Supprimer gravure_upic/python/README.md

Reviewed-on: #3

.gitignore

@@ -1,10 +0,0 @@
-# Environments
-venv/
-.venv/
-/pyvenv.cfg
-.python-version
-
-# Media
-media/
-downloaded_images
-downloaded_videos

README.md

@@ -1,15 +1 @@
-# git repo for art num
-
-*the wiki will be updated with more information and usefull snipet. fell free to contribute*
-
-test port ssh 
-
-## how to 
-1. clone this repo to you own computer 
-    ` git clone https://git.erg.school/P039/art_num_2024.git`
-2. check before eatch courses for update 
-    ` git pull ` 
-
-## content 
-
- 
+git repo for Vivien Perrot

arduino/README.md

@@ -1 +0,0 @@
-git repo for art num

bash/README.md

@@ -1 +0,0 @@
-git repo for art num

gravure_upic/README.md

@@ -0,0 +1,4 @@
+
+# Un système UPIC pour un orchestre de gravures
+
+C'est un projet prenant pour source de création des gravures faites avec la technique d'eau forte sur plaque de zinc. Il inclut la transcription visuelle dans le champ sonore fréquentiel dont le mouvement du dessin fixe va altérer le comportement sonore dans le temps.

gravure_upic/python/circular_to_plane/circular_to_plain.py

@@ -0,0 +1,26 @@
+import cv2
+import math
+
+ring = cv2.imread('../circular.png')
+
+size = ring.shape[0]
+outer_radius = size // 2
+inner_radius = 0
+
+unwrapped = cv2.warpPolar(
+    ring,
+    (size, int(size * math.pi)),
+    (outer_radius, outer_radius),
+    outer_radius,
+    flags = 0
+)
+
+unwrapped = cv2.rotate(unwrapped, cv2.ROTATE_90_COUNTERCLOCKWISE)
+
+unwrapped = unwrapped[inner_radius:, :]
+
+cv2.imshow("original", ring)
+cv2.imshow("Unwrapped", unwrapped)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
+cv2.imwrite("output.png", unwrapped)

gravure_upic/python/img_to_freq/img_to_freq.py

@@ -0,0 +1,101 @@
+from tqdm import tqdm
+from scipy.ndimage import uniform_filter1d
+from scipy.io.wavfile import write
+import math, cv2, sys, getopt, wave
+import numpy as np
+
+def unwrap_img(input_file):
+    ring_img = cv2.imread(input_file)
+
+    size = ring_img.shape[0]
+    outer_radius = size // 2
+    inner_radius = 0
+
+    unwrapped_img = cv2.warpPolar(
+        ring_img,
+        (size, int(size * math.pi)),
+        (outer_radius, outer_radius),
+        outer_radius,
+        flags=0
+    )
+
+    rotated_unwrapped = cv2.rotate(unwrapped_img, cv2.ROTATE_90_COUNTERCLOCKWISE)
+    cropped_unwrapped = rotated_unwrapped[inner_radius:, :]
+
+    inverted_unwrapped = cv2.bitwise_not(cropped_unwrapped)
+
+    return inverted_unwrapped
+
+
+def image_to_audio(input_img, out_wav, duration_seconds, sample_rate, vertical_res, amp_threshold):
+    max_freq = 10000
+    min_freq = 50
+
+    duration_seconds = float(duration_seconds)
+
+    # Downsample image vertically to reduce number of frequencies
+    input_img = input_img[::vertical_res]  # Use one row every 20 pixels
+    height, width, _ = input_img.shape
+
+    num_samples = int(sample_rate * duration_seconds)
+    freqs = np.logspace(np.log10(min_freq), np.log10(max_freq), height)[::-1]
+
+    brightness = np.mean(input_img / 255.0, axis=2)
+    amplitudes = np.where(brightness >= 0.1, brightness, 0)
+
+    samples = np.zeros(num_samples, dtype=np.float32)
+    chunk_size = 10000
+
+    for start in tqdm(range(0, num_samples, chunk_size)):
+        end = min(start + chunk_size, num_samples)
+        t = np.linspace(start / sample_rate, end / sample_rate, end - start)
+
+        pixel_xs = (t * width / duration_seconds).astype(int)
+        pixel_xs = np.clip(pixel_xs, 0, width - 1)
+
+        amp_per_sample = amplitudes[:, pixel_xs]
+        amp_per_sample[amp_per_sample < amp_threshold] = 0
+        amp_per_sample = uniform_filter1d(amp_per_sample, size=7, axis=1)  # Smooth
+        
+        phases = 2 * np.pi * freqs[:, None] * t[None, :]
+        active = np.count_nonzero(amp_per_sample, axis=0)
+        chunk = np.sum(amp_per_sample * np.sin(phases), axis=0)
+
+        # Normalize chunk by number of active oscillators to avoid "snow"
+        chunk = np.where(active > 0, chunk / active, 0)
+        samples[start:end] = chunk
+
+    # Final normalization
+    samples /= np.max(np.abs(samples) + 1e-8)
+
+    # Convert to int16 for wav
+    wav_samples = (samples * 32767).astype(np.int16)
+    write(out_wav, sample_rate, wav_samples)
+    print(f"Audio saved to {out_wav}")
+
+
+if __name__ == '__main__':
+    input_file = ''
+    output_file = ''
+    duration = 10
+
+    try:
+        opts, args = getopt.getopt(sys.argv[1:], "hi:o:d:yRes:thresh:")
+    except getopt.GetoptError:
+        print('error: img_to_freq.py -i <input_picture> -o <output_sound> -d <audio_duration>')
+
+    for opt, arg in opts:
+        if opt == '-h':
+            print('error: img_to_freq.py -i <input_picture> -o <output_sound> -d <audio_duration>')
+            sys.exit(2)
+        elif opt == '-i':
+            input_file = arg
+        elif opt == '-o':
+            output_file = arg
+        elif opt == '-d':
+            duration = arg
+
+    unwrapped_img = unwrap_img(input_file)
+    cv2.imwrite('output.png', unwrapped_img)
+
+    image_to_audio(unwrapped_img, output_file, duration, 44100, 20, 0.1)

