Merge branch 4.x

samples/python/tutorial_code/Histograms_Matching/back_projection/calcBackProject_Demo1.py

@@ -38,10 +38,10 @@ def Hist_and_Backproj(val):
 
 ## [Read the image]
 parser = argparse.ArgumentParser(description='Code for Back Projection tutorial.')
-parser.add_argument('--input', help='Path to input image.')
+parser.add_argument('--input', help='Path to input image.', default='home.jpg')
 args = parser.parse_args()
 
-src = cv.imread(args.input)
+src = cv.imread(cv.samples.findFile(args.input))
 if src is None:
     print('Could not open or find the image:', args.input)
     exit(0)

samples/python/tutorial_code/Histograms_Matching/back_projection/calcBackProject_Demo2.py

@@ -54,10 +54,10 @@ def Hist_and_Backproj(mask):
 
 # Read the image
 parser = argparse.ArgumentParser(description='Code for Back Projection tutorial.')
-parser.add_argument('--input', help='Path to input image.')
+parser.add_argument('--input', help='Path to input image.', default='home.jpg')
 args = parser.parse_args()
 
-src = cv.imread(args.input)
+src = cv.imread(cv.samples.findFile(args.input))
 if src is None:
     print('Could not open or find the image:', args.input)
     exit(0)

samples/python/tutorial_code/Histograms_Matching/histogram_calculation/calcHist_Demo.py

@@ -53,14 +53,14 @@ cv.normalize(r_hist, r_hist, alpha=0, beta=hist_h, norm_type=cv.NORM_MINMAX)
 
 ## [Draw for each channel]
 for i in range(1, histSize):
-    cv.line(histImage, ( bin_w*(i-1), hist_h - int(round(b_hist[i-1])) ),
-            ( bin_w*(i), hist_h - int(round(b_hist[i])) ),
+    cv.line(histImage, ( bin_w*(i-1), hist_h - int(b_hist[i-1]) ),
+            ( bin_w*(i), hist_h - int(b_hist[i]) ),
             ( 255, 0, 0), thickness=2)
-    cv.line(histImage, ( bin_w*(i-1), hist_h - int(round(g_hist[i-1])) ),
-            ( bin_w*(i), hist_h - int(round(g_hist[i])) ),
+    cv.line(histImage, ( bin_w*(i-1), hist_h - int(g_hist[i-1]) ),
+            ( bin_w*(i), hist_h - int(g_hist[i]) ),
             ( 0, 255, 0), thickness=2)
-    cv.line(histImage, ( bin_w*(i-1), hist_h - int(round(r_hist[i-1])) ),
-            ( bin_w*(i), hist_h - int(round(r_hist[i])) ),
+    cv.line(histImage, ( bin_w*(i-1), hist_h - int(r_hist[i-1]) ),
+            ( bin_w*(i), hist_h - int(r_hist[i]) ),
             ( 0, 0, 255), thickness=2)
 ## [Draw for each channel]
 

samples/python/tutorial_code/ImgTrans/distance_transformation/imageSegmentation.py

@@ -102,7 +102,8 @@ for i in range(len(contours)):
 
 # Draw the background marker
 cv.circle(markers, (5,5), 3, (255,255,255), -1)
-cv.imshow('Markers', markers*10000)
+markers_8u = (markers * 10).astype('uint8')
+cv.imshow('Markers', markers_8u)
 ## [seeds]
 
 ## [watershed]

samples/python/tutorial_code/TrackingMotion/corner_subpixels/cornerSubPix_Demo.py

@@ -30,7 +30,7 @@ def goodFeaturesToTrack_Demo(val):
     print('** Number of corners detected:', corners.shape[0])
     radius = 4
     for i in range(corners.shape[0]):
-        cv.circle(copy, (corners[i,0,0], corners[i,0,1]), radius, (rng.randint(0,256), rng.randint(0,256), rng.randint(0,256)), cv.FILLED)
+        cv.circle(copy, (int(corners[i,0,0]), int(corners[i,0,1])), radius, (rng.randint(0,256), rng.randint(0,256), rng.randint(0,256)), cv.FILLED)
 
     # Show what you got
     cv.namedWindow(source_window)

samples/python/tutorial_code/TrackingMotion/good_features_to_track/goodFeaturesToTrack_Demo.py

