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algorithm/train_src/estimate_gender.py

@@ -9,7 +9,7 @@ from path_constants import abs_path_imageai_model
 from algorithm.train_src.new_straightening import _ACCESS_TF_AND_GPU_SEMA
 
 EXTRACT_DETECTED_OBJECTS = False
-MINIMUM_PERCENTAGE_PROBABILITY = 50
+MINIMUM_PERCENTAGE_PROBABILITY = 0.1
 NMS_TRESHOLD = 0.4
 DISPLAY_PERCENTAGE_PROBABILITY = True
 DISPLAY_OBJECT_NAME = True
@@ -120,6 +120,8 @@ def run_estimation(detection_model_path: str = None, configuration_json: str = N
 
         # find the best estimation:
         seq = [x['percentage_probability'] for x in output_objects_array]
+        gender = None
+        if len(seq)>0:
             index = seq.index(max(seq))
             gender = output_objects_array[index]
         # have to append because for some reason I can't x = [1,2,3]
@@ -179,7 +181,7 @@ def estimate_image(detection_model_path: str = abs_path_imageai_model,
             p = Process(target=run_estimation, args=args)
             p.start()
             p.join()
-            if return_dict[0]['name']:
+            if return_dict[0]:
                 gender = return_dict[0]['name']
             return gender, return_dict[1], return_dict[2], return_dict[3]
         return gender, None, None, None

algorithm/train_src/estimate_gender_demo.py

@@ -5,7 +5,7 @@ from path_constants import abs_path_temp_images
 
 
 def test():
-	img_path = abs_path_temp_images + "/aspectTest.jpg"
+	img_path = abs_path_temp_images + "/2.png"
 	execution_path = os.getcwd()
 	img = cv2.imread(img_path)
 	if None is not img:

api/endpoints/findsalamanderinfo.py

@@ -38,7 +38,8 @@ class FindSalamanderInfo(Resource):
                     str_image = cv2.cvtColor(str_image, cv2.COLOR_BGR2RGB)
                     cv2.imwrite(os.path.join(temp_img_path, str(user_id) + "_str." + image_type), str_image)
                     encoded = encode(os.path.join(temp_img_path, str(user_id) + "_str." + image_type))[2:-1]
-                    gender, _, _, _ = estimate_image(abs_path_imageai_model,abs_path_imageai_config)
+                    gender, _, _, _ = estimate_image(abs_path_imageai_model,abs_path_imageai_config, img)
+                    print(gender)
                     return jsonify({"sex": gender, "species": "AI_species", "status": 200, "image": encoded})
 
                 else: