Improvements after call with Peter.

README.md

@@ -11,3 +11,11 @@ license: apache-2.0
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+## Usage
+
+```python app.py```
+
+## Notes
+
+body_shape_measures_normalised is not being used because the input can't be normalised. Keeping it for future reference.

app.py

@@ -5,7 +5,7 @@ os.system("pip install 'mmcv>=2.0.0rc4,<2.1.0'")
 os.system("pip install 'mmdet>=3.0.0,<4.0.0'")
 os.system("pip install 'mmpose'")
 
-from keypoints_extraction import predict_pose
+from keypoints_extraction import predict_poses
 from calculate_measures import calculate_all_measures
 from calculate_masks import calculate_seg_mask
 from select_body_shape import select_body_shape
@@ -14,10 +14,10 @@ import gradio as gr
 
 def generate_output(front_img_path, side_img_path):
     # TODO: These file names will need to be unique in case of multiple requests at once, and they will need to be deleted after the function is done.
-    front_keypoint_result = predict_pose(front_img_path, "front.jpg")
-    side_keypoint_result = predict_pose(side_img_path, "side.jpg")
+    keypoint_results = predict_poses(front_img_path, "front.jpg", side_img_path, "side.jpg")
+    front_keypoint_result = keypoint_results[0]
+    side_keypoint_result = keypoint_results[1]
 
-    # TODO: LOW PRIORITY: Should we create the image separately? Seems weird to get it as a result from this only to use it in something else below.
     front_image = front_keypoint_result[0]
     side_image = side_keypoint_result[0]
 
@@ -25,28 +25,24 @@ def generate_output(front_img_path, side_img_path):
     side_keypoint_data = side_keypoint_result[1]
 
     front_seg_mask = calculate_seg_mask(front_img_path)
-    # TODO: Is this the correct mask? In the original code there is a function called 'get_rcnn_mask' which is not used anywhere. 
-    # The name implies that it should be a rcnn mask, but the code actually requests a seg mask.
-    # If we don't need a rcnn mask, then delete the rcnn code from calculate_masks.py and rename side_rcnn_mask to side_seg_mask.
-    side_rcnn_mask = calculate_seg_mask(side_img_path)
+    side_seg_mask = calculate_seg_mask(side_img_path)
 
-    measures_data_frame = calculate_all_measures(front_image, side_image, front_keypoint_data, side_keypoint_data, front_seg_mask, side_rcnn_mask)
+    measures_data_frame = calculate_all_measures(front_image, side_image, front_keypoint_data, side_keypoint_data, front_seg_mask, side_seg_mask)
 
-    # TODO: Normalise the measures somehow? Don't understand how this works yet if it is for a single person. Do we need to do this? Or not?
+    # We can't normalise the measures because we don't have multiple images in this case, so just use the measures directly.
     normalised_measures_data_frame = measures_data_frame
 
-    selected_body_shape = select_body_shape(normalised_measures_data_frame)
+    body_shape_result = select_body_shape(normalised_measures_data_frame)
+    selected_body_shape = body_shape_result[0]
+    calculation_information = body_shape_result[1]
 
-    return (selected_body_shape)
+    return (selected_body_shape, calculation_information)
 
 input_image_front = gr.inputs.Image(type='pil', label="Front Image")
 input_image_side = gr.inputs.Image(type='pil', label="Side Image")
-# output_image_front = gr.outputs.Image(type="pil", label="Front Output Image")
-# output_text_front = gr.outputs.Textbox(label="Front Output Text")
-# output_image_side = gr.outputs.Image(type="pil", label="Front Output Image")
-# output_text_side = gr.outputs.Textbox(label="Side Output Text")
 output_body_shape = gr.outputs.Textbox(label="Body Shape")
+output_calculation_information = gr.outputs.Textbox(label="Calculation Information")
 
 title = "ShopByShape"
-iface = gr.Interface(fn=generate_output, inputs=[input_image_front, input_image_side], outputs=[output_body_shape], title=title)
+iface = gr.Interface(fn=generate_output, inputs=[input_image_front, input_image_side], outputs=[output_body_shape, output_calculation_information], title=title)
 iface.launch()

