Create app.py

app.py

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+import streamlit as st
+import utils
+import cv2
+import numpy
+import io
+import tempfile
+from PIL import Image 
+import moviepy.editor as mpy
+from camera_input_live import camera_input_live
+
+st.set_page_config(
+    page_title="Hello Text Detection",
+    page_icon=":sun_with_face:",
+    layout="centered",
+    initial_sidebar_state="expanded",)
+
+st.title("Hello Text Dection :sun_with_face:")
+
+st.sidebar.header("Type")
+source_radio = st.sidebar.radio("Select Source", ["IMAGE", "VIDEO", "WEBCAM"])
+
+st.sidebar.header("Confidence")
+conf_threshold = float(st.sidebar.slider("Select the Confidence Threshold", 10, 100, 20))/100
+
+input = None 
+if source_radio == "IMAGE":
+    st.sidebar.header("Upload")
+    input = st.sidebar.file_uploader("Choose an image.", type=("jpg", "png"))
+    if input is not None:
+        uploaded_image = Image.open(input)
+        uploaded_image_cv = cv2.cvtColor(numpy.array(uploaded_image), cv2.COLOR_RGB2BGR)
+        boxes, resized_image = utils.predict_image(uploaded_image_cv, conf_threshold = conf_threshold)
+        result_image = utils.convert_result_to_image(uploaded_image_cv, resized_image, boxes, conf_labels=False)
+        st.image(result_image, channels = "RGB")
+        st.markdown(f"<h4 style='color: blue;'><strong>The result of running the AI inference on an image.</strong></h4>", unsafe_allow_html=True)
+    else: 
+        st.image("data/intel_rnb.jpg")
+        st.write("Click on 'Browse Files' in the sidebar to run inference on an image." )
+
+def play_video(video_source):
+    camera = cv2.VideoCapture(video_source)
+    fps = camera.get(cv2.CAP_PROP_FPS)
+    temp_file_2 = tempfile.NamedTemporaryFile(delete=False,suffix='.mp4')
+    video_row=[]
+    # frame
+    total_frames = int(camera.get(cv2.CAP_PROP_FRAME_COUNT))
+    progress_bar = st.progress(0)
+    frame_count = 0
+    st_frame = st.empty()
+    while(camera.isOpened()):
+        ret, frame = camera.read()
+        if ret:
+            try:
+                boxes, resized_image = utils.predict_image(frame, conf_threshold)
+                visualized_image = utils.convert_result_to_image(frame, resized_image, boxes, conf_labels=False)
+            except:
+                visualized_image = frame
+            st_frame.image(visualized_image, channels = "BGR")
+            video_row.append(cv2.cvtColor(visualized_image,cv2.COLOR_BGR2RGB))
+            frame_count +=1 
+            progress_bar.progress(frame_count/total_frames, text=None)
+    
+        else:
+            progress_bar.empty()
+            camera.release()
+            st_frame.empty()
+            break
+        clip = mpy.ImageSequenceClip(video_row, fps = fps)
+        clip.write_videofile(temp_file_2.name)
+        st.video(temp_file_2.name)
+
+# 파일 업로드 처리
+temporary_location = None
+if source_radio == "VIDEO":
+    st.sidebar.header("Upload")
+    input_file = st.sidebar.file_uploader("Choose a video.", type=("mp4"))
+    if input_file is not None:
+        # 파일을 임시 경로에 저장
+        g = io.BytesIO(input_file.read())
+        temporary_location = "upload.mp4"
+        with open(temporary_location, "wb") as out:
+            out.write(g.read())
+        out.close()
+    # 업로드된 비디오 파일이 있는 경우 비디오 재생
+    if temporary_location is not None:
+        play_video(temporary_location)
+    else:
+        st.video("data/sample_video.mp4")
+        st.write("Click on 'Browse Files' in the sidebar to run inference on a video.")
+
+if source_radio == "WEBCAM":
+    input = camera_input_live()
+    uploaded_image = Image.open(input)
+    uploaded_image_cv = cv2.cvtColor(numpy.array(uploaded_image), cv2.COLOR_RGB2BGR)
+    boxes, resized_image = utils.predict_image(uploaded_image_cv, conf_threshold)
+    visualized_image = utils.convert_result_to_image(uploaded_image_cv, resized_image, boxes, conf_labels=False)
+    st.image(visualized_image, channels = "RGB")