opencv/modules/gapi/misc/python/test/test_gapi_infer.py

#!/usr/bin/env python

import numpy as np
import cv2 as cv
import os

from tests_common import NewOpenCVTests


class test_gapi_infer(NewOpenCVTests):

    def infer_reference_network(self, model_path, weights_path, img):
        net = cv.dnn.readNetFromModelOptimizer(model_path, weights_path)
        net.setPreferableBackend(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE)
        net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU)

        blob = cv.dnn.blobFromImage(img)

        net.setInput(blob)
        return net.forward(net.getUnconnectedOutLayersNames())


    def make_roi(self, img, roi):
        return img[roi[1]:roi[1] + roi[3], roi[0]:roi[0] + roi[2], ...]


    def test_age_gender_infer(self):
        # NB: Check IE
        if not cv.dnn.DNN_TARGET_CPU in cv.dnn.getAvailableTargets(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE):
            return

        root_path    = '/omz_intel_models/intel/age-gender-recognition-retail-0013/FP32/age-gender-recognition-retail-0013'
        model_path   = self.find_file(root_path + '.xml',   [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')])
        weights_path = self.find_file(root_path + '.bin',   [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')])
        device_id    = 'CPU'

        img_path  = self.find_file('cv/face/david2.jpg', [os.environ.get('OPENCV_TEST_DATA_PATH')])
        img       = cv.resize(cv.imread(img_path), (62,62))

        # OpenCV DNN
        dnn_age, dnn_gender = self.infer_reference_network(model_path, weights_path, img)

        # OpenCV G-API
        g_in   = cv.GMat()
        inputs = cv.GInferInputs()
        inputs.setInput('data', g_in)

        outputs  = cv.gapi.infer("net", inputs)
        age_g    = outputs.at("age_conv3")
        gender_g = outputs.at("prob")

        comp = cv.GComputation(cv.GIn(g_in), cv.GOut(age_g, gender_g))
        pp = cv.gapi.ie.params("net", model_path, weights_path, device_id)

        gapi_age, gapi_gender = comp.apply(cv.gin(img), args=cv.compile_args(cv.gapi.networks(pp)))

        # Check
        self.assertEqual(0.0, cv.norm(dnn_gender, gapi_gender, cv.NORM_INF))
        self.assertEqual(0.0, cv.norm(dnn_age, gapi_age, cv.NORM_INF))


    def test_age_gender_infer_roi(self):
        # NB: Check IE
        if not cv.dnn.DNN_TARGET_CPU in cv.dnn.getAvailableTargets(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE):
            return

        root_path    = '/omz_intel_models/intel/age-gender-recognition-retail-0013/FP32/age-gender-recognition-retail-0013'
        model_path   = self.find_file(root_path + '.xml',   [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')])
        weights_path = self.find_file(root_path + '.bin',   [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')])
        device_id    = 'CPU'

        img_path = self.find_file('cv/face/david2.jpg', [os.environ.get('OPENCV_TEST_DATA_PATH')])
        img = cv.imread(img_path)
        roi      = (10, 10, 62, 62)

        # OpenCV DNN
        dnn_age, dnn_gender = self.infer_reference_network(model_path,
                                                           weights_path,
                                                           self.make_roi(img, roi))

        # OpenCV G-API
        g_in   = cv.GMat()
        g_roi  = cv.GOpaqueT(cv.gapi.CV_RECT)
        inputs = cv.GInferInputs()
        inputs.setInput('data', g_in)

        outputs  = cv.gapi.infer("net", g_roi, inputs)
        age_g    = outputs.at("age_conv3")
        gender_g = outputs.at("prob")

        comp = cv.GComputation(cv.GIn(g_in, g_roi), cv.GOut(age_g, gender_g))
        pp = cv.gapi.ie.params("net", model_path, weights_path, device_id)

        gapi_age, gapi_gender = comp.apply(cv.gin(img, roi), args=cv.compile_args(cv.gapi.networks(pp)))

        # Check
        self.assertEqual(0.0, cv.norm(dnn_gender, gapi_gender, cv.NORM_INF))
        self.assertEqual(0.0, cv.norm(dnn_age, gapi_age, cv.NORM_INF))


    def test_age_gender_infer_roi_list(self):
        # NB: Check IE
        if not cv.dnn.DNN_TARGET_CPU in cv.dnn.getAvailableTargets(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE):
            return

        root_path    = '/omz_intel_models/intel/age-gender-recognition-retail-0013/FP32/age-gender-recognition-retail-0013'
        model_path   = self.find_file(root_path + '.xml',   [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')])
        weights_path = self.find_file(root_path + '.bin',   [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')])
        device_id    = 'CPU'

        rois = [(10, 15, 62, 62), (23, 50, 62, 62), (14, 100, 62, 62), (80, 50, 62, 62)]
        img_path = self.find_file('cv/face/david2.jpg', [os.environ.get('OPENCV_TEST_DATA_PATH')])
        img = cv.imread(img_path)

