=gr.Textbox(),
52
  title="Image to Base64 Encoder",
53
+ description="Upload an image to convert it to a base64 encoded string."
54
  )
55
 
56
  face_crop_interface = gr.Interface(
 
62
  )
63
 
64
  if __name__ == "__main__":
65
+ gr.TabbedInterface([base64_converter_interface, face_crop_interface], ["Convert to Base64", "Crop Face"]).launch(share=True)