@@ -30,7 +30,7 @@ def goodFeaturesToTrack_Demo(val):
     print('** Number of corners detected:', corners.shape[0])
     radius = 4
     for i in range(corners.shape[0]):
-        cv.circle(copy, (corners[i,0,0], corners[i,0,1]), radius, (rng.randint(0,256), rng.randint(0,256), rng.randint(0,256)), cv.FILLED)
+        cv.circle(copy, (int(corners[i,0,0]), int(corners[i,0,1])), radius, (rng.randint(0,256), rng.randint(0,256), rng.randint(0,256)), cv.FILLED)
 
     # Show what you got
     cv.namedWindow(source_window)

samples/python/tutorial_code/dnn/dnn_conversion/common/test/voc_segm_test.py

@@ -0,0 +1,41 @@
+import numpy as np
+
+from ..accuracy_eval import SemSegmEvaluation
+from ..utils import plot_acc
+
+
+def test_segm_models(models_list, data_fetcher, eval_params, experiment_name, is_print_eval_params=True,
+                     is_plot_acc=True):
+    if is_print_eval_params:
+        print(
+            "===== Running evaluation of the classification models with the following params:\n"
+            "\t0. val data location: {}\n"
+            "\t1. val data labels: {}\n"
+            "\t2. frame size: {}\n"
+            "\t3. batch size: {}\n"
+            "\t4. transform to RGB: {}\n"
+            "\t5. log file location: {}\n".format(
+                eval_params.imgs_segm_dir,
+                eval_params.img_cls_file,
+                eval_params.frame_size,
+                eval_params.batch_size,
+                eval_params.bgr_to_rgb,
+                eval_params.log
+            )
+        )
+
+    accuracy_evaluator = SemSegmEvaluation(eval_params.log, eval_params.img_cls_file, eval_params.batch_size)
+    accuracy_evaluator.process(models_list, data_fetcher)
+    accuracy_array = np.array(accuracy_evaluator.general_fw_accuracy)
+
+    print(
+        "===== End of processing. Accuracy results:\n"
+        "\t1. max accuracy (top-5) for the original model: {}\n"
+        "\t2. max accuracy (top-5) for the DNN model: {}\n".format(
+            max(accuracy_array[:, 0]),
+            max(accuracy_array[:, 1]),
+        )
+    )
+
+    if is_plot_acc:
+        plot_acc(accuracy_array, experiment_name)

samples/python/tutorial_code/dnn/dnn_conversion/pytorch/segmentation/py_to_py_fcn_resnet50.py

@@ -0,0 +1,59 @@
+from torchvision import models
+
+from ..pytorch_model import (
+    PyTorchModelPreparer,
+    PyTorchModelProcessor,
+    PyTorchDnnModelProcessor
+)
+from ...common.utils import set_pytorch_env, create_parser
+
+
+class PyTorchFcnResNet50(PyTorchModelPreparer):
+    def __init__(self, model_name, original_model):
+        super(PyTorchFcnResNet50, self).__init__(model_name, original_model)
+
+
+def main():
+    parser = create_parser()
+    cmd_args = parser.parse_args()
+    set_pytorch_env()
+
+    # Test the base process of model retrieval
+    resnets = PyTorchFcnResNet50(
+        model_name="resnet50",
+        original_model=models.segmentation.fcn_resnet50(pretrained=True)
+    )
+    model_dict = resnets.get_prepared_models()
+
+    if cmd_args.is_evaluate:
+        from ...common.test_config import TestConfig
+        from ...common.accuracy_eval import PASCALDataFetch
+        from ...common.test.voc_segm_test import test_segm_models
+
+        eval_params = TestConfig()
+
+        model_names = list(model_dict.keys())
+        original_model_name = model_names[0]
+        dnn_model_name = model_names[1]
+
+        #img_dir, segm_dir, names_file, segm_cls_colors_file, preproc)
+        data_fetcher = PASCALDataFetch(
+            imgs_dir=eval_params.imgs_segm_dir,
+            frame_size=eval_params.frame_size,
+            bgr_to_rgb=eval_params.bgr_to_rgb,
+
+        )
+
+        test_segm_models(
+            [
+                PyTorchModelProcessor(model_dict[original_model_name], original_model_name),
+                PyTorchDnnModelProcessor(model_dict[dnn_model_name], dnn_model_name)
+            ],
+            data_fetcher,
+            eval_params,
+            original_model_name
+        )
+
+
+if __name__ == "__main__":
+    main()

