Create app.py

app.py

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+import streamlit as st
+import numpy as np
+import cv2
+import tempfile
+import os
+from io import BytesIO
+from matplotlib import pyplot as plt
+
+# Load model files
+prototxt_path = "colorization_deploy_v2.prototxt"
+model_path = "colorization_release_v2.caffemodel"
+kernel_path = "pts_in_hull.npy"
+
+# Streamlit app title
+st.title("Video Colorization App")
+
+# File upload
+uploaded_video = st.file_uploader("Upload a black and white video", type=["mp4", "avi"])
+
+if uploaded_video is not None:
+    # Save uploaded video to a temporary file
+    tfile = tempfile.NamedTemporaryFile(delete=False)
+    tfile.write(uploaded_video.read())
+    video_path = tfile.name
+
+    # Output path for the colorized video
+    output_path = os.path.join(tempfile.gettempdir(), "colorized_video.mp4")
+
+    # Load the pre-trained model
+    net = cv2.dnn.readNetFromCaffe(prototxt_path, model_path)
+    points = np.load(kernel_path)
+    points = points.transpose().reshape(2, 313, 1, 1)
+    net.getLayer(net.getLayerId("class8_ab")).blobs = [points.astype(np.float32)]
+    net.getLayer(net.getLayerId("conv8_313_rh")).blobs = [np.full([1, 313], 2.686, dtype="float32")]
+
+    # Open the video file
+    cap = cv2.VideoCapture(video_path)
+
+    # Get video properties
+    frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    fps = cap.get(cv2.CAP_PROP_FPS)
+
+    # Create a VideoWriter object to save the colorized video
+    fourcc = cv2.VideoWriter_fourcc(*"mp4v")
+    out = cv2.VideoWriter(output_path, fourcc, fps, (frame_width, frame_height))
+
+    frame_count = 0
+    stframe = st.empty()  # Placeholder for showing frames in Streamlit
+
+    # Process each frame
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        frame_count += 1
+        print(f"Frame: {frame_count}")
+        # Convert frame to LAB color space and preprocess
+        normalized = frame.astype("float32") / 255.0
+        lab = cv2.cvtColor(normalized, cv2.COLOR_BGR2LAB)
+        resized = cv2.resize(lab, (224, 224))
+        L = cv2.split(resized)[0]
+        L -= 50
+
+        # Set the input and get the colorization
+        net.setInput(cv2.dnn.blobFromImage(L))
+        ab = net.forward()[0, :, :, :].transpose((1, 2, 0))
+        ab = cv2.resize(ab, (frame.shape[1], frame.shape[0]))
+
+        # Combine with the L channel
+        L = cv2.split(lab)[0]
+        colorized = np.concatenate((L[:, :, np.newaxis], ab), axis=2)
+        colorized = cv2.cvtColor(colorized, cv2.COLOR_LAB2BGR)
+        colorized = (255 * colorized).astype("uint8")
+
+              
+
+        # Write colorized frame to output
+        out.write(colorized)
+
+    # Release resources
+    cap.release()
+    out.release()
+
+    # Provide a download link for the colorized video
+    st.success("Video colorization completed!")
+    with open(output_path, "rb") as file:
+        btn = st.download_button(label="Download Colorized Video", data=file, file_name="colorized_video.mp4", mime="video/mp4")