Merge pull request #1383 from serengil/feat-task-0911-file-input-for-api

Feat task 0911 file input for api

.github/workflows/tests.yml

@@ -37,6 +37,8 @@ jobs:
       run: |
         python -m pip install --upgrade pip
         pip install pytest
+        # sending files in form data throwing error in flask 3 while running tests
+        pip install Werkzeug==2.0.2 flask==2.0.2
         pip install .
         
     - name: Test with pytest

README.md

@@ -341,7 +341,7 @@ cd scripts
 
 <p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/deepface-api.jpg" width="90%" height="90%"></p>
 
-Face recognition, facial attribute analysis and vector representation functions are covered in the API. You are expected to call these functions as http post methods. Default service endpoints will be `http://localhost:5005/verify` for face recognition, `http://localhost:5005/analyze` for facial attribute analysis, and `http://localhost:5005/represent` for vector representation. You can pass input images as exact image paths on your environment, base64 encoded strings or images on web. [Here](https://github.com/serengil/deepface/tree/master/deepface/api/postman), you can find a postman project to find out how these methods should be called.
+Face recognition, facial attribute analysis and vector representation functions are covered in the API. You are expected to call these functions as http post methods. Default service endpoints will be `http://localhost:5005/verify` for face recognition, `http://localhost:5005/analyze` for facial attribute analysis, and `http://localhost:5005/represent` for vector representation. The API accepts images as file uploads (via form data), or as exact image paths, URLs, or base64-encoded strings (via either JSON or form data), providing versatile options for different client requirements. [Here](https://github.com/serengil/deepface/tree/master/deepface/api/postman), you can find a postman project to find out how these methods should be called.
 
