fixed normalization in demo 03

Author: Erol444

examples/03_depth_preview.py

@@ -4,6 +4,24 @@
 import depthai as dai
 import numpy as np
 
+
+'''
+If one or more of the additional depth modes (lrcheck, extended, subpixel)
+are enabled, then:
+ - depth output is FP16. TODO enable U16.
+ - median filtering is disabled on device. TODO enable.
+ - with subpixel, either depth or disparity has valid data.
+Otherwise, depth output is U16 (mm) and median is functional.
+But like on Gen1, either depth or disparity has valid data. TODO enable both.
+'''
+
+# Closer-in minimum depth, disparity range is doubled (from 95 to 190):
+extended_disparity = False
+# Better accuracy for longer distance, fractional disparity 32-levels:
+subpixel = False
+# Better handling for occlusions:
+lr_check = False
+
 # Start defining a pipeline
 pipeline = dai.Pipeline()
 
@@ -19,25 +37,25 @@
 # Create a node that will produce the depth map (using disparity output as it's easier to visualize depth this way)
 depth = pipeline.createStereoDepth()
 depth.setConfidenceThreshold(200)
+depth.setOutputDepth(False)
 # Options: MEDIAN_OFF, KERNEL_3x3, KERNEL_5x5, KERNEL_7x7 (default)
 median = dai.StereoDepthProperties.MedianFilter.KERNEL_7x7 # For depth filtering
 depth.setMedianFilter(median)
 
-'''
-If one or more of the additional depth modes (lrcheck, extended, subpixel)
-are enabled, then:
- - depth output is FP16. TODO enable U16.
- - median filtering is disabled on device. TODO enable.
- - with subpixel, either depth or disparity has valid data.
-Otherwise, depth output is U16 (mm) and median is functional.
-But like on Gen1, either depth or disparity has valid data. TODO enable both.
-'''
-# Better handling for occlusions:
-depth.setLeftRightCheck(False)
-# Closer-in minimum depth, disparity range is doubled:
-depth.setExtendedDisparity(False)
-# Better accuracy for longer distance, fractional disparity 32-levels:
-depth.setSubpixel(False)
+depth.setLeftRightCheck(lr_check)
+
+# Normal disparity values range from 0..95, will be used for normalization
+max_disparity = 95
+
+if extended_disparity: max_disparity *= 2 # Double the range
+depth.setExtendedDisparity(extended_disparity)
+
+if subpixel: max_disparity *= 32 # 5 fractional bits, x32
+depth.setSubpixel(subpixel)
+
+# When we get disparity to the host, we will multiply all values with the multiplier
+# for better visualization
+multiplier = 255 / max_disparity
 
 left.out.link(depth.left)
 right.out.link(depth.right)
@@ -54,12 +72,10 @@
 
     # Output queue will be used to get the disparity frames from the outputs defined above
     q = device.getOutputQueue(name="disparity", maxSize=4, blocking=False)
-
     while True:
         inDepth = q.get()  # blocking call, will wait until a new data has arrived
         frame = inDepth.getFrame()
-        frame = cv2.normalize(frame, None, 0, 255, cv2.NORM_MINMAX)
-
+        frame = (frame*multiplier).astype(np.uint8)
         # Available color maps: https://docs.opencv.org/3.4/d3/d50/group__imgproc__colormap.html
         frame = cv2.applyColorMap(frame, cv2.COLORMAP_JET)