python/README.md

@@ -1 +0,0 @@
-git repo for art num

python/scrape/README.md

@@ -1,12 +0,0 @@
-## A script that scrapes images from a given URL
-we need to use the requests and BeautifulSoup libraries to retrieve and parse the HTML content. `os` and `shutil` are helpful for managing files and saving the images. Scraping should be done ethically, following the website's robots.txt rules and terms of service.
-
-```
-pip install requests beautifulsoup4 tldextract
-
-```
-
-Run the script with:
-```
-python cyberfeminist_images.py
-```

python/scrape/get_images.py

@@ -1,66 +0,0 @@
-import requests
-import time
-from bs4 import BeautifulSoup
-from urllib.parse import urlparse
-import os
-import sys
-import tldextract
-
-# URL of the webpage with images
-input_url = sys.argv[1] 
-
-# extract full domain
-def split_domain_or_subdomain_and_path(url):
-    # Parse the URL
-    parsed_url = urlparse(url)
-    extracted = tldextract.extract(url)
-    
-    # Build the full domain, including subdomain if present
-    if extracted.subdomain:
-        full_domain = f"{extracted.subdomain}.{extracted.domain}.{extracted.suffix}"
-    else:
-        full_domain = f"{extracted.domain}.{extracted.suffix}"
-    
-    return "https://" + full_domain
-
-full_domain = split_domain_or_subdomain_and_path(input_url)
-print(f"Domain/Subdomain: {full_domain}")
-
-# Folder to save images
-save_folder = "downloaded_images"
-if not os.path.exists(save_folder):
-    os.makedirs(save_folder)
-
-# Send GET request to the page
-response = requests.get(input_url)
-if response.status_code == 200:
-    # Parse the HTML content with BeautifulSoup
-    soup = BeautifulSoup(response.text, 'html.parser')
-
-    # Find all image tags
-    images = soup.find_all('img')
-
-    # Loop through image tags
-    for idx, img in enumerate(images):
-        img_url = img.get('src')
-        
-        # Check if img_url is complete; if not, adjust it accordingly
-        if not img_url.startswith("http"):
-            img_url = full_domain + "/" + img_url
-
-        try:
-            # Send request to the image URL
-            img_data = requests.get(img_url).content
-            # Define file name and path
-            img_name = os.path.join(save_folder, f"image_{idx}.jpg")
-            # Write image data to file
-            with open(img_name, 'wb') as handler:
-                handler.write(img_data)
-
-            print(f"Downloaded {img_name}")
-            time.sleep(1)
-        
-        except Exception as e:
-            print(f"Failed to download {img_url}. Error: {e}")
-else:
-    print("Failed to retrieve the page.")

references/README.md

@@ -1 +0,0 @@
-git repo for art num

upython/README.md

@@ -1 +0,0 @@
-git repo for art num