 **Dockerized Service** - [`Demo`](https://youtu.be/9Tk9lRQareA)
 

deepface/api/postman/deepface-api.postman_collection.json

@@ -1,12 +1,54 @@
 {
 	"info": {
-		"_postman_id": "4c0b144e-4294-4bdd-8072-bcb326b1fed2",
+		"_postman_id": "26c5ee53-1f4b-41db-9342-3617c90059d3",
 		"name": "deepface-api",
 		"schema": "https://schema.getpostman.com/json/collection/v2.1.0/collection.json"
 	},
 	"item": [
 		{
-			"name": "Represent",
+			"name": "Represent - form data",
+			"request": {
+				"method": "POST",
+				"header": [],
+				"body": {
+					"mode": "formdata",
+					"formdata": [
+						{
+							"key": "img",
+							"type": "file",
+							"src": "/Users/sefik/Desktop/deepface/tests/dataset/img1.jpg"
+						},
+						{
+							"key": "model_name",
+							"value": "Facenet",
+							"type": "text"
+						}
+					],
+					"options": {
+						"raw": {
+							"language": "json"
+						}
+					}
+				},
+				"url": {
+					"raw": "http://127.0.0.1:5005/represent",
+					"protocol": "http",
+					"host": [
+						"127",
+						"0",
+						"0",
+						"1"
+					],
+					"port": "5005",
+					"path": [
+						"represent"
+					]
+				}
+			},
+			"response": []
+		},
+		{
+			"name": "Represent - default",
 			"request": {
 				"method": "POST",
 				"header": [],
@@ -20,7 +62,7 @@
 					}
 				},
 				"url": {
-					"raw": "http://127.0.0.1:5000/represent",
+					"raw": "http://127.0.0.1:5005/represent",
 					"protocol": "http",
 					"host": [
 						"127",
@@ -28,7 +70,7 @@
 						"0",
 						"1"
 					],
-					"port": "5000",
+					"port": "5005",
 					"path": [
 						"represent"
 					]
@@ -37,13 +79,13 @@
 			"response": []
 		},
 		{
-			"name": "Face verification",
+			"name": "Face verification - default",
 			"request": {
 				"method": "POST",
 				"header": [],
 				"body": {
 					"mode": "raw",
-					"raw": "  {\n  \t\"img1_path\": \"/Users/sefik/Desktop/deepface/tests/dataset/img1.jpg\",\n    \"img2_path\": \"/Users/sefik/Desktop/deepface/tests/dataset/img2.jpg\",\n    \"model_name\": \"Facenet\",\n    \"detector_backend\": \"mtcnn\",\n    \"distance_metric\": \"euclidean\"\n  }",
+					"raw": "  {\n  \t\"img1\": \"/Users/sefik/Desktop/deepface/tests/dataset/img1.jpg\",\n    \"img2\": \"/Users/sefik/Desktop/deepface/tests/dataset/img2.jpg\",\n    \"model_name\": \"Facenet\",\n    \"detector_backend\": \"mtcnn\",\n    \"distance_metric\": \"euclidean\"\n  }",
 					"options": {
 						"raw": {
 							"language": "json"
@@ -51,7 +93,7 @@
 					}
 				},
 				"url": {
-					"raw": "http://127.0.0.1:5000/verify",
+					"raw": "http://127.0.0.1:5005/verify",
 					"protocol": "http",
 					"host": [
 						"127",
@@ -59,7 +101,7 @@
 						"0",
 						"1"
 					],
-					"port": "5000",
+					"port": "5005",
 					"path": [
 						"verify"
 					]
@@ -68,13 +110,29 @@
 			"response": []
 		},
 		{
-			"name": "Face analysis",
+			"name": "Face verification - form data",
 			"request": {
 				"method": "POST",
 				"header": [],
 				"body": {
-					"mode": "raw",
+					"mode": "formdata",
-					"raw": "{\n    \"img_path\": \"/Users/sefik/Desktop/deepface/tests/dataset/couple.jpg\",\n    \"actions\": [\"age\", \"gender\", \"emotion\", \"race\"]\n}",
+					"formdata": [
+						{
+							"key": "img1",
+							"type": "file",
+							"src": "/Users/sefik/Desktop/deepface/tests/dataset/img1.jpg"
+						},
+						{
+							"key": "img2",
+							"type": "file",
+							"src": "/Users/sefik/Desktop/deepface/tests/dataset/img2.jpg"
+						},
+						{
+							"key": "model_name",
+							"value": "Facenet",
+							"type": "text"
+						}
+					],
 					"options": {
 						"raw": {
 							"language": "json"
@@ -82,7 +140,7 @@
 					}
 				},
 				"url": {
-					"raw": "http://127.0.0.1:5000/analyze",
+					"raw": "http://127.0.0.1:5005/verify",
 					"protocol": "http",
 					"host": [
 						"127",
@@ -90,7 +148,77 @@
 						"0",
 						"1"
 					],
-					"port": "5000",
+					"port": "5005",
+					"path": [
+						"verify"
+					]
+				}
+			},
+			"response": []
+		},
+		{
+			"name": "Face analysis - default",
+			"request": {
+				"method": "POST",
+				"header": [],
+				"body": {
+					"mode": "raw",
+					"raw": "{\n    \"img\": \"/Users/sefik/Desktop/deepface/tests/dataset/img1.jpg\",\n    \"actions\": [\"age\", \"gender\", \"emotion\", \"race\"]\n}",
+					"options": {
+						"raw": {
+							"language": "json"
+						}
+					}
+				},
+				"url": {
+					"raw": "http://127.0.0.1:5005/analyze",
+					"protocol": "http",
+					"host": [
+						"127",
+						"0",
+						"0",
+						"1"
+					],
+					"port": "5005",
+					"path": [
+						"analyze"
+					]
+				}
+			},
+			"response": []
+		},
+		{
+			"name": "Face analysis - form data",
+			"request": {
+				"method": "POST",
+				"header": [],
+				"body": {
+					"mode": "formdata",
+					"formdata": [
+						{
+							"key": "img",
+							"type": "file",
+							"src": "/Users/sefik/Desktop/deepface/tests/dataset/img1.jpg"
+						},
+						{
+							"key": "actions",
+							"value": "\"[age, gender]\"",
+							"type": "text"
+						}
+					],
+					"options": {
+						"raw": {
+							"language": "json"
+						}
+					}
+				},
+				"url": {
+					"raw": "http://localhost:5005/analyze",
+					"protocol": "http",
+					"host": [
+						"localhost"
+					],
+					"port": "5005",
 					"path": [
 						"analyze"
 					]

deepface/api/src/modules/core/routes.py

@@ -1,31 +1,86 @@
+# built-in dependencies
+from typing import Union
+
+# 3rd party dependencies
 from flask import Blueprint, request
+import numpy as np
+
+# project dependencies
 from deepface import DeepFace
 from deepface.api.src.modules.core import service
+from deepface.commons import image_utils
 from deepface.commons.logger import Logger
 
 logger = Logger()
 
 blueprint = Blueprint("routes", __name__)
 
