Initial commit for image processing and vision project

Author: JosephJonathanFernandes

ipv1.py

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+import streamlit as st
+import cv2
+import numpy as np
+from PIL import Image
+
+st.title("🖼️ Basic Image Enhancer (IPV Project for Beginners)")
+
+# Upload the image
+uploaded_file = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])
+
+if uploaded_file:
+    image = Image.open(uploaded_file)
+    img_np = np.array(image)
+    st.image(img_np, caption="Original Image")
+
+    # Convert to Grayscale
+    gray = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY)
+    st.image(gray, caption="Grayscale Image", channels="GRAY")
+
+    # Apply Gaussian Blur
+    blur = cv2.GaussianBlur(gray, (5, 5), 0)
+    st.image(blur, caption="Gaussian Blur", channels="GRAY")
+
+    # Apply Median Blur
+    median = cv2.medianBlur(gray, 5)
+    st.image(median, caption="Median Blur", channels="GRAY")
+
+    # Sobel Edge Detection
+    sobel_x = cv2.Sobel(gray, cv2.CV_64F, 1, 0)
+    sobel_y = cv2.Sobel(gray, cv2.CV_64F, 0, 1)
+    sobel_combined = cv2.magnitude(sobel_x, sobel_y)
+    st.image(np.uint8(sobel_combined), caption="Sobel Edge Detection", channels="GRAY")
+
+    # Canny Edge Detection
+    canny = cv2.Canny(gray, 100, 200)
+    st.image(canny, caption="Canny Edge Detection", channels="GRAY")
+
+    # Morphological Operations
+    kernel = np.ones((5, 5), np.uint8)
+    
+    dilation = cv2.dilate(gray, kernel, iterations=1)
+    erosion = cv2.erode(gray, kernel, iterations=1)
+    opening = cv2.morphologyEx(gray, cv2.MORPH_OPEN, kernel)
+    closing = cv2.morphologyEx(gray, cv2.MORPH_CLOSE, kernel)
+
+    st.image(dilation, caption="Dilation", channels="GRAY")
+    st.image(erosion, caption="Erosion", channels="GRAY")
+    st.image(opening, caption="Opening", channels="GRAY")
+    st.image(closing, caption="Closing", channels="GRAY")

ipv2.py

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+import streamlit as st
+import cv2
+import numpy as np
+from PIL import Image
+
+st.set_page_config(page_title="Best Image Enhancer", layout="wide")
+st.title("🌟 Best Image Enhancer (Beginner IPV Project)")
+st.markdown("""
+<div style='background-color:#f0f8ff; padding:20px; border-radius:10px'>
+<h3 style='color:#2e86de'>Upload an image and apply powerful image processing tools!</h3>
+</div>
+""", unsafe_allow_html=True)
+
+uploaded_file = st.file_uploader("📤 Upload your image", type=["jpg", "png", "jpeg"])
+
+if uploaded_file:
+    col1, col2 = st.columns(2)
+    image = Image.open(uploaded_file)
+    img_np = np.array(image)
+    with col1:
+        st.image(img_np, caption="🖼️ Original Image", use_container_width=True)
+
+    # Convert to Grayscale
+    gray = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY)
+    with col2:
+        st.image(gray, caption="🖤 Grayscale Image", use_container_width=True, channels="GRAY")
+
+    st.markdown("---")
+    st.subheader("✨ Apply Filters and Morphological Operations")
+
+    kernel_size = st.slider("🔧 Kernel Size (odd only)", min_value=3, max_value=11, step=2, value=5)
+    kernel = np.ones((kernel_size, kernel_size), np.uint8)
+
+    col3, col4 = st.columns(2)
+
+    # Gaussian Blur
+    blur = cv2.GaussianBlur(gray, (kernel_size, kernel_size), 0)
+    # Median Blur
+    median = cv2.medianBlur(gray, kernel_size)
+
+    with col3:
+        st.image(blur, caption="🌫️ Gaussian Blur", use_container_width=True, channels="GRAY")
+        st.image(median, caption="🎯 Median Blur", use_container_width=True, channels="GRAY")
+
+    # Sobel
+    sobel_x = cv2.Sobel(gray, cv2.CV_64F, 1, 0)
+    sobel_y = cv2.Sobel(gray, cv2.CV_64F, 0, 1)
+    sobel_combined = cv2.magnitude(sobel_x, sobel_y)
+
+    # Canny
+    canny = cv2.Canny(gray, 100, 200)
+
+    with col4:
+        st.image(np.uint8(sobel_combined), caption="⚡ Sobel Edge Detection", use_container_width=True, channels="GRAY")
+        st.image(canny, caption="🧠 Canny Edge Detection", use_container_width=True, channels="GRAY")
+
+    st.markdown("---")
+    st.subheader("🧱 Morphological Operations")
+
+    col5, col6 = st.columns(2)
+    dilation = cv2.dilate(gray, kernel, iterations=1)
+    erosion = cv2.erode(gray, kernel, iterations=1)
+    opening = cv2.morphologyEx(gray, cv2.MORPH_OPEN, kernel)
+    closing = cv2.morphologyEx(gray, cv2.MORPH_CLOSE, kernel)
+
+    with col5:
+        st.image(dilation, caption="🚀 Dilation", use_container_width=True, channels="GRAY")
+        st.image(erosion, caption="⛏️ Erosion", use_container_width=True, channels="GRAY")
+
+    with col6:
+        st.image(opening, caption="🔓 Opening", use_container_width=True, channels="GRAY")
+        st.image(closing, caption="🔒 Closing", use_container_width=True, channels="GRAY")

