brainai-spaces commited on
Commit
b5a6688
·
verified ·
1 Parent(s): 4592a37

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +3 -3
app.py CHANGED
@@ -1,7 +1,7 @@
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  import streamlit as st
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  import utils
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  import cv2
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- import numpy
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  import io
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  import tempfile
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  from PIL import Image
@@ -28,7 +28,7 @@ if source_radio == "IMAGE":
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  input = st.sidebar.file_uploader("Choose an image.", type=("jpg", "png"))
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  if input is not None:
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  uploaded_image = Image.open(input)
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- uploaded_image_cv = cv2.cvtColor(numpy.array(uploaded_image), cv2.COLOR_RGB2BGR)
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  boxes, resized_image = utils.predict_image(uploaded_image_cv, conf_threshold = conf_threshold)
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  result_image = utils.convert_result_to_image(uploaded_image_cv, resized_image, boxes, conf_labels=False)
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  st.image(result_image, channels = "RGB")
@@ -92,7 +92,7 @@ if source_radio == "VIDEO":
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  if source_radio == "WEBCAM":
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  input = camera_input_live()
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  uploaded_image = Image.open(input)
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- uploaded_image_cv = cv2.cvtColor(numpy.array(uploaded_image), cv2.COLOR_RGB2BGR)
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  boxes, resized_image = utils.predict_image(uploaded_image_cv, conf_threshold)
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  visualized_image = utils.convert_result_to_image(uploaded_image_cv, resized_image, boxes, conf_labels=False)
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  st.image(visualized_image, channels = "RGB")
 
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  import streamlit as st
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  import utils
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  import cv2
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+ import numpy as np
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  import io
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  import tempfile
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  from PIL import Image
 
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  input = st.sidebar.file_uploader("Choose an image.", type=("jpg", "png"))
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  if input is not None:
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  uploaded_image = Image.open(input)
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+ uploaded_image_cv = cv2.cvtColor(np.array(uploaded_image), cv2.COLOR_RGB2BGR)
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  boxes, resized_image = utils.predict_image(uploaded_image_cv, conf_threshold = conf_threshold)
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  result_image = utils.convert_result_to_image(uploaded_image_cv, resized_image, boxes, conf_labels=False)
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  st.image(result_image, channels = "RGB")
 
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  if source_radio == "WEBCAM":
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  input = camera_input_live()
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  uploaded_image = Image.open(input)
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+ uploaded_image_cv = cv2.cvtColor(np.array(uploaded_image), cv2.COLOR_RGB2BGR)
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  boxes, resized_image = utils.predict_image(uploaded_image_cv, conf_threshold)
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  visualized_image = utils.convert_result_to_image(uploaded_image_cv, resized_image, boxes, conf_labels=False)
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  st.image(visualized_image, channels = "RGB")