+# pylint: disable=no-else-return, broad-except
+
 
 @blueprint.route("/")
 def home():
     return f"<h1>Welcome to DeepFace API v{DeepFace.__version__}!</h1>"
 
 
+def extract_image_from_request(img_key: str) -> Union[str, np.ndarray]:
+    """
+    Extracts an image from the request either from json or a multipart/form-data file.
+
+    Args:
+        img_key (str): The key used to retrieve the image data
+            from the request (e.g., 'img1').
+
+    Returns:
+        img (str or np.ndarray): Given image detail (base64 encoded string, image path or url)
+            or the decoded image as a numpy array.
+    """
+
+    # Check if the request is multipart/form-data (file input)
+    if request.files:
+        # request.files is instance of werkzeug.datastructures.ImmutableMultiDict
+        # file is instance of werkzeug.datastructures.FileStorage
+        file = request.files.get(img_key)
+
+        if file is None:
+            raise ValueError(f"Request form data doesn't have {img_key}")
+
+        if file.filename == "":
+            raise ValueError(f"No file uploaded for '{img_key}'")
+
+        img = image_utils.load_image_from_file_storage(file)
+
+        return img
+    # Check if the request is coming as base64, file path or url from json or form data
+    elif request.is_json or request.form:
+        input_args = request.get_json() or request.form.to_dict()
+
+        if input_args is None:
+            raise ValueError("empty input set passed")
+
+        # this can be base64 encoded image, and image path or url
+        img = input_args.get(img_key)
+
+        if not img:
+            raise ValueError(f"'{img_key}' not found in either json or form data request")
+
+        return img
+
+    # If neither JSON nor file input is present
+    raise ValueError(f"'{img_key}' not found in request in either json or form data")
+
+
 @blueprint.route("/represent", methods=["POST"])
 def represent():
-    input_args = request.get_json()
+    input_args = request.get_json() or request.form.to_dict()
 
-    if input_args is None:
+    try:
-        return {"message": "empty input set passed"}
+        img = extract_image_from_request("img")
-
+    except Exception as err:
-    img_path = input_args.get("img") or input_args.get("img_path")
+        return {"exception": str(err)}, 400
-    if img_path is None:
-        return {"message": "you must pass img_path input"}
 
     obj = service.represent(
-        img_path=img_path,
+        img_path=img,
         model_name=input_args.get("model_name", "VGG-Face"),
         detector_backend=input_args.get("detector_backend", "opencv"),
         enforce_detection=input_args.get("enforce_detection", True),
@@ -41,23 +96,21 @@ def represent():
 
 @blueprint.route("/verify", methods=["POST"])
 def verify():
-    input_args = request.get_json()
+    input_args = request.get_json() or request.form.to_dict()
 
-    if input_args is None:
+    try:
-        return {"message": "empty input set passed"}
+        img1 = extract_image_from_request("img1")
+    except Exception as err:
+        return {"exception": str(err)}, 400
 
-    img1_path = input_args.get("img1") or input_args.get("img1_path")
+    try:
-    img2_path = input_args.get("img2") or input_args.get("img2_path")
+        img2 = extract_image_from_request("img2")
-
+    except Exception as err:
-    if img1_path is None:
+        return {"exception": str(err)}, 400
-        return {"message": "you must pass img1_path input"}
-
-    if img2_path is None:
-        return {"message": "you must pass img2_path input"}
 
     verification = service.verify(
-        img1_path=img1_path,
+        img1_path=img1,
-        img2_path=img2_path,
+        img2_path=img2,
         model_name=input_args.get("model_name", "VGG-Face"),
         detector_backend=input_args.get("detector_backend", "opencv"),
         distance_metric=input_args.get("distance_metric", "cosine"),
@@ -73,18 +126,31 @@ def verify():
 
 @blueprint.route("/analyze", methods=["POST"])
 def analyze():
-    input_args = request.get_json()
+    input_args = request.get_json() or request.form.to_dict()
 
-    if input_args is None:
+    try:
-        return {"message": "empty input set passed"}
+        img = extract_image_from_request("img")
+    except Exception as err:
+        return {"exception": str(err)}, 400
 