        # OpenCV DNN
        dnn_age_list    = []
        dnn_gender_list = []
        for roi in rois:
            age, gender = self.infer_reference_network(model_path,
                                                       weights_path,
                                                       self.make_roi(img, roi))
            dnn_age_list.append(age)
            dnn_gender_list.append(gender)

        # OpenCV G-API
        g_in   = cv.GMat()
        g_rois = cv.GArrayT(cv.gapi.CV_RECT)
        inputs = cv.GInferInputs()
        inputs.setInput('data', g_in)

        outputs  = cv.gapi.infer("net", g_rois, inputs)
        age_g    = outputs.at("age_conv3")
        gender_g = outputs.at("prob")

        comp = cv.GComputation(cv.GIn(g_in, g_rois), cv.GOut(age_g, gender_g))
        pp = cv.gapi.ie.params("net", model_path, weights_path, device_id)

        gapi_age_list, gapi_gender_list = comp.apply(cv.gin(img, rois),
                                                     args=cv.compile_args(cv.gapi.networks(pp)))

        # Check
        for gapi_age, gapi_gender, dnn_age, dnn_gender in zip(gapi_age_list,
                                                              gapi_gender_list,
                                                              dnn_age_list,
                                                              dnn_gender_list):
            self.assertEqual(0.0, cv.norm(dnn_gender, gapi_gender, cv.NORM_INF))
            self.assertEqual(0.0, cv.norm(dnn_age, gapi_age, cv.NORM_INF))


    def test_age_gender_infer2_roi(self):
        # NB: Check IE
        if not cv.dnn.DNN_TARGET_CPU in cv.dnn.getAvailableTargets(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE):
            return

        root_path    = '/omz_intel_models/intel/age-gender-recognition-retail-0013/FP32/age-gender-recognition-retail-0013'
        model_path   = self.find_file(root_path + '.xml',   [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')])
        weights_path = self.find_file(root_path + '.bin',   [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')])
        device_id    = 'CPU'

        rois = [(10, 15, 62, 62), (23, 50, 62, 62), (14, 100, 62, 62), (80, 50, 62, 62)]
        img_path = self.find_file('cv/face/david2.jpg', [os.environ.get('OPENCV_TEST_DATA_PATH')])
        img = cv.imread(img_path)

        # OpenCV DNN
        dnn_age_list    = []
        dnn_gender_list = []
        for roi in rois:
            age, gender = self.infer_reference_network(model_path,
                                                       weights_path,
                                                       self.make_roi(img, roi))
            dnn_age_list.append(age)
            dnn_gender_list.append(gender)

        # OpenCV G-API
        g_in   = cv.GMat()
        g_rois = cv.GArrayT(cv.gapi.CV_RECT)
        inputs = cv.GInferListInputs()
        inputs.setInput('data', g_rois)

        outputs  = cv.gapi.infer2("net", g_in, inputs)
        age_g    = outputs.at("age_conv3")
        gender_g = outputs.at("prob")

        comp = cv.GComputation(cv.GIn(g_in, g_rois), cv.GOut(age_g, gender_g))
        pp = cv.gapi.ie.params("net", model_path, weights_path, device_id)

        gapi_age_list, gapi_gender_list = comp.apply(cv.gin(img, rois),
                                                     args=cv.compile_args(cv.gapi.networks(pp)))

        # Check
        for gapi_age, gapi_gender, dnn_age, dnn_gender in zip(gapi_age_list,
                                                              gapi_gender_list,
                                                              dnn_age_list,
                                                              dnn_gender_list):
            self.assertEqual(0.0, cv.norm(dnn_gender, gapi_gender, cv.NORM_INF))
            self.assertEqual(0.0, cv.norm(dnn_age, gapi_age, cv.NORM_INF))



    def test_person_detection_retail_0013(self):
        # NB: Check IE
        if not cv.dnn.DNN_TARGET_CPU in cv.dnn.getAvailableTargets(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE):
            return

        root_path    = '/omz_intel_models/intel/person-detection-retail-0013/FP32/person-detection-retail-0013'
        model_path   = self.find_file(root_path + '.xml',   [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')])
        weights_path = self.find_file(root_path + '.bin',   [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')])
        img_path     = self.find_file('gpu/lbpcascade/er.png', [os.environ.get('OPENCV_TEST_DATA_PATH')])
        device_id    = 'CPU'
        img          = cv.resize(cv.imread(img_path), (544, 320))

