CARIA.2.1

Update for the final presentation
huge change with previous version

server-trainer/dataset_generation/dataset_params_edition.py

@@ -0,0 +1,92 @@
+import numpy as np
+import cv2
+import random
+import multiprocessing
+from multiprocessing import Pool
+
+def bruit(image_orig):
+    h, w, c = image_orig.shape
+    n = np.random.randn(h, w, c) * random.randint(5, 30)
+    return np.clip(image_orig + n, 0, 255).astype(np.uint8)
+
+def change_gamma(image, alpha=1.0, beta=0.0):
+    return np.clip(alpha * image + beta, 0, 255).astype(np.uint8)
+    
+def modif_img(img):
+    h, w, c = img.shape
+
+    r_color = [np.random.randint(255), np.random.randint(255), np.random.randint(255)]
+    img = np.where(img == [142, 142, 142], r_color, img).astype(np.uint8)
+
+    if np.random.randint(3):
+        k_max = 3
+        kernel_blur = np.random.randint(k_max) * 2 + 1
+        img = cv2.GaussianBlur(img, (kernel_blur, kernel_blur), 0)
+
+    M = cv2.getRotationMatrix2D((int(w / 2), int(h / 2)), random.randint(-10, 10), 1)
+    img = cv2.warpAffine(img, M, (w, h))
+        
+    if np.random.randint(2):
+        a = int(max(w, h) / 5) + 1
+        pts1 = np.float32([[0, 0], [w, 0], [0, h], [w, h]])
+        pts2 = np.float32([[0 + random.randint(-a, a), 0 + random.randint(-a, a)], 
+                           [w - random.randint(-a, a), 0 + random.randint(-a, a)], 
+                           [0 + random.randint(-a, a), h - random.randint(-a, a)], 
+                           [w - random.randint(-a, a), h - random.randint(-a, a)]])        
+        M = cv2.getPerspectiveTransform(pts1,pts2)
+        img = cv2.warpPerspective(img, M, (w, h))
+        
+    if np.random.randint(2):
+        r = random.randint(0, 5)
+        h2 = int(h * 0.9)
+        w2 = int(w * 0.9)
+        if r == 0:
+            img = img[0:w2, 0:h2]
+        elif r == 1:
+            img = img[w - w2:w, 0:h2]
+        elif r == 2:
+            img = img[0:w2, h - h2:h]
+        elif r == 3:
+            img = img[w - w2:w, h - h2:h]
+        img = cv2.resize(img, (h, w))
+
+    if np.random.randint(2):
+        r = random.randint(1, int(max(w, h) * 0.15))
+        img = img[r:w - r, r:h - r]
+        img = cv2.resize(img, (h, w))
+
+    if not np.random.randint(4):
+        t = np.empty((h, w, c), dtype=np.float32)
+        for i in range(h):
+            for j in range(w):
+                for k in range(c):
+                    t[i][j][k] = (i / h)
+        M = cv2.getRotationMatrix2D((int(w / 2), int(h / 2)), np.random.randint(4) * 90, 1)
+        t = cv2.warpAffine(t, M, (w, h))
+        img = (cv2.multiply((img / 255).astype(np.float32), t) * 255).astype(np.uint8)
+
+    img = change_gamma(img, random.uniform(0.6, 1.0), -np.random.randint(50))
+
+    if not np.random.randint(4):
+        p = (15 + np.random.randint(10)) / 100
+        img = (img * p + 50 * (1 - p)).astype(np.uint8) + np.random.randint(100)
+
+    img = bruit(img)
+        
+    return img
+
+def create_lot_img(image, nbr, nbr_thread=None):
+    if nbr_thread is None:
+        nbr_thread = multiprocessing.cpu_count()
+
+    # Convert the image to a bytes-like object to avoid issues with multiprocessing
+    image_bytes = np.array(image, dtype=np.uint8).tobytes()
+    
+    def modif_img_from_bytes(image_bytes):
+        img = np.frombuffer(image_bytes, dtype=np.uint8).reshape(image.shape)
+        return modif_img(img)
+
+    with Pool(nbr_thread) as p:
+        lot_result = p.map(modif_img_from_bytes, [image_bytes] * nbr)
+    
+    return lot_result