Merge pull request #1042 from dkurt:dnn_tflite

* Test data for TFLite importer

* TFLite Hair Segmentation test data

* Remove commented code

Author: dkurt

testdata/dnn/download_models.py

@@ -1020,6 +1020,26 @@ def get_confirm_token(response):  # in case of large files
         url='https://raw.githubusercontent.com/zihaomu/opencv_extra_data_backup/main/NanoTrackV2/models/nanotrack_head_sim_v2.onnx',
         sha='39f168489671700cf739e402dfc67d41ce648aef',
         filename='onnx/models/nanotrack_head_sim_v2.onnx'),
+    Model(
+        name='Face Mesh (TFLite)',
+        url='https://storage.googleapis.com/mediapipe-assets/face_landmark.tflite',
+        sha='eb01d1d88c833aaea64c880506da72e4a4f43154',
+        filename='tflite/face_landmark.tflite'),
+    Model(
+        name='Face Detection (TFLite)',
+        url='https://storage.googleapis.com/mediapipe-assets/face_detection_short_range.tflite',
+        sha='e8f749fafc23bb88daac85bc9f7e0698436f29a0',
+        filename='tflite/face_detection_short_range.tflite'),
+    Model(
+        name='Selfie Segmentation (TFLite)',
+        url='https://storage.googleapis.com/mediapipe-assets/selfie_segmentation.tflite',
+        sha='8d497f51bd678fa5fb95c3871be72eb5d722b831',
+        filename='tflite/selfie_segmentation.tflite'),
+    Model(
+        name='Hair Segmentation (TFLite)',
+        url='https://storage.googleapis.com/mediapipe-assets/hair_segmentation.tflite',
+        sha='bba28400dfc264b1ed7ee95df718fada1879644d',
+        filename='tflite/hair_segmentation.tflite'),
 ]
 
 # Note: models will be downloaded to current working directory

testdata/dnn/tflite/face_detection_short_range_out_classificators.npy

@@ -0,0 +1,47 @@
+# Use this script to generate test data for dnn module and TFLite models
+import os
+import numpy as np
+import tensorflow as tf
+import mediapipe as mp
+
+import cv2 as cv
+
+testdata = os.environ['OPENCV_TEST_DATA_PATH']
+
+image = cv.imread(os.path.join(testdata, "cv", "shared", "lena.png"))
+image = cv.cvtColor(image, cv.COLOR_BGR2RGB)
+
+def run_tflite_model(model_name, inp_size):
+    interpreter = tf.lite.Interpreter(model_name + ".tflite",
+                                      experimental_preserve_all_tensors=True)
+    interpreter.allocate_tensors()
+
+    # Get input and output tensors.
+    input_details = interpreter.get_input_details()
+    output_details = interpreter.get_output_details()
+
+    # Run model
+    inp = cv.resize(image, inp_size)
+    inp = np.expand_dims(inp, 0)
+    inp = inp.astype(np.float32) / 255  # NHWC
+
+    interpreter.set_tensor(input_details[0]['index'], inp)
+
+    interpreter.invoke()
+
+    for details in output_details:
+        out = interpreter.get_tensor(details['index'])  # Or use an intermediate layer index
+        out_name = details['name']
+        np.save(f"{model_name}_out_{out_name}.npy", out)
+
+
+def run_mediapipe_solution(solution, inp_size):
+    with solution as selfie_segmentation:
+        inp = cv.resize(image, inp_size)
+        results = selfie_segmentation.process(inp)
+        np.save(f"selfie_segmentation_out_activation_10.npy", results.segmentation_mask)
+
+run_tflite_model("face_landmark", (192, 192))
+run_tflite_model("face_detection_short_range", (128, 128))
+
+run_mediapipe_solution(mp.solutions.selfie_segmentation.SelfieSegmentation(model_selection=0), (256, 256))