-    img_path = input_args.get("img") or input_args.get("img_path")
+    actions = input_args.get("actions", ["age", "gender", "emotion", "race"])
-    if img_path is None:
+    # actions is the only argument instance of list or tuple
-        return {"message": "you must pass img_path input"}
+    # if request is form data, input args can either be text or file
+    if isinstance(actions, str):
+        actions = (
+            actions.replace("[", "")
+            .replace("]", "")
+            .replace("(", "")
+            .replace(")", "")
+            .replace('"', "")
+            .replace("'", "")
+            .replace(" ", "")
+            .split(",")
+        )
 
     demographies = service.analyze(
-        img_path=img_path,
+        img_path=img,
-        actions=input_args.get("actions", ["age", "gender", "emotion", "race"]),
+        actions=actions,
         detector_backend=input_args.get("detector_backend", "opencv"),
         enforce_detection=input_args.get("enforce_detection", True),
         align=input_args.get("align", True),

deepface/api/src/modules/core/service.py

@@ -1,15 +1,22 @@
 # built-in dependencies
 import traceback
-from typing import Optional
+from typing import Optional, Union
+
+# 3rd party dependencies
+import numpy as np
 
 # project dependencies
 from deepface import DeepFace
+from deepface.commons.logger import Logger
+
+logger = Logger()
+
 
 # pylint: disable=broad-except
 
 
 def represent(
-    img_path: str,
+    img_path: Union[str, np.ndarray],
     model_name: str,
     detector_backend: str,
     enforce_detection: bool,
@@ -32,12 +39,14 @@ def represent(
         return result
     except Exception as err:
         tb_str = traceback.format_exc()
+        logger.error(str(err))
+        logger.error(tb_str)
         return {"error": f"Exception while representing: {str(err)} - {tb_str}"}, 400
 
 
 def verify(
-    img1_path: str,
+    img1_path: Union[str, np.ndarray],
-    img2_path: str,
+    img2_path: Union[str, np.ndarray],
     model_name: str,
     detector_backend: str,
     distance_metric: str,
@@ -59,11 +68,13 @@ def verify(
         return obj
     except Exception as err:
         tb_str = traceback.format_exc()
+        logger.error(str(err))
+        logger.error(tb_str)
         return {"error": f"Exception while verifying: {str(err)} - {tb_str}"}, 400
 
 
 def analyze(
-    img_path: str,
+    img_path: Union[str, np.ndarray],
     actions: list,
     detector_backend: str,
     enforce_detection: bool,
@@ -85,4 +96,6 @@ def analyze(
         return result
     except Exception as err:
         tb_str = traceback.format_exc()
+        logger.error(str(err))
+        logger.error(tb_str)
         return {"error": f"Exception while analyzing: {str(err)} - {tb_str}"}, 400

deepface/commons/image_utils.py

@@ -11,6 +11,7 @@ import requests
 import numpy as np
 import cv2
 from PIL import Image
+from werkzeug.datastructures import FileStorage
 
 
 def list_images(path: str) -> List[str]:
@@ -133,6 +134,21 @@ def load_image_from_base64(uri: str) -> np.ndarray:
     return img_bgr
 
 
+def load_image_from_file_storage(file: FileStorage) -> np.ndarray:
+    """
+    Loads an image from a FileStorage object and decodes it into an OpenCV image.
+    Args:
+        file (FileStorage): The FileStorage object containing the image file.
+    Returns:
+        img (np.ndarray): The decoded image as a numpy array (OpenCV format).
+    """
+    file_bytes = np.frombuffer(file.read(), np.uint8)
+    image = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
+    if image is None:
+        raise ValueError("Failed to decode image")
+    return image
+
+
 def load_image_from_web(url: str) -> np.ndarray:
     """
     Loading an image from web

requirements_local

@@ -3,4 +3,4 @@ pandas==2.0.3
 Pillow==9.0.0
 opencv-python==4.9.0.80
 tensorflow==2.13.1
-keras==2.13.1
+keras==2.13.1

tests/test_api.py

@@ -1,16 +1,29 @@
 # built-in dependencies
+import os
 import base64
 import unittest
 
+# 3rd party dependencies
+import gdown
+
 # project dependencies
 from deepface.api.src.app import create_app
 from deepface.commons.logger import Logger
 
 logger = Logger()
 