ipv3.py

@@ -0,0 +1,79 @@
+import streamlit as st
+import cv2
+import numpy as np
+from PIL import Image
+
+st.set_page_config(page_title="Best Image Enhancer", layout="wide")
+st.title("🌟 Best Image Enhancer (Beginner IPV Project)")
+
+st.markdown("""
+<div style='background-color:#f0f8ff; padding:20px; border-radius:10px'>
+<h3 style='color:#2e86de'>Upload an image and choose what operation you'd like to apply!</h3>
+</div>
+""", unsafe_allow_html=True)
+
+uploaded_file = st.file_uploader("📤 Upload your image", type=["jpg", "png", "jpeg"])
+
+if uploaded_file:
+    image = Image.open(uploaded_file)
+    img_np = np.array(image)
+    st.image(img_np, caption="🖼️ Original Image", use_container_width=True)
+
+    # Convert to Grayscale
+    gray = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY)
+
+    # Select Operation
+    st.markdown("---")
+    operation = st.selectbox("🔍 Choose an Operation", [
+        "Grayscale",
+        "Gaussian Blur",
+        "Median Blur",
+        "Sobel Edge Detection",
+        "Canny Edge Detection",
+        "Dilation",
+        "Erosion",
+        "Opening",
+        "Closing"
+    ])
+
+    kernel_size = st.slider("🔧 Kernel Size (odd only)", min_value=3, max_value=11, step=2, value=5)
+    kernel = np.ones((kernel_size, kernel_size), np.uint8)
+
+    if operation == "Grayscale":
+        st.image(gray, caption="🖤 Grayscale Image", use_container_width=True, channels="GRAY")
+
+    elif operation == "Gaussian Blur":
+        blur = cv2.GaussianBlur(gray, (kernel_size, kernel_size), 0)
+        st.image(blur, caption="🌫️ Gaussian Blur", use_container_width=True, channels="GRAY")
+
+    elif operation == "Median Blur":
+        median = cv2.medianBlur(gray, kernel_size)
+        st.image(median, caption="🎯 Median Blur", use_container_width=True, channels="GRAY")
+
+    elif operation == "Sobel Edge Detection":
+        sobel_x = cv2.Sobel(gray, cv2.CV_64F, 1, 0)
+        sobel_y = cv2.Sobel(gray, cv2.CV_64F, 0, 1)
+        sobel_combined = cv2.magnitude(sobel_x, sobel_y)
+        st.image(np.uint8(sobel_combined), caption="⚡ Sobel Edge Detection", use_container_width=True, channels="GRAY")
+
+    elif operation == "Canny Edge Detection":
+        canny = cv2.Canny(gray, 100, 200)
+        st.image(canny, caption="🧠 Canny Edge Detection", use_container_width=True, channels="GRAY")
+
+    elif operation == "Dilation":
+        dilation = cv2.dilate(gray, kernel, iterations=1)
+        st.image(dilation, caption="🚀 Dilation", use_container_width=True, channels="GRAY")
+
+    elif operation == "Erosion":
+        erosion = cv2.erode(gray, kernel, iterations=1)
+        st.image(erosion, caption="⛏️ Erosion", use_container_width=True, channels="GRAY")
+
+    elif operation == "Opening":
+        opening = cv2.morphologyEx(gray, cv2.MORPH_OPEN, kernel)
+        st.image(opening, caption="🔓 Opening", use_container_width=True, channels="GRAY")
+
+    elif operation == "Closing":
+        closing = cv2.morphologyEx(gray, cv2.MORPH_CLOSE, kernel)
+        st.image(closing, caption="🔒 Closing", use_container_width=True, channels="GRAY")
+
+    st.success(f"✅ Operation Applied: {operation}")