body_shape_lookup.py

@@ -2,18 +2,28 @@
 
 def body_shape_lookup(index):
     if index == 1:
-        return "Hourglass"
+        return "Skittle"
     elif index == 2:
-        return "Triangle"
+        return "Goblet"
     elif index == 3:
-        return "Inverted Triangle"
+        return "Hourglass"
     elif index == 4:
-        return "Rectangle"
+        return "Cornet"
     elif index == 5:
-        return "Diamond"
+        return "Cello"
     elif index == 6:
-        return "Oval"
+        return "Apple"
     elif index == 7:
-        return "Round"
+        return "Column"
+    elif index == 8:
+        return "Bell"
+    elif index == 9:
+        return "Vase"
+    elif index == 10:
+        return "Brick"
+    elif index == 11:
+        return "Lollipop"
+    elif index == 12:
+        return "Pear"
     else:
         return "Unknown"

body_shape_measures.csv

@@ -0,0 +1,13 @@
+id,shoulder_width,hip_width,shoulder_to_hip_distance,hip_to_ankle_distance,torso_to_leg_ratio,waist_width,thigh_area,torso_area,lower_torso_area,upper_torso_area,full_side_body_area,thigh_normalised,torso_normalised,thigh_to_torso_ratio_normalised,thigh_to_torso_ratio,upper_torso_normalised,lower_torso_normalised,upper_to_lower_torso_normalised_ratio,upper_to_lower_torso_ratio,shoulder_to_hip_ratio,shoulder_to_waist_ratio,waist_to_hip_ratio,thigh_to_body_ratio,upper_torso_to_body_ratio,front_img_jpg,front_img_json,side_img_jpg,side_img_json
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body_shape_measures_normalised.csv

@@ -1,11 +0,0 @@
-id,shoulder_width,hip_width,shoulder_to_hip_distance,hip_to_ankle_distance,torso_to_leg_ratio,waist_width,thigh_area,torso_area,lower_torso_area,upper_torso_area,full_side_body_area,thigh_normalised,torso_normalised,thigh_to_torso_ratio_normalised,thigh_to_torso_ratio,upper_torso_normalised,lower_torso_normalised,upper_to_lower_torso_normalised_ratio,upper_to_lower_torso_ratio,shoulder_to_hip_ratio,shoulder_to_waist_ratio,waist_to_hip_ratio,thigh_to_body_ratio,upper_torso_to_body_ratio,front_img_jpg,front_img_json,side_img_jpg,side_img_json
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-12A,669,672,783.056640625,1269.091796875,0.6170212765957447,567,211624,335094,170606,164488,813075,90.92243084724528,143.97025404645413,0.6315362256560846,0.6315362256560846,70.67085399199374,73.2994000544604,0.9641395964971924,0.9641395964971924,0.9955357142857143,1.17989417989418,0.84375,0.26027611228976416,0.20230360052885651,data/volunteers_full_res/12. A front.jpg,data/volunteers_full_res/keypoints/12. A front_keypoints.json,data/volunteers_full_res/12. A side.jpg,data/volunteers_full_res/keypoints/12. A side_keypoints.json

