Removed python2 executable dependency

modules/dnn/CMakeLists.txt

@@ -52,12 +52,10 @@ endif()
 # ----------------------------------------------------------------------------
 # Download pre-trained models for complex testing on GoogLeNet and AlexNet
 # ----------------------------------------------------------------------------
-OCV_OPTION(${the_module}_DOWNLOAD_CAFFE_MODELS "Use GoogLeNet Caffe model for testing" OFF IF BUILD_TESTS AND PYTHON2_EXECUTABLE AND DEFINED ENV{OPENCV_TEST_DATA_PATH})
-if(BUILD_TESTS AND PYTHON2_EXECUTABLE AND DEFINED ENV{OPENCV_TEST_DATA_PATH}
-        AND (DOWNLOAD_EXTERNAL_TEST_DATA OR ${the_module}_DOWNLOAD_CAFFE_MODELS))
+OCV_OPTION(${the_module}_DOWNLOAD_CAFFE_MODELS "Use GoogLeNet Caffe model for testing" OFF IF BUILD_TESTS AND DEFINED ENV{OPENCV_TEST_DATA_PATH})
+if(BUILD_TESTS AND DEFINED ENV{OPENCV_TEST_DATA_PATH} AND (DOWNLOAD_EXTERNAL_TEST_DATA OR ${the_module}_DOWNLOAD_CAFFE_MODELS))
     add_custom_command( TARGET opencv_test_${name} POST_BUILD
-                        COMMAND ${PYTHON2_EXECUTABLE} download_model.py test_models.json
-                        WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/scripts )
+                        COMMAND ${CMAKE_COMMAND} -Dmodel=GoogleNet -P ${CMAKE_CURRENT_SOURCE_DIR}/cmake/download_model.cmake)
     add_definitions(-DENABLE_CAFFE_MODEL_TESTS=1)
 endif()
 
@@ -68,21 +66,29 @@ OCV_OPTION(${the_module}_BUILD_TORCH_IMPORTER "Build Torch model importer (exper
 if(${the_module}_BUILD_TORCH_IMPORTER)
     add_definitions(-DENABLE_TORCH_IMPORTER=1)
     ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4702 /wd4127 /wd4267) #supress warnings in original torch files
+
+    if(NOT DEFINED HAVE_TORCH_EXE)
+        execute_process(COMMAND th ${CMAKE_CURRENT_SOURCE_DIR}/testdata/dnn/torch/torch_nn_echo.lua RESULT_VARIABLE TORCH_EXE_STATUS)
+        set(HAVE_TORCH_EXE OFF)
+        if(${TORCH_EXE_STATUS} EQUAL 0)
+            set(HAVE_TORCH_EXE ON)
+        endif()
+        set(HAVE_TORCH_EXE ${HAVE_TORCH_EXE} CACHE INTERNAL "Have torch binary")
+    endif()
 endif()
 
 # ----------------------------------------------------------------------------
 # Generating test data for Torch importer
 # ----------------------------------------------------------------------------
-OCV_OPTION(${the_module}_BUILD_TORCH_TESTS "Build Torch tests (installed torch7 with nn module is required)" ON IF BUILD_TESTS AND ${the_module}_BUILD_TORCH_IMPORTER)
+OCV_OPTION(${the_module}_BUILD_TORCH_TESTS "Build Torch tests (installed torch7 with nn module is required)" ON IF BUILD_TESTS AND ${the_module}_BUILD_TORCH_IMPORTER AND HAVE_TORCH_EXE)
 if(${the_module}_BUILD_TORCH_TESTS)
 
     if(NOT DEFINED ENV{OPENCV_TEST_DATA_PATH})
         message(FATAL_ERROR "OPENCV_TEST_DATA_PATH environment variable was not specified")
     endif()
 
-    execute_process(COMMAND th ${CMAKE_CURRENT_SOURCE_DIR}/testdata/dnn/torch/torch_nn_echo.lua RESULT_VARIABLE TORCH_STATUS)
-    if(TORCH_STATUS)
-        message(FATAL_ERROR "Torch executable \"th\" not found (status: ${TORCH_STATUS}) or nn module not found")
+    if(NOT HAVE_TORCH_EXE)
+        message(FATAL_ERROR "Torch executable \"th\" not found or nn module not found")
     endif()
 