ipv4.py

@@ -0,0 +1,130 @@
+import streamlit as st
+import cv2
+import numpy as np
+from PIL import Image
+
+st.set_page_config(page_title="Ultimate Image Enhancer", layout="wide")
+st.title("🎨 Ultimate Image Enhancer – Beginner Friendly Image Processing App")
+
+st.markdown("""
+<div style='background-color:#f0f8ff; padding:20px; border-radius:10px'>
+<h3 style='color:#2e86de'>Upload an image and apply a wide range of filters and operations!</h3>
+</div>
+""", unsafe_allow_html=True)
+
+uploaded_file = st.file_uploader("📤 Upload your image", type=["jpg", "png", "jpeg"])
+
+if uploaded_file:
+    image = Image.open(uploaded_file)
+    img_np = np.array(image)
+
+    st.image(img_np, caption="🖼️ Original Image", use_container_width=True)
+
+    gray = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY)
+    operation = st.selectbox("🛠️ Choose an Operation", [
+        "Grayscale",
+        "Gaussian Blur",
+        "Median Blur",
+        "Bilateral Filter",
+        "Sobel Edge Detection",
+        "Canny Edge Detection",
+        "Histogram Equalization",
+        "Thresholding",
+        "Adaptive Thresholding",
+        "Brightness & Contrast",
+        "Sharpening",
+        "Rotation",
+        "Flip",
+        "Dilation",
+        "Erosion",
+        "Opening",
+        "Closing",
+        "Gradient",
+        "Top Hat",
+        "Black Hat"
+    ])
+
+    kernel_size = st.slider("🧩 Kernel Size (odd only)", 3, 11, step=2, value=5)
+    kernel = np.ones((kernel_size, kernel_size), np.uint8)
+
+    result = None
+
+    if operation == "Grayscale":
+        result = gray
+
+    elif operation == "Gaussian Blur":
+        result = cv2.GaussianBlur(img_np, (kernel_size, kernel_size), 0)
+
+    elif operation == "Median Blur":
+        result = cv2.medianBlur(img_np, kernel_size)
+
+    elif operation == "Bilateral Filter":
+        result = cv2.bilateralFilter(img_np, 9, 75, 75)
+
+    elif operation == "Sobel Edge Detection":
+        sobel_x = cv2.Sobel(gray, cv2.CV_64F, 1, 0)
+        sobel_y = cv2.Sobel(gray, cv2.CV_64F, 0, 1)
+        sobel = cv2.magnitude(sobel_x, sobel_y)
+        result = np.uint8(sobel)
+
+    elif operation == "Canny Edge Detection":
+        result = cv2.Canny(gray, 100, 200)
+
+    elif operation == "Histogram Equalization":
+        result = cv2.equalizeHist(gray)
+
+    elif operation == "Thresholding":
+        _, result = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
+
+    elif operation == "Adaptive Thresholding":
+        result = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C,
+                                       cv2.THRESH_BINARY, kernel_size, 5)
+
+    elif operation == "Brightness & Contrast":
+        brightness = st.slider("🔆 Brightness", -100, 100, 0)
+        contrast = st.slider("🎚️ Contrast", -100, 100, 0)
+        result = cv2.convertScaleAbs(img_np, alpha=1 + contrast / 100.0, beta=brightness)
+
+    elif operation == "Sharpening":
+        sharp_kernel = np.array([[0, -1, 0],
+                                 [-1, 5, -1],
+                                 [0, -1, 0]])
+        result = cv2.filter2D(img_np, -1, sharp_kernel)
+
+    elif operation == "Rotation":
+        angle = st.slider("🔁 Rotate Angle", -180, 180, 0)
+        (h, w) = img_np.shape[:2]
+        center = (w // 2, h // 2)
+        M = cv2.getRotationMatrix2D(center, angle, 1.0)
+        result = cv2.warpAffine(img_np, M, (w, h))
+
+    elif operation == "Flip":
+        flip_code = st.radio("↔️ Flip Mode", ["Horizontal", "Vertical"])
+        result = cv2.flip(img_np, 1 if flip_code == "Horizontal" else 0)
+
+    elif operation == "Dilation":
+        result = cv2.dilate(gray, kernel, iterations=1)
+
+    elif operation == "Erosion":
+        result = cv2.erode(gray, kernel, iterations=1)
+
+    elif operation == "Opening":
+        result = cv2.morphologyEx(gray, cv2.MORPH_OPEN, kernel)
+
+    elif operation == "Closing":
+        result = cv2.morphologyEx(gray, cv2.MORPH_CLOSE, kernel)
+
+    elif operation == "Gradient":
+        result = cv2.morphologyEx(gray, cv2.MORPH_GRADIENT, kernel)
+
+    elif operation == "Top Hat":
+        result = cv2.morphologyEx(gray, cv2.MORPH_TOPHAT, kernel)
+
+    elif operation == "Black Hat":
+        result = cv2.morphologyEx(gray, cv2.MORPH_BLACKHAT, kernel)
+
+    if result is not None:
+        st.markdown("---")
+        st.image(result, caption=f"✨ Result: {operation}", use_container_width=True,
+                 channels="GRAY" if len(result.shape) == 2 else "BGR")
+        st.success("✅ Operation Applied Successfully!")