add camera parameters

example_load.py

@@ -2,29 +2,18 @@ from datasets import load_dataset
 import matplotlib.pyplot as plt
 import numpy as np
 from scipy import stats
-# similar to cityscapes for mmsegmentation
-# class name, (new_id, img_id)
-semantic_map = {
-                "bacterial_spot":       (0, 5),
-                "early_blight":         (1, 10),
-                "late_blight":          (2, 20),
-                "leaf_mold":            (3, 25),
-                "septoria_leaf_spot":   (4,30),
-                "spider_mites":         (5,35),
-                "target_spot":          (6,40),
-                "mosaic_virus":         (7,45),
-                "yellow_leaf_curl_virus":(8,50),
-                "healthy_leaf_pv":      (9, 15),  # plant village healthy leaf
-                "healthy_leaf_t":       (9, 255), # texture leaf (healthy)
-                "background":           (10, 0),
-                "tomato":               (11, 121),
-                "stem":                 (12, 111),
-                "wood_rod":             (13, 101),
-                "red_band":             (14, 140),
-                "yellow_flower":        (15, 131)
-                }
 
-def maj_vote(img,x,y,n=3):
+# Additional Information for Depth and Camera Parameters
+#
+# Creating intrinsics for the camera
+#   fov = 95.452621 # degrees
+#   fx = (2448 / np.tan((fov*np.pi/180.0)/2.0)) / 2
+#   intrinsics = o3d.camera.PinholeCameraIntrinsic(2448,2048,fx,fx,2448/2,2048/2)
+# baseline = 3.88112 # cm
+# Note: Depth is also in centimeters
+#
+
+def maj_vote(img,x,y,semantic_map,n=3):
     half = n // 2
     x_min, x_max = max(0, x - half), min(img.shape[1], x + half + 1)
     y_min, y_max = max(0, y - half), min(img.shape[0], y + half + 1)
@@ -39,7 +28,7 @@ def maj_vote(img,x,y,n=3):
     else:
         return semantic_map["background"][0]
     
-def color_to_id(img_semantic, top_k_disease = 10, semantic_map = semantic_map):
+def color_to_id(img_semantic,  semantic_map, top_k_disease = 10):
     semantic_id_img = np.ones(img_semantic.shape) * 255
     disease_counts = []
     # remap rendered color to semantic id
@@ -57,32 +46,53 @@ def color_to_id(img_semantic, top_k_disease = 10, semantic_map = semantic_map):
     # Apply majority voting for unlabeled pixels (needed as the rendering process can blend pixels)
     unknown_mask = (semantic_id_img == 255)
     for y,x in np.argwhere(unknown_mask):
-        semantic_id_img[y, x] = maj_vote(semantic_id_img, x, y, 3)
+        semantic_id_img[y, x] = maj_vote(semantic_id_img, x, y, semantic_map, 3)
     return semantic_id_img
 
-
-dataset = load_dataset("xingjianli/tomatotest", 'sample',trust_remote_code=True, num_proc=4)
-print(dataset["train"][0])
+if __name__ == "__main__":
+    # similar to cityscapes for mmsegmentation
+    # class name, (new_id, img_id)
+    semantic_map =  {
+        "bacterial_spot":       (0, 5),
+        "early_blight":         (1, 10),
+        "late_blight":          (2, 20),
+        "leaf_mold":            (3, 25),
+        "septoria_leaf_spot":   (4,30),
+        "spider_mites":         (5,35),
+        "target_spot":          (6,40),
+        "mosaic_virus":         (7,45),
+        "yellow_leaf_curl_virus":(8,50),
+        "healthy_leaf_pv":      (9, 15),  # plant village healthy leaf
+        "healthy_leaf_t":       (9, 255), # texture leaf (healthy)
+        "background":           (10, 0),
+        "tomato":               (11, 121),
+        "stem":                 (12, 111),
+        "wood_rod":             (13, 101),
+        "red_band":             (14, 140),
+        "yellow_flower":        (15, 131)
+                    }
+    dataset = load_dataset("xingjianli/tomatotest", 'sample',trust_remote_code=True, num_proc=4)
+    print(dataset["train"][0])
 
 
-left_rgb_img = dataset["train"][0]['left_rgb']
-right_rgb_img = dataset["train"][0]['right_rgb']
-left_semantic_img = np.asarray(dataset["train"][0]['left_semantic'])
-left_instance_img = np.asarray(dataset["train"][0]['left_instance'])
-left_depth_img = np.asarray(dataset["train"][0]['left_depth'])
-right_depth_img = np.asarray(dataset["train"][0]['right_depth'])
-plt.subplot(231)
-plt.imshow(left_rgb_img)
-plt.subplot(232)
-plt.imshow(right_rgb_img)
-plt.subplot(233)
-plt.imshow(color_to_id(left_semantic_img))
-plt.subplot(234)
-plt.imshow(np.where(left_depth_img>500,0,left_depth_img))
-plt.subplot(235)
-plt.imshow(np.where(right_depth_img>500,0,right_depth_img))
-plt.subplot(236)
-plt.imshow(left_instance_img)
-plt.show()
+    left_rgb_img = dataset["train"][0]['left_rgb']
+    right_rgb_img = dataset["train"][0]['right_rgb']
+    left_semantic_img = np.asarray(dataset["train"][0]['left_semantic'])
+    left_instance_img = np.asarray(dataset["train"][0]['left_instance'])
+    left_depth_img = np.asarray(dataset["train"][0]['left_depth'])
+    right_depth_img = np.asarray(dataset["train"][0]['right_depth'])
+    plt.subplot(231)
+    plt.imshow(left_rgb_img)
+    plt.subplot(232)
+    plt.imshow(right_rgb_img)
+    plt.subplot(233)
+    plt.imshow(color_to_id(left_semantic_img, semantic_map))
+    plt.subplot(234)
+    plt.imshow(np.where(left_depth_img>500,0,left_depth_img))
+    plt.subplot(235)
+    plt.imshow(np.where(right_depth_img>500,0,right_depth_img))
+    plt.subplot(236)
+    plt.imshow(left_instance_img)
+    plt.show()