186 lines
5.8 KiB
Python
186 lines
5.8 KiB
Python
import colorsys
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import plotly.graph_objects as go
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from PIL import Image
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import numpy as np
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from matplotlib import pyplot as plt
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import time
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def apply_convolution_operator(operator, image):
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if isinstance(image, str):
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image = Image.open(image)
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if isinstance(image, Image.Image):
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x_pixel, y_pixel = image.size
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# turn into editable list of colors
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data = np.asarray(image, dtype=np.uint8)
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# storage for the convoluted image
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data_new = np.zeros([y_pixel, x_pixel, 3], dtype=np.uint8)
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for i in range(1, y_pixel - 1):
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for j in range(1, x_pixel - 1):
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for k in range(3):
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new_value = (
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operator[0, 0] * data[i - 1, j - 1, k] +
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operator[0, 1] * data[i - 1, j, k] +
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operator[0, 2] * data[i - 1, j + 1, k] +
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operator[1, 0] * data[i, j - 1, k] +
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operator[1, 1] * data[i, j, k] +
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operator[1, 2] * data[i, j + 1, k] +
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operator[2, 0] * data[i + 1, j - 1, k] +
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operator[2, 1] * data[i + 1, j, k] +
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operator[2, 2] * data[i + 1, j + 1, k]
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)
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new_value = max(0, min(new_value, 255))
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data_new[i, j, k] = new_value
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return Image.fromarray(data_new)
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else:
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raise TypeError("Not a valid instance of Image.Image")
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g1 = np.array([
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[0, 1, 0],
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[0, 0, 0],
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[0, -1, 0]
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])
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g2 = np.array([
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[1, 0, 0],
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[0, 0, 0],
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[0, 0, -1]
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])
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g3 = np.array([
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[0, 0, 0],
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[1, 0, -1],
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[0, 0, 0]
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])
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g4 = np.array([
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[0, 0, 1],
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[0, 0, 0],
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[-1, 0, 0]
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])
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identity = np.array([
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[0, 0, 0],
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[0, 1, 0],
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[0, 0, 0]
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])
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laplace = np.array([
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[0, 1, 0],
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[1, -4, 1],
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[0, 1, 0]
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])
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"""
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url = "https://cdn.prod.www.spiegel.de/images/9f9be1b3-ac0e-49f9-acbd-a809a102f9b6_w960_r1.778_fpx39_fpy30.jpg"
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response = requests.get(url)
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if response.status_code == 200:
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# image = Image.open(BytesIO(response.content))
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image = Image.open("next_generation.jpg")
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operators = [laplace]
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edited_image = image
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for operator in operators:
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edited_image = apply_convolution_operator(operator, edited_image)
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edited_image = edited_image.convert("RGB") # Convert to RGB mode
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edited_image.show()
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edited_image.save("edited.jpg")
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else:
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print(f"Failed to download image. Status code: {response.status_code}")
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"""
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class Heatmap_Simulator:
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k = 1
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h = 2
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delta_f = 0.005
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cookie_size = 0.5
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def __init__(self, y_size=28, x_size=28, tmp_min=0, tmp_max=1):
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init_heatmap = np.zeros((y_size, x_size))
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self.x_size = x_size
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self.y_size = y_size
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self.tmp_min = tmp_min
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self.tmp_max = tmp_max
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size = self.cookie_size / 2
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for y in range(int(y_size * (0.5 - size)), int(y_size * (0.5 + size))):
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for x in range(int(x_size * (0.5 - size)), int(x_size * (0.5 + size))):
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init_heatmap[y][x] = 1
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self.heatmap = init_heatmap
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def simulate(self, cycles=1):
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for c in range(cycles):
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new_heatmap = np.zeros((self.y_size, self.x_size))
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for y in range(self.y_size):
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for x in range(self.x_size):
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u = self.heatmap[y, x]
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value = u + self.delta_f * (self.k / (1 ** 2)) * self._consider_convolution(x, y)
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value = max(self.tmp_min, min(self.tmp_max, value))
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new_heatmap[y][x] = value
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self.heatmap = new_heatmap
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def _consider_convolution(self, x, y):
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top = self.heatmap[y - 1, x] if y > 0 else 0
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bottom = self.heatmap[y + 1, x] if y < self.heatmap.shape[0] - 1 else 0
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left = self.heatmap[y, x - 1] if x > 0 else 0
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right = self.heatmap[y, x + 1] if x < self.heatmap.shape[1] - 1 else 0
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center = self.heatmap[y, x]
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return top + bottom + left + right + center * (-4)
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def export(self):
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if self.heatmap is not None:
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return Heatmap_Image(self.heatmap, 0, 1)
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else:
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AttributeError("Heatmap is not initialized.")
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class Visual_Heatmap_Simulator(Heatmap_Simulator):
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def __init__(self, y_size=28, x_size=28, tmp_min=0, tmp_max=1, delay=0.5):
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super().__init__(y_size, x_size, tmp_min, tmp_max)
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self.delay = delay
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def simulate_visual(self, cycles=100, skip=5):
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for c in range(cycles):
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if c != 0:
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self.simulate(1)
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if c % skip == 0:
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self._update_plot()
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print(f"Figure updated: {c}.")
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def _update_plot(self):
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plt.imshow(self.heatmap, cmap='viridis', interpolation='nearest')
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plt.draw()
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plt.pause(self.delay)
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simulator = Visual_Heatmap_Simulator(28, 28, -0.5, 1)
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simulator.simulate_visual(10000, 50)
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class Heatmap_Image:
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def __init__(self, heatmap, min_val, max_val):
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self.min_val = min_val
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self.max_val = max_val
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x_size, y_size = heatmap.shape
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colormap = np.zeros((y_size, x_size, 3), dtype=np.uint8)
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for y in range(y_size):
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for x in range(x_size):
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colormap[y][x] = self._val_to_color(heatmap[y][x])
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self.image = Image.fromarray(colormap, 'RGB')
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def _val_to_color(self, value):
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normalized_value = (value - self.min_val) / (self.max_val - self.min_val)
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hsv_color = (0.66 - 0.66 * normalized_value, 1.0, 1.0) # Blue to Red
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rgb_color = colorsys.hsv_to_rgb(*hsv_color)
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scaled_rgb_color = tuple(int(c * 255) for c in rgb_color)
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return scaled_rgb_color
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def show(self):
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self.image.show()
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def save(self, file):
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self.image.save(file)
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