Create app.py

app.py

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+import streamlit as st
+from PIL import Image
+import cv2
+import numpy as np
+import subprocess
+import shutil
+import os
+import torch
+import time
+import streamlit_analytics
+
+
+def clear_detect_directory():
+    detect_directory = "yolov5/runs/detect"
+    if os.path.exists(detect_directory):
+        shutil.rmtree(detect_directory)
+    os.makedirs(detect_directory)
+
+
+def save_image():
+    st.title("Hand Sign Detection")
+    col1, col2 = st.columns(2)  # 2 for two col
+    pd_df = None
+    with col1:
+        genre = st.radio(
+            "Upload Your Hand Sign",
+            ('Browse', 'Camera'))
+
+        if genre == 'Camera':
+            uploaded_image = st.camera_input("Take a picture")
+
+        else:
+            uploaded_image = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+
+        if uploaded_image is not None:
+
+            # Convert the image to a format compatible with PIL and OpenCV
+            pil_image = Image.open(uploaded_image)
+            opencv_image = np.array(pil_image)
+            opencv_image = cv2.cvtColor(opencv_image, cv2.COLOR_BGR2RGB)
+
+            # Provide a file path to save the image
+            upload_image_path = "processed_image.jpg"
+            cv2.imwrite(upload_image_path, opencv_image)
+
+            st.success(f"Image saved as {upload_image_path}")
+            st.success("Processing Image...")
+
+            # label of image:
+            model = torch.hub.load('ultralytics/yolov5', 'custom', path='best.pt')  # local model
+            results = model(upload_image_path)
+            pd_df = (results.pandas().xyxy[0])
+
+            # max_confidence_name = pd_df.loc[pd_df['confidence'].idxmax(), 'name']
+
+            clear_detect_directory()
+
+            command = [
+                "python",
+                "yolov5/detect.py",
+                "--weights", "best.pt",
+                "--img", "416",
+                "--conf", "0.50",
+                "--source", upload_image_path
+            ]
+
+            process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
+            std_out, std_err = process.communicate()
+            if process.returncode != 0:
+                error_message = f"Error: {std_err}"
+                st.text(error_message)
+
+    with col2:
+        detect_image_pred = "yolov5/runs/detect/exp/processed_image.jpg"
+        if os.path.exists(detect_image_pred):
+            st.text("Detected Gesture")
+            st.image(detect_image_pred, caption="Detected Image", use_column_width=True)
+
+            st.text("Detection class probabilities")
+            if pd_df is not None:
+                st.text(pd_df)
+            else:
+                pass
+        else:
+            st.text("Detection Threshold is 60")
+    st.text("Detection Gesture")
+    st.text("Note: Make clean Gesture if not detected try another Gesture")
+    st.image("Untitled_img.png")
+
+
+
+streamlit_analytics.start_tracking()
+
+save_image()
+
+streamlit_analytics.stop_tracking()
+