Update app.py

app.py

@@ -1,62 +1,80 @@
 import streamlit as st
-from PIL import Image, ImageEnhance, ImageFilter
+import cv2
 import numpy as np
+from PIL import Image
 from io import BytesIO
 
-# Function to apply the filter
+# Function to apply Lightroom-like filters
 def apply_filter(img):
-    # Convert image to editable format
-    img = img.convert("RGB")
-
-    # Apply exposure
-    exposure = 1 + (-0.31)  # Adjusted for Brightness
-    enhancer = ImageEnhance.Brightness(img)
-    img = enhancer.enhance(exposure)
-
-    # Apply contrast
-    contrast = 1 + (-18 / 100)  # Adjusted for Contrast
-    enhancer = ImageEnhance.Contrast(img)
-    img = enhancer.enhance(contrast)
-
-    # Highlights and shadows approximation
-    img_array = np.array(img, dtype=np.float32) / 255.0
-    shadows = 1.5  # Adjusted for Shadows (+50)
-    highlights = 0.0  # Adjusted for Highlights (-100)
-    img_array = np.clip(img_array * shadows + highlights, 0, 1)
-    img = Image.fromarray((img_array * 255).astype(np.uint8))
-
-    # Whites and blacks approximation
-    whites = 1.12  # Adjusted for Whites (+12)
-    blacks = 0.80  # Adjusted for Blacks (-20)
-    img_array = np.clip(img_array * whites * blacks, 0, 1)
-    img = Image.fromarray((img_array * 255).astype(np.uint8))
-
-    # Vibrance and saturation
-    img_hsv = img.convert("HSV")
-    h, s, v = img_hsv.split()
-    vibrance = 1 + 70 / 100
-    s = s.point(lambda i: min(255, max(0, int(i * vibrance))))
-    saturation = 1 + 20 / 100
-    s = s.point(lambda i: min(255, max(0, int(i * saturation))))
-    img = Image.merge("HSV", (h, s, v)).convert("RGB")
-
-    # Texture approximation
-    texture = 1 + 4 / 100
-    enhancer = ImageEnhance.Sharpness(img)
-    img = enhancer.enhance(texture)
-
-    # Sharpening using UnsharpMask
+    # Convert PIL Image to OpenCV format
+    img = np.array(img)
+    img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+
+    # Exposure: Adjust brightness
+    exposure = -0.31  # Negative for darker, positive for brighter
+    img = cv2.convertScaleAbs(img, alpha=1.0, beta=exposure * 100)
+
+    # Contrast
+    contrast = -18
+    alpha = 1 + contrast / 100.0  # Scaling factor
+    img = cv2.convertScaleAbs(img, alpha=alpha, beta=0)
+
+    # Highlights and Shadows
+    highlights = -100
+    shadows = 50
+    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+    v = hsv[:, :, 2]
+    v = np.clip(v * (1 + shadows / 100.0) + highlights, 0, 255).astype(np.uint8)
+    hsv[:, :, 2] = v
+    img = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
+
+    # Whites and Blacks
+    whites = 12
+    blacks = -20
+    img = np.clip(img + whites, 0, 255).astype(np.uint8)
+    img = np.clip(img + blacks, 0, 255).astype(np.uint8)
+
+    # Temperature and Tint
+    temp = -2  # Blue (+ve) or Yellow (-ve)
+    tint = 2  # Green (-ve) or Magenta (+ve)
+    b, g, r = cv2.split(img)
+    b = np.clip(b + temp, 0, 255).astype(np.uint8)
+    r = np.clip(r + tint, 0, 255).astype(np.uint8)
+    img = cv2.merge((b, g, r))
+
+    # Vibrance
+    vibrance = 70
+    lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
+    l, a, b = cv2.split(lab)
+    a = np.clip(a * (1 + vibrance / 100.0), 0, 255).astype(np.uint8)
+    b = np.clip(b * (1 + vibrance / 100.0), 0, 255).astype(np.uint8)
+    img = cv2.merge((l, a, b))
+    img = cv2.cvtColor(img, cv2.COLOR_LAB2BGR)
+
+    # Saturation
+    saturation = 20
+    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+    s = hsv[:, :, 1]
+    s = np.clip(s * (1 + saturation / 100.0), 0, 255).astype(np.uint8)
+    hsv[:, :, 1] = s
+    img = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
+
+    # Texture (Clarity approximation)
+    texture = 4
+    kernel = np.array([[-1, -1, -1], [-1, 8 + texture, -1], [-1, -1, -1]])
+    img = cv2.filter2D(img, -1, kernel)
+
+    # Sharpening
+    sharpening = 18
     radius = 1.2
-    detail = 1 + 30 / 100
-    img = img.filter(
-        ImageFilter.UnsharpMask(radius=radius, percent=int(detail * 100), threshold=3)
-    )
+    gaussian = cv2.GaussianBlur(img, (0, 0), sigmaX=radius)
+    img = cv2.addWeighted(img, 1 + sharpening / 100.0, gaussian, -sharpening / 100.0, 0)
 
     return img
 
 # Streamlit app
 st.title("Classic Photo Editor")
-st.write("Upload an image to apply the filter.")
+st.write("Upload an image to apply the Lightroom-like filter.")
 
 # Upload image
 uploaded_file = st.file_uploader("Choose an image", type=["jpg", "jpeg", "png"])
@@ -72,11 +90,14 @@ if uploaded_file:
     # Apply the filter
     st.subheader("Filtered Image")
     filtered_img = apply_filter(img)
-    st.image(filtered_img, use_column_width=True)
+
+    # Convert back to PIL Image for display
+    filtered_pil = Image.fromarray(cv2.cvtColor(filtered_img, cv2.COLOR_BGR2RGB))
+    st.image(filtered_pil, use_column_width=True)
 
     # Provide download button
     buf = BytesIO()
-    filtered_img.save(buf, format="JPEG")
+    filtered_pil.save(buf, format="JPEG")
     byte_im = buf.getvalue()
     st.download_button(
         label="Download Filtered Image",