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import os
import cv2
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
import exifread
import pandas as pd
from IPython.display import display_html
import locale
from matplotlib.colors import ListedColormap, LinearSegmentedColormap
locale.setlocale(locale.LC_ALL, '')
# Specify the path to the folder containing CR2 images
folder_path = "Box/RAW - 2023 Personal Photos/Singapore July 2023"
# Initialize histograms for hue, saturation, and brightness
hue_hist = np.zeros(180, dtype=int)
saturation_hist = np.zeros(256, dtype=int)
brightness_hist = np.zeros(256, dtype=int)
# Iterate through all CR2 images in the folder
for filename in os.listdir(folder_path):
if filename.endswith(".CR2"):
try:
# Open the image using PIL for proper CR2 support
image = Image.open(os.path.join(folder_path, filename))
# Convert to RGB color space if needed
if image.mode != "RGB":
image = image.convert("RGB")
# Convert to NumPy array for processing
image_array = np.array(image)
# Convert to HSV color space
hsv_image = cv2.cvtColor(image_array, cv2.COLOR_RGB2HSV)
# Separate hue, saturation, and brightness channels
hue, saturation, brightness = cv2.split(hsv_image)
# Flatten the channels for easier histogram calculation
hue_flat = hue.flatten()
saturation_flat = saturation.flatten()
brightness_flat = brightness.flatten()
# Update the histograms based on pixel counts
hue_hist += np.bincount(hue_flat, minlength=180)
saturation_hist += np.bincount(saturation_flat, minlength=256)
brightness_hist += np.bincount(brightness_flat, minlength=256)
except Exception as e:
print(f"Error processing {filename}: {e}")
# Create the hue histogram with color-mapped bins
plt.figure(figsize=(20, 10))
plt.title("Histogram of Hues of tremolo_truong's Singapore July 2023 Shoot")
plt.xlabel("Hue Value")
plt.ylabel("Pixel Count")
plt.xlim(0,180)
plt.bar(np.arange(180), hue_hist, color=plt.cm.hsv(np.arange(180) / 180))
plt.show()
purple = plt.get_cmap('Purples')
gray = plt.get_cmap('gray')
purples = purple(np.linspace(0,1,256))
grays = gray(np.linspace(0,1,256))
# Create the saturation histogram with custom colormap
plt.figure(figsize=(20, 10))
plt.title("Histogram of Saturations of tremolo_truong's Singapore July 2023 Shoot")
plt.xlabel("Saturation Value")
plt.ylabel("Pixel Count")
plt.xlim(0,255)
plt.bar(np.arange(256), saturation_hist, color=purples)
plt.show()
# Create the brightness histogram with custom colormap
plt.figure(figsize=(20, 10))
plt.title("Histogram of Brightnesses of tremolo_truong's Singapore July 2023 Shoot")
plt.xlabel("Brightness Value")
plt.ylabel("Pixel Count")
plt.xlim(0,255)
plt.bar(np.arange(256), brightness_hist, color=grays)
plt.show()
# Specify the path to the folder containing CR2 images
folder_path_1 = "Box/RAW - 2023 Personal Photos/Downtown Austin Sept 2023"
# Initialize histograms for hue, saturation, and brightness
hue_hist_1 = np.zeros(180, dtype=int)
saturation_hist_1 = np.zeros(256, dtype=int)
brightness_hist_1 = np.zeros(256, dtype=int)
# Iterate through all CR2 images in the folder
for filename in os.listdir(folder_path_1):
if filename.endswith(".CR2"):
try:
# Open the image using PIL for proper CR2 support
image = Image.open(os.path.join(folder_path_1, filename))
# Convert to RGB color space if needed
if image.mode != "RGB":
image = image.convert("RGB")
# Convert to NumPy array for processing
image_array = np.array(image)
# Convert to HSV color space
hsv_image = cv2.cvtColor(image_array, cv2.COLOR_RGB2HSV)
# Separate hue, saturation, and brightness channels
hue, saturation, brightness = cv2.split(hsv_image)
# Flatten the channels for easier histogram calculation
hue_flat = hue.flatten()
saturation_flat = saturation.flatten()
brightness_flat = brightness.flatten()
# Update the histograms based on pixel counts
hue_hist_1 += np.bincount(hue_flat, minlength=180)
saturation_hist_1 += np.bincount(saturation_flat, minlength=256)
brightness_hist_1 += np.bincount(brightness_flat, minlength=256)
except Exception as e:
print(f"Error processing {filename}: {e}")
# Create the hue histogram with color-mapped bins
plt.figure(figsize=(20, 10))
plt.title("Histogram of Hues of tremolo_truong's Austin September 2023 Shoot")
plt.xlabel("Hue Value")
plt.ylabel("Pixel Count")
plt.xlim(0,180)
plt.bar(np.arange(180), hue_hist_1, color=plt.cm.hsv(np.arange(180) / 180))
plt.show()
# The saturation and brightness colormaps are already declared above, so are available for use in this chunk as well
# Create the saturation histogram with custom colormap
plt.figure(figsize=(20, 10))
plt.title("Histogram of Saturations of tremolo_truong's Austin September 2023 Shoot")
plt.xlabel("Saturation Value")
plt.ylabel("Pixel Count")
plt.xlim(0,255)
plt.bar(np.arange(256), saturation_hist_1, color=purples)
plt.show()
# Create the brightness histogram with custom colormap
plt.figure(figsize=(20, 10))
plt.title("Histogram of Brightnesses of tremolo_truong's Austin September 2023 Shoot")
plt.xlabel("Brightness Value")
plt.ylabel("Pixel Count")
plt.xlim(0,255)
plt.bar(np.arange(256), brightness_hist_1, color=grays)
plt.show()
