Merge pull request #46 from v-jaswel/patch-2

[Face] Add quickstart code for FindSimilar

samples/face/find_similar.py

@@ -0,0 +1,82 @@
+import os
+import uuid
+import time
+
+from azure.cognitiveservices.vision.face import FaceClient
+from msrest.authentication import CognitiveServicesCredentials
+from azure.cognitiveservices.vision.face.models import TrainingStatusType, Person
+
+# NOTE: Replace this with a valid Face subscription key.
+SUBSCRIPTION_KEY = "INSERT KEY HERE"
+
+# You must use the same region as you used to get your subscription
+# keys. For example, if you got your subscription keys from westus,
+# replace "westcentralus" with "westus".
+#
+# Free trial subscription keys are generated in the westcentralus
+# region. If you use a free trial subscription key, you shouldn't
+# need to change the region.
+FACE_LOCATION = "westcentralus"
+
+face_base_url = "https://{}.api.cognitive.microsoft.com".format(FACE_LOCATION)
+face_client = FaceClient(face_base_url, CognitiveServicesCredentials(SUBSCRIPTION_KEY))
+
+# This image should contain a single face.
+remote_image_URL_1 = "https://www.biography.com/.image/t_share/MTQ1MzAyNzYzOTgxNTE0NTEz/john-f-kennedy---mini-biography.jpg"
+
+# This image should contain several faces, at least one of which is similar to the face in remote_image_URL_1.
+remote_image_URL_2 = "https://www.biography.com/.image/t_share/MTQ1NDY3OTIxMzExNzM3NjE3/john-f-kennedy---debating-richard-nixon.jpg"
+
+# Detect faces in a remote image.
+def detect_faces(face_client, image_url):
+	print ("Detecting faces...")
+	detected_faces = face_client.face.detect_with_url(url=image_url)
+	if not detected_faces:
+		raise Exception('No face detected from image {}'.format(image_url))
+	if not detected_faces[0]:
+		raise Exception("Parameter return_face_id of detect_with_stream or detect_with_url must be set to true (by default) for recognition purpose.")
+	return detected_faces
+
+# Find similar faces to @face_ID in @face_IDs.
+def find_similar_faces(face_client, face_ID, face_IDs):
+	print("Finding similar faces ...")
+	return face_client.face.find_similar(face_id=face_ID, face_ids=face_IDs)
+
+# Detect a face in the first image.
+faces_1 = detect_faces(face_client, remote_image_URL_1)
+if not faces_1[0]:
+	print("No faces detected in " + remote_image_URL_1 + ".")
+else:
+	print("Face IDs of faces detected in " + remote_image_URL_1 + ":")
+	for x in faces_1: print (x.face_id)
+
+	print("Using first face ID.")
+	face_ID = faces_1[0].face_id
+
+	# Detect a list of faces in the second image.
+	faces_2 = detect_faces(face_client, remote_image_URL_2)
+	if not faces_2[0]:
+		print("No faces detected in " + remote_image_URL_2 + ".")
+	else:
+		print("Face IDs of faces detected in " + remote_image_URL_2 + ":")
+		for x in faces_2: print (x.face_id)
+
+		# Search the faces detected in the second image to find a similar face to the first one.
+		similar_faces = find_similar_faces(face_client, face_ID, list(map(lambda x: x.face_id, faces_2)))
+		if not similar_faces[0]:
+			print("No similar faces found in " + remote_image_URL_2 + ".")
+		else:
+			print("Similar faces found in " + remote_image_URL_2 + ":")
+			for face in similar_faces:
+				face_ID = face.face_id
+				# SimilarFace only contains a Face ID, Persisted Face ID, and confidence score.
+				# So we look up the Face ID in the list of DetectedFaces found in
+				# remote_image_URL_2 to get the rest of the face information.
+				face_info = next(x for x in faces_2 if x.face_id == face_ID)
+				if face_info:
+					print("Face ID: " + face_ID)
+					print("Face rectangle:")
+					print("Left: " + str(face_info.face_rectangle.left))
+					print("Top: " + str(face_info.face_rectangle.top))
+					print("Width: " + str(face_info.face_rectangle.width))
+					print("Height: " + str(face_info.face_rectangle.height))