     add_custom_command( TARGET opencv_test_${name} POST_BUILD

modules/dnn/cmake/download_model.cmake

@@ -0,0 +1,31 @@
+set(GoogleNet_url "http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel")
+set(GoogleNet_dst "$ENV{OPENCV_TEST_DATA_PATH}/dnn/bvlc_googlenet.caffemodel")
+set(GoogleNet_sha "405fc5acd08a3bb12de8ee5e23a96bec22f08204")
+
+set(VGG16_url "http://www.robots.ox.ac.uk/~vgg/software/very_deep/caffe/VGG_ILSVRC_16_layers.caffemodel")
+set(GG16_dst "$ENV{OPENCV_TEST_DATA_PATH}/dnn/VGG_ILSVRC_16_layers.caffemodel")
+
+set(voc-fcn32s_url "http://dl.caffe.berkeleyvision.org/fcn32s-heavy-pascal.caffemodel")
+set(voc-fcn32s_dst "$ENV{OPENCV_TEST_DATA_PATH}/dnn/fcn32s-heavy-pascal.caffemodel")
+
+if(NOT model)
+    set(model "GoogleNet")
+endif()
+
+message(STATUS "Downloading ${${model}_url} to ${${model}_dst}")
+
+if(NOT EXISTS ${${model}_dst})
+    if(${${model}_sha})
+        file(DOWNLOAD ${${model}_url} ${${model}_dst} SHOW_PROGRESS EXPECTED_HASH SHA1=${${model}_sha} STATUS status_vec)
+    else()
+        file(DOWNLOAD ${${model}_url} ${${model}_dst} SHOW_PROGRESS STATUS status_vec)
+    endif()
+    
+    list(GET status_vec 0 status)
+    list(GET status_vec 1 status_msg)
+    if(status EQUAL 0)
+        message(STATUS "Ok! ${status_msg}")
+    else()
+        message(STATUS "Fail! ${status_msg}")
+    endif()
+endif()

modules/dnn/scripts/download_model.py

@@ -1,79 +0,0 @@
-#!/usr/bin/env python
-import os
-import sys
-import time
-import urllib
-import hashlib
-import argparse
-import json
-
-
-def reporthook(count, block_size, total_size):
-    """
-    From http://blog.moleculea.com/2012/10/04/urlretrieve-progres-indicator/
-    """
-    global start_time
-    global prev_duration
-    if count == 0:
-        start_time = time.time()
-        prev_duration = -1
-        return
-    duration = max(1, time.time() - start_time)
-    if int(duration) == int(prev_duration):
-        return
-
-    progress_size = int(count * block_size)
-    speed = int(progress_size / (1024 * duration))
-    percent = int(count * block_size * 100 / total_size)
-    sys.stdout.write("\r...%d%%, %d MB, %d KB/s, %d seconds passed" %
-                     (percent, progress_size / (1024 * 1024), speed, duration))
-    sys.stdout.flush()
-    prev_duration = duration
-
-
-# Function for checking SHA1.
-def model_checks_out(filename, sha1):
-    with open(filename, 'r') as f:
-        return hashlib.sha1(f.read()).hexdigest() == sha1
-
-def model_download(filename, url, sha1):
-    # Check if model exists.
-    if os.path.exists(filename) and model_checks_out(filename, sha1):
-        print("Model {} already exists.".format(filename))
-        return
-
-    # Download and verify model.
-    urllib.urlretrieve(url, filename, reporthook)
-    print model_checks_out(filename, sha1)
-    if not model_checks_out(filename, sha1):
-        print("ERROR: model {} did not download correctly!".format(url))
-        sys.exit(1)
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description="Downloading trained model binaries.")
-    parser.add_argument("download_list")
-    args = parser.parse_args()
-
-    test_dir = os.environ.get("OPENCV_TEST_DATA_PATH")
-    if not test_dir:
-        print "ERROR: OPENCV_TEST_DATA_PATH environment not specified"
-        sys.exit(1)
-
-    try:
-        with open(args.download_list, 'r') as f:
-            models_to_download = json.load(f)
-    except:
-        print "ERROR: Can't pasrse {}".format(args.download_list)
-        sys.exit(1)
-
-    for model_name in models_to_download:
-        model = models_to_download[model_name]
-
-        dst_dir = os.path.join(test_dir, os.path.dirname(model['file']))
-        dst_file = os.path.join(test_dir, model['file'])
-        if not os.path.exists(dst_dir):
-            print "ERROR: Can't find module testdata path '{}'".format(dst_dir)
-            sys.exit(1)
-
-        print "Downloading model '{}' to {} from {} ...".format(model_name, dst_file, model['url'])
-        model_download(dst_file, model['url'], model['sha1'])

modules/dnn/scripts/test_models.json

@@ -1,7 +0,0 @@
-{
-  "googlenet": {
-    "file": "dnn/bvlc_googlenet.caffemodel",
-    "url": "http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel",
-    "sha1": "405fc5acd08a3bb12de8ee5e23a96bec22f08204"
-  }
-}