+IMG1_SOURCE = (
+    "https://raw.githubusercontent.com/serengil/deepface/refs/heads/master/tests/dataset/img1.jpg"
+)
+IMG2_SOURCE = (
+    "https://raw.githubusercontent.com/serengil/deepface/refs/heads/master/tests/dataset/img2.jpg"
+)
+
 
 class TestVerifyEndpoint(unittest.TestCase):
     def setUp(self):
+        download_test_images(IMG1_SOURCE)
+        download_test_images(IMG2_SOURCE)
         app = create_app()
         app.config["DEBUG"] = True
         app.config["TESTING"] = True
@@ -18,8 +31,8 @@ class TestVerifyEndpoint(unittest.TestCase):
 
     def test_tp_verify(self):
         data = {
-            "img1_path": "dataset/img1.jpg",
+            "img1": "dataset/img1.jpg",
-            "img2_path": "dataset/img2.jpg",
+            "img2": "dataset/img2.jpg",
         }
         response = self.app.post("/verify", json=data)
         assert response.status_code == 200
@@ -40,8 +53,8 @@ class TestVerifyEndpoint(unittest.TestCase):
 
     def test_tn_verify(self):
         data = {
-            "img1_path": "dataset/img1.jpg",
+            "img1": "dataset/img1.jpg",
-            "img2_path": "dataset/img2.jpg",
+            "img2": "dataset/img2.jpg",
         }
         response = self.app.post("/verify", json=data)
         assert response.status_code == 200
@@ -83,14 +96,11 @@ class TestVerifyEndpoint(unittest.TestCase):
     def test_represent_encoded(self):
         image_path = "dataset/img1.jpg"
         with open(image_path, "rb") as image_file:
-            encoded_string = "data:image/jpeg;base64," + \
+            encoded_string = "data:image/jpeg;base64," + base64.b64encode(image_file.read()).decode(
-                base64.b64encode(image_file.read()).decode("utf8")
+                "utf8"
+            )
 
-        data = {
+        data = {"model_name": "Facenet", "detector_backend": "mtcnn", "img": encoded_string}
-            "model_name": "Facenet",
-            "detector_backend": "mtcnn",
-            "img": encoded_string
-        }
 
         response = self.app.post("/represent", json=data)
         assert response.status_code == 200
@@ -112,7 +122,7 @@ class TestVerifyEndpoint(unittest.TestCase):
         data = {
             "model_name": "Facenet",
             "detector_backend": "mtcnn",
-            "img": "https://github.com/serengil/deepface/blob/master/tests/dataset/couple.jpg?raw=true"
+            "img": "https://github.com/serengil/deepface/blob/master/tests/dataset/couple.jpg?raw=true",
         }
 
         response = self.app.post("/represent", json=data)
@@ -155,8 +165,9 @@ class TestVerifyEndpoint(unittest.TestCase):
     def test_analyze_inputformats(self):
         image_path = "dataset/couple.jpg"
         with open(image_path, "rb") as image_file:
-            encoded_image = "data:image/jpeg;base64," + \
+            encoded_image = "data:image/jpeg;base64," + base64.b64encode(image_file.read()).decode(
-                base64.b64encode(image_file.read()).decode("utf8")
+                "utf8"
+            )
 
         image_sources = [
             # image path
@@ -164,7 +175,7 @@ class TestVerifyEndpoint(unittest.TestCase):
             # image url
             f"https://github.com/serengil/deepface/blob/master/tests/{image_path}?raw=true",
             # encoded image
-            encoded_image
+            encoded_image,
         ]
 
         results = []
@@ -189,25 +200,38 @@ class TestVerifyEndpoint(unittest.TestCase):
                 assert i.get("dominant_emotion") is not None
                 assert i.get("dominant_race") is not None
 
-        assert len(results[0]["results"]) == len(results[1]["results"])\
+        assert len(results[0]["results"]) == len(results[1]["results"]) and len(
-            and len(results[0]["results"]) == len(results[2]["results"])
+            results[0]["results"]
+        ) == len(results[2]["results"])
 
-        for i in range(len(results[0]['results'])):
+        for i in range(len(results[0]["results"])):
-            assert results[0]["results"][i]["dominant_emotion"] == results[1]["results"][i]["dominant_emotion"]\
+            assert (
-                and results[0]["results"][i]["dominant_emotion"] == results[2]["results"][i]["dominant_emotion"]
+                results[0]["results"][i]["dominant_emotion"]
+                == results[1]["results"][i]["dominant_emotion"]
+                and results[0]["results"][i]["dominant_emotion"]
+                == results[2]["results"][i]["dominant_emotion"]
+            )
 
