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2018_SD_processing_parameters

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+# Used function from rov_preprocessing.py with adjustable parameters
+def full_preprocessing3_7(image_rgb, enable_udcp=True, enable_wb=True, enable_clahe1=True,
+                       enable_clahe2=True, enable_canny=True, enable_unsharp=True):
+    if enable_udcp:
+        image_rgb = UDCP(image_rgb, omega=0.50, t0=0.7, r=15, eps=1e-3, radius=25)
+    if enable_wb:
+        image_rgb = white_balance_blend(image_rgb, gamma=0.95, blend_factor=0.25)
+    if enable_clahe1:
+        image_rgb = preprocess_clahe(image_rgb, clipLimit=0.2, tileGridSize=(8,8))
+    if enable_clahe2:
+        image_rgb = color_balance_and_saturation_enhancement(image_rgb, saturation_factor=1.0, clip_limit=0.2, tile_grid_size=(8,8))
+    if enable_canny:
+        image_rgb = edge_enhancement_canny(image_rgb, edge_intensity=0.00005)
+    if enable_unsharp:
+        image_rgb = unsharp_mask(image_rgb, sigma=1.0, strength=1.0)
+
+    return image_rgb.astype('uint8')
+

2019_HD_processing_parameters

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+# 2019 Processing parameters
+## 2019 had high quality HD videos
+
+
+# Used function from rov_preprocessing.py with adjustable parameters
+def full_preprocessing3_7(image_rgb, enable_udcp=False, enable_wb=True, enable_clahe1=True,
+                       enable_clahe2=True, enable_canny=True, enable_unsharp=True):
+    if enable_udcp:
+        image_rgb = UDCP(image_rgb, omega=0.50, t0=0.7, r=15, eps=1e-3, radius=25)
+    if enable_wb:
+        image_rgb = white_balance_blend(image_rgb, gamma=0.85, blend_factor=0.5)
+    if enable_clahe1:
+        image_rgb = preprocess_clahe(image_rgb, clipLimit=0.2, tileGridSize=(8,8))
+    if enable_clahe2:
+        image_rgb = color_balance_and_saturation_enhancement(image_rgb, saturation_factor=1.1, clip_limit=0.2, tile_grid_size=(8,8))
+    if enable_canny:
+        image_rgb = edge_enhancement_canny(image_rgb, edge_intensity=0.05)
+    if enable_unsharp:
+        image_rgb = unsharp_mask(image_rgb, sigma=1.0, strength=3.0)
+
+    return image_rgb.astype('uint8')

concatenate_videos.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f4884294",
+   "metadata": {},
+   "source": [
+    "# Concatenate videos for visual comparison\n",
+    "1) Concatenate in series, several shorter video clips into 1 longer video.\n",
+    "2) Process/restore the combined video.\n",
+    "3) Concatenate the Original and Restored videos side-by-side for visual comparison.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "1b2346eb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#%% import libraries\n",
+    "import numpy as np\n",
+    "import cv2\n",
+    "\n",
+    "# custom functions\n",
+    "from restoration_toolbox import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "63f322a4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#%% Set directory\n",
+    "WORKING_DIR = \"C:\\\\Users\\\\torha\\\\div.prog\\\\Image-analysis\\\\Preprocessing\\\\\"\n",
+    "VIDEO_DIR = \"2019\\\\HD\\\\test-videos\\\\\"\n",
+    "OUTPUT_VID = \"2019\\\\HD\\\\result-videos\\\\\"\n",
+    "\n",
+    "# test videos\n",
+    "videoA = VIDEO_DIR + \"videoA.mp4\"\n",
+    "videoB = VIDEO_DIR + \"videoB.mp4\"\n",
+    "list_of_original_short_clips = [videoA, videoB]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cec28d36",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# concatenate several video clips into 1 video\n",
+    "video_list = list_of_original_short_clips\n",
+    "concatenate_videos(video_list, OUTPUT_VID + 'concatenated_video_clips.mp4')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cf74ac62",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Run image restoration (2019 HD parameter settings) on combined video\n",
+    "process_video(video_for_processing=OUTPUT_VID + 'concatenated_video_clips.mp4', \n",
+    "              output_vid_name=OUTPUT_VID+\"concatenated_video_clips_processed_steps3_7.mp4\",\n",
+    "              processing_func=full_preprocessing3_7_2019hd,\n",
+    "              video_codec='H264')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "950842bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Prepare videos for concatenation - now side by side\n",
+    "concatenated_video = OUTPUT_VID + \"concatenated_video_clips.mp4\"\n",
+    "concatenated_video_processed_steps3_7 = OUTPUT_VID + \"concatenated_video_clips_processed_steps3_7.mp4\"\n",
+    "list_of_concat_vids = [concatenated_video, concatenated_video_processed_steps3_7]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6a0f0ef0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Comparison of Original video(left) and Restored video(right) playing next to each other\n",
+    "combine_videos_side_by_side(concatenated_video, concatenated_video_processed_steps3_7, 'SideBySideComparison.mp4')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