        # OpenCV DNN
        net = cv.dnn.readNetFromModelOptimizer(model_path, weights_path)
        net.setPreferableBackend(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE)
        net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU)

        blob = cv.dnn.blobFromImage(img)

        def parseSSD(detections, size):
            h, w = size
            bboxes = []
            detections = detections.reshape(-1, 7)
            for sample_id, class_id, confidence, xmin, ymin, xmax, ymax in detections:
                if confidence >= 0.5:
                    x      = int(xmin * w)
                    y      = int(ymin * h)
                    width  = int(xmax * w - x)
                    height = int(ymax * h - y)
                    bboxes.append((x, y, width, height))

            return bboxes

        net.setInput(blob)
        dnn_detections = net.forward()
        dnn_boxes = parseSSD(np.array(dnn_detections), img.shape[:2])

        # OpenCV G-API
        g_in   = cv.GMat()
        inputs = cv.GInferInputs()
        inputs.setInput('data', g_in)

        g_sz       = cv.gapi.streaming.size(g_in)
        outputs    = cv.gapi.infer("net", inputs)
        detections = outputs.at("detection_out")
        bboxes     = cv.gapi.parseSSD(detections, g_sz, 0.5, False, False)

        comp = cv.GComputation(cv.GIn(g_in), cv.GOut(bboxes))
        pp = cv.gapi.ie.params("net", model_path, weights_path, device_id)

        gapi_age, gapi_gender = comp.apply(cv.gin(img), args=cv.compile_args(cv.gapi.networks(pp)))

        gapi_boxes = comp.apply(cv.gin(img.astype(np.float32)),
                                args=cv.compile_args(cv.gapi.networks(pp)))

        # Comparison
        self.assertEqual(0.0, cv.norm(np.array(dnn_boxes).flatten(),
                                      np.array(gapi_boxes).flatten(),
                                      cv.NORM_INF))


    def test_person_detection_retail_0013(self):
        # NB: Check IE
        if not cv.dnn.DNN_TARGET_CPU in cv.dnn.getAvailableTargets(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE):
            return

        root_path    = '/omz_intel_models/intel/person-detection-retail-0013/FP32/person-detection-retail-0013'
        model_path   = self.find_file(root_path + '.xml',   [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')])
        weights_path = self.find_file(root_path + '.bin',   [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')])
        img_path     = self.find_file('gpu/lbpcascade/er.png', [os.environ.get('OPENCV_TEST_DATA_PATH')])
        device_id    = 'CPU'
        img          = cv.resize(cv.imread(img_path), (544, 320))

        # OpenCV DNN
        net = cv.dnn.readNetFromModelOptimizer(model_path, weights_path)
        net.setPreferableBackend(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE)
        net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU)

        blob = cv.dnn.blobFromImage(img)

        def parseSSD(detections, size):
            h, w = size
            bboxes = []
            detections = detections.reshape(-1, 7)
            for sample_id, class_id, confidence, xmin, ymin, xmax, ymax in detections:
                if confidence >= 0.5:
                    x      = int(xmin * w)
                    y      = int(ymin * h)
                    width  = int(xmax * w - x)
                    height = int(ymax * h - y)
                    bboxes.append((x, y, width, height))

            return bboxes

        net.setInput(blob)
        dnn_detections = net.forward()
        dnn_boxes = parseSSD(np.array(dnn_detections), img.shape[:2])

        # OpenCV G-API
        g_in   = cv.GMat()
        inputs = cv.GInferInputs()
        inputs.setInput('data', g_in)

        g_sz       = cv.gapi.streaming.size(g_in)
        outputs    = cv.gapi.infer("net", inputs)
        detections = outputs.at("detection_out")
        bboxes     = cv.gapi.parseSSD(detections, g_sz, 0.5, False, False)

        comp = cv.GComputation(cv.GIn(g_in), cv.GOut(bboxes))
        pp = cv.gapi.ie.params("net", model_path, weights_path, device_id)

        gapi_boxes = comp.apply(cv.gin(img.astype(np.float32)),
                                args=cv.compile_args(cv.gapi.networks(pp)))

        # Comparison
        self.assertEqual(0.0, cv.norm(np.array(dnn_boxes).flatten(),
                                      np.array(gapi_boxes).flatten(),
                                      cv.NORM_INF))


if __name__ == '__main__':
    NewOpenCVTests.bootstrap()