-            assert results[0]["results"][i]["dominant_gender"] == results[1]["results"][i]["dominant_gender"]\
+            assert (
-                and results[0]["results"][i]["dominant_gender"] == results[2]["results"][i]["dominant_gender"]
+                results[0]["results"][i]["dominant_gender"]
+                == results[1]["results"][i]["dominant_gender"]
+                and results[0]["results"][i]["dominant_gender"]
+                == results[2]["results"][i]["dominant_gender"]
+            )
 
-            assert results[0]["results"][i]["dominant_race"] == results[1]["results"][i]["dominant_race"]\
+            assert (
-                and results[0]["results"][i]["dominant_race"] == results[2]["results"][i]["dominant_race"]
+                results[0]["results"][i]["dominant_race"]
+                == results[1]["results"][i]["dominant_race"]
+                and results[0]["results"][i]["dominant_race"]
+                == results[2]["results"][i]["dominant_race"]
+            )
 
         logger.info("✅ different inputs test is done")
 
     def test_invalid_verify(self):
         data = {
-            "img1_path": "dataset/invalid_1.jpg",
+            "img1": "dataset/invalid_1.jpg",
-            "img2_path": "dataset/invalid_2.jpg",
+            "img2": "dataset/invalid_2.jpg",
         }
         response = self.app.post("/verify", json=data)
         assert response.status_code == 400
@@ -227,3 +251,87 @@ class TestVerifyEndpoint(unittest.TestCase):
         }
         response = self.app.post("/analyze", json=data)
         assert response.status_code == 400
+
+    def test_analyze_for_multipart_form_data(self):
+        with open("/tmp/img1.jpg", "rb") as img_file:
+            response = self.app.post(
+                "/analyze",
+                content_type="multipart/form-data",
+                data={
+                    "img": (img_file, "test_image.jpg"),
+                    "actions": '["age", "gender"]',
+                    "detector_backend": "mtcnn",
+                },
+            )
+            assert response.status_code == 200
+            result = response.json
+            assert isinstance(result, dict)
+            assert result.get("age") is not True
+            assert result.get("dominant_gender") is not True
+            logger.info("✅ analyze api for multipart form data test is done")
+
+    def test_verify_for_multipart_form_data(self):
+        with open("/tmp/img1.jpg", "rb") as img1_file:
+            with open("/tmp/img2.jpg", "rb") as img2_file:
+                response = self.app.post(
+                    "/verify",
+                    content_type="multipart/form-data",
+                    data={
+                        "img1": (img1_file, "first_image.jpg"),
+                        "img2": (img2_file, "second_image.jpg"),
+                        "model_name": "Facenet",
+                        "detector_backend": "mtcnn",
+                        "distance_metric": "euclidean",
+                    },
+                )
+                assert response.status_code == 200
+                result = response.json
+                assert isinstance(result, dict)
+                assert result.get("verified") is not None
+                assert result.get("model") == "Facenet"
+                assert result.get("similarity_metric") is not None
+                assert result.get("detector_backend") == "mtcnn"
+                assert result.get("threshold") is not None
+                assert result.get("facial_areas") is not None
+
+                logger.info("✅ verify api for multipart form data test is done")
+
+    def test_represent_for_multipart_form_data(self):
+        with open("/tmp/img1.jpg", "rb") as img_file:
+            response = self.app.post(
+                "/represent",
+                content_type="multipart/form-data",
+                data={
+                    "img": (img_file, "first_image.jpg"),
+                    "model_name": "Facenet",
+                    "detector_backend": "mtcnn",
+                },
+            )
+            assert response.status_code == 200
+            result = response.json
+            assert isinstance(result, dict)
+            logger.info("✅ represent api for multipart form data test is done")
+
+    def test_represent_for_multipart_form_data_and_filepath(self):
+        response = self.app.post(
+            "/represent",
+            content_type="multipart/form-data",
+            data={
+                "img": "/tmp/img1.jpg",
+                "model_name": "Facenet",
+                "detector_backend": "mtcnn",
+            },
+        )
+        assert response.status_code == 200
+        result = response.json
+        assert isinstance(result, dict)
+        logger.info("✅ represent api for multipart form data and file path test is done")
+
+
+def download_test_images(url: str):
+    file_name = url.split("/")[-1]
+    target_file = f"/tmp/{file_name}"
+    if os.path.exists(target_file) is True:
+        return
+
+    gdown.download(url, target_file, quiet=False)