(5.x) Merge 4.x

testdata/cv/rgbd/warped_depth_generator/requirements.txt

@@ -0,0 +1,3 @@
+numpy>=1.17.4
+scipy>=1.3.3
+PIL>=7.0.0

testdata/cv/rgbd/warped_depth_generator/warp_test.py

@@ -0,0 +1,111 @@
+import numpy as np
+from scipy.spatial.transform import Rotation
+from PIL import Image
+import os
+
+depthFactor = 5000
+psize = (640, 480)
+fx = 525.0
+fy = 525.0
+cx = psize[0]/2-0.5
+cy = psize[1]/2-0.5
+K = np.array([[fx,  0, cx],
+              [ 0, fy, cy],
+              [ 0,  0,  1]])
+
+# some random transform
+rmat = Rotation.from_rotvec(np.array([0.1, 0.2, 0.3])).as_dcm()
+tmat = np.array([[-0.04, 0.05, 0.6]]).T
+rtmat = np.vstack((np.hstack((rmat, tmat)), np.array([[0, 0, 0, 1]])))
+
+testDataPath = os.getenv("OPENCV_TEST_DATA_PATH", default=None)
+srcDepth = np.asarray(Image.open(testDataPath + "/cv/rgbd/depth.png"))
+srcRgb   = np.asarray(Image.open(testDataPath + "/cv/rgbd/rgb.png"))
+
+def reproject(image, df, K):
+    Kinv = np.linalg.inv(K)
+    xsz, ysz = image.shape[1], image.shape[0]
+    reprojected = np.zeros((ysz, xsz, 3))
+    for y in range(ysz):
+        for x in range(xsz):
+            z = image[y, x]/df
+
+            v = Kinv @ np.array([x*z, y*z, z]).T
+
+            #xv = (x - cx)/fx * z
+            #yv = (y - cy)/fy * z
+            #zv = z
+
+            reprojected[y, x, :] = v[:]
+    return reprojected
+
+def reprojectRtProject(image, K, depthFactor, rmat, tmat):
+    Kinv = np.linalg.inv(K)
+    xsz, ysz = image.shape[1], image.shape[0]
+    projected = np.zeros((ysz, xsz, 3))
+    for y in range(ysz):
+        for x in range(xsz):
+            z = image[y, x]/depthFactor
+
+            v = K @ (rmat @ Kinv @ np.array([x*z, y*z, z]).T + tmat[:, 0])
+
+            if z > 0:
+                projected[y, x, :] = v[:]
+
+    return projected
+
+def reprojectRt(image, K, depthFactor, rmat, tmat):
+    Kinv = np.linalg.inv(K)
+    xsz, ysz = image.shape[1], image.shape[0]
+    rotated = np.zeros((ysz, xsz, 3))
+    for y in range(ysz):
+        for x in range(xsz):
+            z = image[y, x]/depthFactor
+
+            v = rmat @ Kinv @ np.array([x*z, y*z, z]).T + tmat[:, 0]
+
+            rotated[y, x, :] = v[:]
+
+    return rotated
+
+# put projected points on a depth map
+def splat(projected, maxv, rgb):
+    xsz, ysz = projected.shape[1], projected.shape[0]
+    depth = np.full((ysz, xsz), maxv, np.float32)
+    colors = np.full((ysz, xsz, 3), 0, np.uint8)
+    for y in range(ysz):
+        for x in range(xsz):
+            p = projected[y, x, :]
+            z = p[2]
+            if z > 0:
+                u, v = int(p[0]/z), int(p[1]/z)
+                okuv = (u >= 0 and v >= 0 and u < xsz and v < ysz)
+                if okuv and depth[v, u] > z:
+                    depth[v, u] = z
+                    colors[v, u, :] = rgb[y, x, :]
+    return depth, colors
+
+maxv = depthFactor
+dstDepth, dstRgb = splat(reprojectRtProject(srcDepth, K, depthFactor, rmat, tmat), maxv, srcRgb)
+dstDepth[dstDepth >= maxv] = 0
+dstDepth = (dstDepth*depthFactor).astype(np.uint16)
+
+Image.fromarray(dstDepth).save(testDataPath + "/cv/rgbd/warpedDepth.png")
+Image.fromarray(dstRgb  ).save(testDataPath + "/cv/rgbd/warpedRgb.png")
+
+# debug
+def outFile(path, ptsimg):
+    f = open(path, "w")
+    for y in range(ptsimg.shape[0]):
+        for x in range(ptsimg.shape[1]):
+            v = ptsimg[y, x, :]
+            if v[2] > 0:
+                f.write(f"v {v[0]} {v[1]} {v[2]}\n")
+    f.close()
+
+outObj = False
+if outObj:
+    objdir = "/path/to/objdir/"
+    outFile(objdir + "reproj_rot_proj.obj", reproject(dstDepth, depthFactor, K))
+    outFile(objdir + "rotated.obj", reprojectRt(srcDepth, K, depthFactor, rmat, tmat))
+

