improve synthetic data generation

Phantom_generate/synthetic_data.py

@@ -21,11 +21,12 @@ generation_number = 100  # number of files to output
 line_thickness = (4,15)*255
 
 line_color = (0.6,0.98)
-
+background_color = (0.1,0.25)
 line_count = 10
 square_count = 15
 circle_count = 15
 
+random_noise = 0.2
 blur_kernel = "custom"
 
 #Options for blur_kernel = "gaussian"
@@ -41,8 +42,9 @@ output_npz = True
 output_single_npz = False # requires lots of memory
 
 interactive = False
-def gen_image(height, width, thickness, color, line_count , square_count):
-    image = np.zeros((height, width), dtype=np.float32)
+def gen_image(height, width, b_color, thickness, color, line_count , square_count):
+    b_color =  random.uniform(b_color[0],b_color[1])
+    image = np.ones((height, width),dtype=np.float32)* b_color
     for i in range(line_count):
         thickness_i = random.randint(thickness[0],thickness[1])
         color_i = random.uniform(color[0],color[1])
@@ -92,8 +94,11 @@ if output_single_npz:
     storage = np.zeros((2,generation_number,output_height,output_width),dtype=np.float64)
 
 for i in range(generation_number):
-    gt = gen_image(output_height, output_width, line_thickness, line_color, line_count,square_count)
+    gt = gen_image(output_height, output_width, background_color,line_thickness, line_color, line_count,square_count)
+    gt = np.clip(gt,a_min=0,a_max=1)
     blured = cv2.filter2D(gt, -1, kernel)
+    blured = blured + np.random.rand(blured.shape[0],blured.shape[1]) * random_noise
+    blured = np.clip(blured,a_min=0,a_max=1)
 
     if interactive:
         show_image(gt, "gt")