Port mtcnn to Tensorflow 2

Using the TF1 model.pb

doc/plot/detect_faces_mtcnn.py

+import matplotlib.pyplot as plt
+from bob.io.base import load
+from bob.io.base.test_utils import datafile
+from bob.io.image import imshow
+from bob.ip.facedetect.mtcnn import MTCNN
+from matplotlib.patches import Circle
+from matplotlib.patches import Rectangle
+
+# load colored test image
+color_image = load(datafile("test_image_multi_face.png", "bob.ip.facedetect"))
+is_tf_available = True
+try:
+    import tensorflow
+except Exception:
+    is_tf_available = False
+
+if not is_tf_available:
+    imshow(color_image)
+else:
+
+    # detect all face
+    detector = MTCNN()
+    detections = detector(color_image)
+
+    imshow(color_image)
+    plt.axis("off")
+
+    for annotations in detections:
+        topleft = annotations["topleft"]
+        bottomright = annotations["bottomright"]
+        size = bottomright[0] - topleft[0], bottomright[1] - topleft[1]
+        # draw bounding boxes
+        plt.gca().add_patch(
+            Rectangle(
+                topleft[::-1],
+                size[1],
+                size[0],
+                edgecolor="b",
+                facecolor="none",
+                linewidth=2,
+            )
+        )
+        # draw face landmarks
+        for key, color in (
+            ("reye", "r"),
+            ("leye", "g"),
+            ("nose", "b"),
+            ("mouthright", "k"),
+            ("mouthleft", "w"),
+        ):
+            plt.gca().add_patch(
+                Circle(
+                    annotations[key][::-1],
+                    radius=2,
+                    edgecolor=color,
+                    facecolor="none",
+                    linewidth=2,
+                )
+            )
+        # show quality of detections
+        plt.text(
+            topleft[1],
+            topleft[0],
+            round(annotations["quality"], 3),
+            color="b",
+            fontsize=14,
+        )