testdata/dnn/download_models.py

@@ -1,4 +1,4 @@
-#!/usr/bin/env python3
+#!/usr/bin/env python
 
 from __future__ import print_function
 import hashlib
@@ -252,11 +252,6 @@ def is_archive(self):
         url='https://github.com/petewarden/tf_ios_makefile_example/raw/master/data/tensorflow_inception_graph.pb',
         sha='c8a5a000ee8d8dd75886f152a50a9c5b53d726a5',
         filename='tensorflow_inception_graph.pb'),
-    Model(
-        name='Enet',  # https://github.com/e-lab/ENet-training
-        url='https://www.dropbox.com/s/tdde0mawbi5dugq/Enet-model-best.net?dl=1',
-        sha='b4123a73bf464b9ebe9cfc4ab9c2d5c72b161315',
-        filename='Enet-model-best.net'),
     Model(
         name='Fcn',
         url='http://dl.caffe.berkeleyvision.org/fcn8s-heavy-pascal.caffemodel',
@@ -290,11 +285,6 @@ def is_archive(self):
         url='https://raw.githubusercontent.com/chuanqi305/MobileNet-SSD/97406996b1eee2d40eb0a00ae567cf41e23369f9/deploy.prototxt',
         sha='50cf80235a8fcccc641bf9f8efc803edbf21c615',
         filename='MobileNetSSD_deploy_19e3ec3.prototxt'),
-    Model(
-        name='OpenFace',  # https://github.com/cmusatyalab/openface
-        url='https://storage.cmusatyalab.org/openface-models/nn4.small2.v1.t7',
-        sha='ac8161a4376fb5a79ceec55d85bbb57ef81da9fe',
-        filename='openface_nn4.small2.v1.t7'),
     Model(
         name='YoloV2voc',  # https://pjreddie.com/darknet/yolo/
         url='https://pjreddie.com/media/files/yolo-voc.weights',
@@ -316,21 +306,12 @@ def is_archive(self):
         sha='4922099342af5993d9d09f63081c8a392f3c1cc6',
         filename='DenseNet_121.prototxt'),
     Model(
-        name='Fast-Neural-Style (starry night)',
-        url=[
-            'https://cs.stanford.edu/people/jcjohns/fast-neural-style/models/eccv16/starry_night.t7',
-            'https://dl.opencv.org/models/fast_neural_style_eccv16_starry_night.t7'
-        ],
-        sha='5b5e115253197b84d6c6ece1dafe6c15d7105ca6',
-        filename='fast_neural_style_eccv16_starry_night.t7'),
-    Model(
-        name='Fast-Neural-Style (feathers)',
+        name='Fast-Neural-Style',
         url=[
-            'https://cs.stanford.edu/people/jcjohns/fast-neural-style/models/instance_norm/feathers.t7',
-            'https://dl.opencv.org/models/fast_neural_style_instance_norm_feathers.t7'
+            'https://media.githubusercontent.com/media/onnx/models/main/vision/style_transfer/fast_neural_style/model/mosaic-9.onnx',
         ],
-        sha='9838007df750d483b5b5e90b92d76e8ada5a31c0',
-        filename='fast_neural_style_instance_norm_feathers.t7'),
+        sha='dc3ef19f7f8a5d906c97c84cd4dd2732bb73960a',
+        filename='mosaic-9.onnx'),
     Model(
         name='MobileNet-SSD (TensorFlow)',
         url='http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_11_06_2017.tar.gz',
@@ -601,10 +582,7 @@ def is_archive(self):
         ]),
     Model(
         name='ResNet-18v1 (ONNX)',
-        url=[
-            'https://github.com/onnx/models/raw/69c5d3751dda5349fd3fc53f525395d180420c07/vision/classification/resnet/model/resnet18-v1-7.tar.gz',
-            'https://dl.opencv.org/models/resnet18v1.tar.gz'
-        ],
+        url='https://github.com/onnx/models/raw/69c5d3751dda5349fd3fc53f525395d180420c07/vision/classification/resnet/model/resnet18-v1-7.tar.gz',
         sha='d132be4857d024de9caa21fd5300dee7c063bc35',
         filename='resnet18v1.tar.gz',
         sub=[
@@ -623,10 +601,7 @@ def is_archive(self):
         ]),
     Model(
         name='ResNet-50v1 (ONNX)',
-        url=[
-            'https://github.com/onnx/models/raw/69c5d3751dda5349fd3fc53f525395d180420c07/vision/classification/resnet/model/resnet50-v1-7.tar.gz',
-            'https://dl.opencv.org/models/resnet50v1.tar.gz'
-        ],
+        url='https://github.com/onnx/models/raw/69c5d3751dda5349fd3fc53f525395d180420c07/vision/classification/resnet/model/resnet50-v1-7.tar.gz',
         sha='a4ac2da7e0024d61fdb80481496ba966b48b9fea',
         filename='resnet50v1.tar.gz',
         sub=[