plot_toolbox.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Utilities: Functions/Methods frequently used
+
+"""
+# import packages
+import math
+import numpy as np
+import matplotlib.pyplot as plt
+
+from PIL import Image
+import cv2
+
+from restoration_toolbox import *
+
+# #%% SETUP DIRECTORY
+# WORKING_DIR = "abc_home"
+# IMAGE_FOLDER = "test-images/"
+# OUTPUT_FOLDER = "result-images/"
+
+# #%% READ IMAGES
+
+# # PIL package
+# image_path = "image_path"  ### SET IMAGE HERE ###
+# image = Image.open(image_path)
+# image_array = np.array(image)
+
+# # Open CV package
+# image_path = "image_path"   #### SET NEW IMAGE HERE ###
+# image_bgr = cv2.imread(image_path) # BGR by open-cv default
+# image_rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)  # Convert from BGR to RGB
+
+#######################################################################
+# NORMAL PLOTS
+#######################################################################
+def plot_image(image, title="Image"):
+    """ Plot an image, either from a file path (str) or numpy array """
+    plt.figure(figsize=(6,6))
+
+    if isinstance(image, str):  # If image is a file path, read the image
+        image = cv2.imread(image)
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)  # Convert from BGR to RGB
+
+    # plot single image with title
+    plt.imshow(image)
+    plt.title(title)
+    plt.axis('off'); plt.show()
+
+def plot_gray_image(img, title, cmap='viridis'):
+    plt.imshow(img, cmap=cmap)
+    plt.title(title)
+    plt.axis('off'); plt.show()
+
+def plot_histogram(img, color='gray', alpha=0.7, title='Histogram', xlabel='Pixel Intensity', ylabel='Frequency'):
+    flat_img = img.flatten()
+    plt.hist(flat_img, bins=256, color='gray', alpha=0.7)
+    plt.title(title); plt.xlabel(xlabel);plt.ylabel(ylabel);plt.show()
+
+def plot_image_list(image_list):
+    """ Plot a list of images, which can either be file paths (str) or numpy arrays """
+    for i, image in enumerate(image_list):
+        plt.figure(figsize=(6,6))
+
+        # Check if the image is a file path string
+        if isinstance(image, str):
+            image = cv2.imread(image)
+            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)  # Convert from BGR to RGB
+
+        plt.imshow(image)
+        plt.title("Image " + str(i+1))
+        plt.axis('off')
+        plt.show()
+
+def plot_original_and_processed(image_original, image_processed):
+    """ Plot an image, either from a file path (str) or numpy array """
+    plt.figure(figsize=(6,6))
+
+    if isinstance(image_original, str):  # If image is a file path, read the image
+        image_original = cv2.imread(image_original)
+        image_original = cv2.cvtColor(image_original, cv2.COLOR_BGR2RGB)  # Convert from BGR to RGB
+
+    if isinstance(image_processed, str):  # If image is a file path, read the image
+        image_processed = cv2.imread(image_processed)
+        image_processed = cv2.cvtColor(image_processed, cv2.COLOR_BGR2RGB)  # Convert from BGR to RGB
+
+    # plot original image
+    plt.imshow(image_original)
+    plt.title("Original")
+    plt.axis('off')
+    plt.show()
+
+    # plot processed image
+    plt.imshow(image_processed)
+    plt.title("Processed")
+    plt.axis('off')
+    plt.show()
+
+
+
+#######################################################################
+# IMAGE ANALYSIS - INSPECTION PLOTS
+#######################################################################
+def visualize_images_and_histograms(image):
+    # Calculate features
+    ndbi = calculate_ndbi(image)
+    intensity = calculate_intensity(image)
+    background_index = calculate_background_index(image)
+    segmented_background = segment_background(image)
+
+    # Prepare data for plotting
+    images = [ndbi, intensity, background_index, segmented_background]
+    titles = ["NDBI", "Intensity", "Background Index", "Segmented Background"]
+
+    # Create 2x4 subplot
+    fig, axes = plt.subplots(2, 4, figsize=(16, 8))
+
+    for i, ax in enumerate(axes[0]):
+        # Plot image
+        ax.imshow(images[i], cmap='gray')
+        ax.set_title(titles[i])
+        ax.axis('off')
+
+    for i, ax in enumerate(axes[1]):
+        # Plot histogram
+        ax.hist(images[i].ravel(), bins=256, color='blue', alpha=0.7)
+        ax.set_title(titles[i] + ' Histogram')
+
+    # Display the plot
+    plt.tight_layout()
+    plt.show()
+
+def plot_hist_rgb(image_bgr):
+    image_rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)
+
+    # Split the image into R,G,B channels
+    r, g, b = cv2.split(image_rgb)
+
+    fig, ax = plt.subplots()
+    # Calculate and plot the histogram for each channel
+    colors = ['r', 'g', 'b']
+    for i, color in enumerate(colors):
+        histogram = cv2.calcHist([image_rgb], [i], None, [256], [0, 256])
+        ax.plot(histogram, color=color)
+
+    ax.set_title('RGB Histogram')
+    ax.set_xlabel('Bins')
+    ax.set_ylabel('# of Pixels')
+    plt.show()
+
+def plot_image_and_hist_rgb(image_rgb):
+    fig, ax = plt.subplots(1, 2, figsize=(12, 6))
+
+    # Display the image
+    ax[0].imshow(cv2.cvtColor(image_rgb, cv2.COLOR_BGR2RGB))  # assuming the image is in BGR format
+    ax[0].axis('off')
+
+    # Plot the histogram
+    colors = ['r', 'g', 'b']
+    for i, color in enumerate(colors):
+        histogram = cv2.calcHist([image_rgb], [i], None, [256], [0, 256])
+        ax[1].plot(histogram, color=color)
+
+    ax[1].set_title('RGB Histogram')
+    ax[1].set_xlabel('Bins')
+    ax[1].set_ylabel('# of Pixels')
+
+    plt.tight_layout()
+    plt.show()
+
+
+
+
+
+