adding the model and dataset

color_analysis/processed_images/make_dataset.py

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+import requests 
+from bs4 import BeautifulSoup
+import pandas as pd
+import subprocess
+import tempfile
+import os
+
+
+
+base_url = 'https://www.spicemarketcolour.com.au/'
+seasons_links_dict = {'spring':['bright-spring-celebrities','true-spring-celebrities','light-spring-celebrities'], 'summer':['light-summer-celebrities','true-summer-celebrities','soft-summer-celebrities'], 'autumn':['soft-autumn-celebrities','true-autumn-celebrities','dark-autumn-celebrities'], 'winter':['dark-winter-celebrities','true-winter-celebrities','bright-winter-celebrities']}
+
+
+headers = {'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.11 (KHTML, like Gecko) Chrome/23.0.1271.64 Safari/537.11',
+      'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
+      'Accept-Charset': 'ISO-8859-1,utf-8;q=0.7,*;q=0.3',
+      'Accept-Encoding': 'none',
+      'Accept-Language': 'en-US,en;q=0.8',
+      'Connection': 'keep-alive'}
+
+request = requests.get(base_url, headers=headers)
+
+
+seasonal_images = {}
+seasons = ['spring','winter','summer','autumn']
+
+# #  extract all img tags with class img
+# for s in seasons:
+#     s_links = []
+#     for url in seasons_links_dict[s]:
+#         request = requests.get(base_url+url, headers=headers)
+#         soup = BeautifulSoup(request.content, 'html.parser')
+#         images = soup.find_all('img', attrs={'class':'thumb-image'})
+#         links = [listing['data-src'] for listing in images]
+#         s_links.extend(links)
+#     seasonal_images[s] = s_links
+
+# for s in seasonal_images.keys():
+#     for i in range(len(seasonal_images[s])):
+#         print(f"Processing image {i+1} / {len(seasonal_images[s])}")
+#         try:
+#             # Download image from URL
+#             r = requests.get(seasonal_images[s][i], stream=True)
+#             r.raise_for_status()  # Raise an exception for HTTP errors
+
+#             # Save the image locally
+#             local_path = filename.format(s, i)
+#             with open(local_path, "wb") as f:
+#                 f.write(r.content)
+
+#             # Update the image path in seasonal_images
+#             seasonal_images[s][i] = local_path
+#         except requests.exceptions.RequestException as e:
+#             print(f"Error downloading {seasonal_images[s][i]}: {e}")
+#         except Exception as e:
+#             print(f"An error occurred with image {seasonal_images[s][i]}: {e}")
+
+processed_images = {}
+script_path = "./preprocess.sh"
+# Directory to save processed images
+
+for s in seasons: 
+    input_folder = f"../../combined-demo/pics/{s}"
+    output_folder = f"../../combined-demo/processed_images/{s}"
+    for image_name in os.listdir(input_folder):
+        print(image_name)
+        input_image_path = os.path.join(input_folder, image_name)
+        output_image_path = os.path.join(output_folder, image_name)
+        try:
+            subprocess.run(["bash", script_path, input_image_path,output_image_path], check=True)
+            print(f"Processed {image_name} successfully.")
+        except subprocess.CalledProcessError as e:
+            print(f"Failed to process {image_name}: {e}")    
+
+
+

color_analysis/processed_images/preprocess.sh

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+#!/bin/bash
+
+# # Check if an argument is provided
+# if [ "$#" -ne 1  ]; then
+#     echo "Usage: $0 <image_file>"
+#     exit 1
+# fi
+
+# Assign the argument to a variable
+
+input_image=$1
+output_path=$2
+# output_path2=$3
+
+# Intermediate directory for temporary files
+intermediate_dir="../processed_images"
+mkdir -p "$intermediate_dir"  # Create intermediate directory if it doesn't exist
+
+python3 ../../color-correction/PyTorch/demo_single_image.py --i "$input_image" -t AWB  -o "../../combined-demo/processed_images"
+
+mv "processed_images/temp.png" "$output_path"
+
+# Run facial detection
+python3 ../../face-detect/facedec/facedetect.py --i  "$output_path"
+
+mv "../output-imgs/cropped.jpg" "$output_path"