samples/python/tutorial_code/ml/introduction_to_svm/introduction_to_svm.py

@@ -53,7 +53,7 @@ thickness = 2
 sv = svm.getUncompressedSupportVectors()
 
 for i in range(sv.shape[0]):
-    cv.circle(image, (sv[i,0], sv[i,1]), 6, (128, 128, 128), thickness)
+    cv.circle(image, (int(sv[i,0]), int(sv[i,1])), 6, (128, 128, 128), thickness)
 ## [show_vectors]
 
 cv.imwrite('result.png', image) # save the image

samples/python/tutorial_code/ml/non_linear_svms/non_linear_svms.py

@@ -94,13 +94,13 @@ thick = -1
 for i in range(NTRAINING_SAMPLES):
     px = trainData[i,0]
     py = trainData[i,1]
-    cv.circle(I, (px, py), 3, (0, 255, 0), thick)
+    cv.circle(I, (int(px), int(py)), 3, (0, 255, 0), thick)
 
 # Class 2
 for i in range(NTRAINING_SAMPLES, 2*NTRAINING_SAMPLES):
     px = trainData[i,0]
     py = trainData[i,1]
-    cv.circle(I, (px, py), 3, (255, 0, 0), thick)
+    cv.circle(I, (int(px), int(py)), 3, (255, 0, 0), thick)
 ## [show_data]
 
 #------------------------- 6. Show support vectors --------------------------------------------
@@ -109,7 +109,7 @@ thick = 2
 sv = svm.getUncompressedSupportVectors()
 
 for i in range(sv.shape[0]):
-    cv.circle(I, (sv[i,0], sv[i,1]), 6, (128, 128, 128), thick)
+    cv.circle(I, (int(sv[i,0]), int(sv[i,1])), 6, (128, 128, 128), thick)
 ## [show_vectors]
 
 cv.imwrite('result.png', I)                      # save the Image

samples/python/tutorial_code/ml/py_svm_opencv/hogsvm.py

@@ -33,7 +33,7 @@ def hog(img):
     return hist
 ## [hog]
 
-img = cv.imread('digits.png',0)
+img = cv.imread(cv.samples.findFile('digits.png'),0)
 if img is None:
     raise Exception("we need the digits.png image from samples/data here !")
 

samples/python/tutorial_code/video/background_subtraction/bg_sub.py

@@ -18,7 +18,7 @@ else:
 
 ## [capture]
 capture = cv.VideoCapture(cv.samples.findFileOrKeep(args.input))
-if not capture.isOpened:
+if not capture.isOpened():
     print('Unable to open: ' + args.input)
     exit(0)
 ## [capture]

samples/python/tutorial_code/video/optical_flow/optical_flow.py

@@ -40,15 +40,16 @@ while(1):
     p1, st, err = cv.calcOpticalFlowPyrLK(old_gray, frame_gray, p0, None, **lk_params)
 
     # Select good points
-    good_new = p1[st==1]
-    good_old = p0[st==1]
+    if p1 is not None:
+        good_new = p1[st==1]
+        good_old = p0[st==1]
 
     # draw the tracks
     for i,(new,old) in enumerate(zip(good_new, good_old)):
         a,b = new.ravel()
         c,d = old.ravel()
-        mask = cv.line(mask, (a,b),(c,d), color[i].tolist(), 2)
-        frame = cv.circle(frame,(a,b),5,color[i].tolist(),-1)
+        mask = cv.line(mask, (int(a),int(b)),(int(c),int(d)), color[i].tolist(), 2)
+        frame = cv.circle(frame,(int(a),int(b)),5,color[i].tolist(),-1)
     img = cv.add(frame,mask)
 
     cv.imshow('frame',img)

samples/python/tutorial_code/videoio/video-input-psnr-ssim.py

@@ -86,8 +86,8 @@ def main():
 
     framenum = -1 # Frame counter
 
-    captRefrnc = cv.VideoCapture(sourceReference)
-    captUndTst = cv.VideoCapture(sourceCompareWith)
+    captRefrnc = cv.VideoCapture(cv.samples.findFileOrKeep(sourceReference))
+    captUndTst = cv.VideoCapture(cv.samples.findFileOrKeep(sourceCompareWith))
 
     if not captRefrnc.isOpened():
         print("Could not open the reference " + sourceReference)