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| import streamlit as st | |
| import cv2 | |
| import numpy as np | |
| from PIL import Image | |
| import io | |
| st.set_page_config(page_title="Image Transformer", layout="centered") | |
| st.title("🖼️ Image Transformer") | |
| uploaded_file = st.file_uploader("📤 Upload an Image", type=["jpg", "jpeg", "png"]) | |
| if uploaded_file: | |
| try: | |
| image = Image.open(uploaded_file).convert("RGB") | |
| img_array = np.array(image) | |
| original_shape = img_array.shape | |
| # Show original in smaller size | |
| st.image(img_array, caption=f"Original Image | {original_shape[1]}x{original_shape[0]}", width=300) | |
| # Sidebar controls | |
| with st.sidebar.expander("🎨 Effects"): | |
| effect = st.selectbox("Choose Effect", ["None", "Grayscale", "Gaussian Blur", "Canny Edge Detection", "HSV", "LAB"]) | |
| with st.sidebar.expander("🔄 Rotate"): | |
| rotate = st.slider("Rotate (degrees)", -180, 180, 0) | |
| with st.sidebar.expander("↔️ Flip"): | |
| flip_h = st.checkbox("Flip Horizontally") | |
| flip_v = st.checkbox("Flip Vertically") | |
| with st.sidebar.expander("📐 Shear & Translate"): | |
| shear_x = st.slider("Shear X", -0.5, 0.5, 0.0) | |
| shear_y = st.slider("Shear Y", -0.5, 0.5, 0.0) | |
| translate_x = st.slider("Translate X", -100, 100, 0) | |
| translate_y = st.slider("Translate Y", -100, 100, 0) | |
| with st.sidebar.expander("✂️ Crop"): | |
| crop = st.checkbox("Enable Cropping") | |
| if crop: | |
| max_top = original_shape[0] // 2 | |
| max_side = original_shape[1] // 2 | |
| crop_top = st.slider("Crop Top", 0, max_top, 0) | |
| crop_bottom = st.slider("Crop Bottom", 0, max_top, 0) | |
| crop_left = st.slider("Crop Left", 0, max_side, 0) | |
| crop_right = st.slider("Crop Right", 0, max_side, 0) | |
| # Start transformation | |
| transformed = img_array.copy() | |
| if effect == "Grayscale": | |
| transformed = cv2.cvtColor(transformed, cv2.COLOR_RGB2GRAY) | |
| elif effect == "Gaussian Blur": | |
| transformed = cv2.GaussianBlur(transformed, (11, 11), 0) | |
| elif effect == "Canny Edge Detection": | |
| gray = cv2.cvtColor(transformed, cv2.COLOR_RGB2GRAY) | |
| transformed = cv2.Canny(gray, 100, 200) | |
| elif effect == "HSV": | |
| transformed = cv2.cvtColor(transformed, cv2.COLOR_RGB2HSV) | |
| elif effect == "LAB": | |
| transformed = cv2.cvtColor(transformed, cv2.COLOR_RGB2Lab) | |
| rows, cols = transformed.shape[:2] | |
| M = np.float32([[1, shear_x, translate_x], [shear_y, 1, translate_y]]) | |
| transformed = cv2.warpAffine(transformed, M, (cols, rows)) | |
| M_rotate = cv2.getRotationMatrix2D((cols / 2, rows / 2), rotate, 1) | |
| transformed = cv2.warpAffine(transformed, M_rotate, (cols, rows)) | |
| if flip_h: | |
| transformed = cv2.flip(transformed, 1) | |
| if flip_v: | |
| transformed = cv2.flip(transformed, 0) | |
| if crop: | |
| transformed = transformed[crop_top:rows - crop_bottom, crop_left:cols - crop_right] | |
| # Show transformed in smaller size | |
| st.image(transformed, caption="Transformed Image", width=300, channels="GRAY" if len(transformed.shape) == 2 else "RGB") | |
| # Download button | |
| final_image = Image.fromarray(cv2.cvtColor(transformed, cv2.COLOR_GRAY2RGB) if len(transformed.shape) == 2 else transformed) | |
| buf = io.BytesIO() | |
| final_image.save(buf, format="PNG") | |
| byte_im = buf.getvalue() | |
| st.download_button("📥 Download Transformed Image", byte_im, "transformed.png", "image/png") | |
| except Exception as e: | |
| st.error(f"❌ Error: {e}") | |
| else: | |
| st.info("📌 Upload an image to get started.") | |