body_shape_measures_normalised_updated.csv

@@ -1,11 +0,0 @@
-id,shoulder_width,hip_width,shoulder_to_hip_distance,hip_to_ankle_distance,torso_to_leg_ratio,waist_width,thigh_area,torso_area,lower_torso_area,upper_torso_area,full_side_body_area,thigh_normalised,torso_normalised,thigh_to_torso_ratio_normalised,thigh_to_torso_ratio,upper_torso_normalised,lower_torso_normalised,upper_to_lower_torso_normalised_ratio,upper_to_lower_torso_ratio,shoulder_to_hip_ratio,shoulder_to_waist_ratio,waist_to_hip_ratio,thigh_to_body_ratio,upper_torso_to_body_ratio,front_img_jpg,front_img_json,side_img_jpg,side_img_json
-2A,309,269,-0.6451853254425501,-0.6682325556525112,0.3512052843480335,235,34229,50778,25191,25587,129184,-0.5136746421535362,-0.6393085847116429,0.5341966631577374,0.5341966631577363,-0.6530399046830264,-0.6235671698990638,-0.18048946872604607,-0.18048946872604604,0.46262354383444276,-0.038443926936658726,0.2848605088833572,0.11603850407389217,-0.6352860522185653,data/volunteers_full_res/2. A front.JPG,data/volunteers_full_res/keypoints/2. A front_keypoints.json,data/volunteers_full_res/2. A side.JPG,data/volunteers_full_res/keypoints/2. A side_keypoints.json
-5A,751,784,0.9144271139787139,1.2087976272001555,-1.393368840086257,820,359036,478409,244104,234305,1317742,1.7908025462101405,1.6581076973120041,1.3373436638658922,1.3373436638658922,1.5898707794468867,1.7184597534399404,-0.7675499961012099,-0.7675499961012098,-1.6885839094884345,-2.105222050862348,2.2088413671422513,0.4115935545489668,-1.3509388020203796,data/volunteers_full_res/5. A front.jpg,data/volunteers_full_res/keypoints/5. A front_keypoints.json,data/volunteers_full_res/5. A side.jpg,data/volunteers_full_res/keypoints/5. A side_keypoints.json
-7A,888,771,1.2266563354411533,1.4798058058940855,-1.3175843564008711,549,252555,338580,163668,174912,874524,0.9620258960628703,0.8055256743012406,1.2894518062332987,1.2894518062332998,0.8862023059241599,0.7239466229852285,0.3762791226520531,0.37627912265205304,0.49703650905427676,1.5287976601007285,-1.5188804555519377,1.0550592184153258,-0.5666856621104159,data/volunteers_full_res/7. A front.jpg,data/volunteers_full_res/keypoints/7. A front_keypoints.json,data/volunteers_full_res/7. A side.jpg,data/volunteers_full_res/keypoints/7. A side_keypoints.json
-7B,718,696,1.4860059660807992,1.0493591618169957,0.01163954223114654,537,252642,372623,181265,191358,979634,0.8963355830168163,0.9892525726734528,0.5753963012377995,0.5753963012377995,1.0609718924822236,0.9157212305526141,0.2394609133057007,0.23946091330570068,-0.8575885214005697,0.07635234591621287,-0.8546416455323987,-0.16253115903080398,-0.7317305165035971,data/volunteers_full_res/7. B front.jpg,data/volunteers_full_res/keypoints/7. B front_keypoints.json,data/volunteers_full_res/7. B side.jpg,data/volunteers_full_res/keypoints/7. B side_keypoints.json
-7C,233,198,-0.7858354516068623,-0.8660844259672597,1.3774253575446744,160,21697,39647,20911,18736,72801,-0.6955531203216642,-0.6341185908976849,-0.7993686079294058,-0.7993686079294058,-0.7209444855652456,-0.5471821012653706,-1.4387549608901573,-1.4387549608901582,0.7791053627807573,0.6936939800717078,-0.4467705325621465,1.4191973718580178,1.4593062364358502,data/volunteers_full_res/7. C front.JPG,data/volunteers_full_res/keypoints/7. C front_keypoints.json,data/volunteers_full_res/7. C side.JPG,data/volunteers_full_res/keypoints/7. C side_keypoints.json
-9A,211,178,-0.8536044015374634,-0.9242373480961905,1.5839544420431042,141,13005,29217,12972,16245,63173,-0.9885722682153351,-0.8866846225820019,-1.8732405871833364,-1.8732405871833364,-0.810033330890077,-0.9580863087680326,2.305864794991042,2.3058647949910416,0.8763606978908735,0.9019251126074833,-0.6248177905120642,-2.212964050626634,1.4519546942072203,data/volunteers_full_res/9. A front.JPG,data/volunteers_full_res/keypoints/9. A front_keypoints.json,data/volunteers_full_res/9. A side.JPG,data/volunteers_full_res/keypoints/9. A side_keypoints.json
-9B,368,314,-0.2627907979699269,-0.24282650808339729,-0.40226526773322185,282,54350,88963,42901,46062,225792,-0.4295591307213896,-0.4121599519583041,-0.12990166261860167,-0.12990166261860167,-0.3829103075755036,-0.4391335397560143,0.42862672647856803,0.4286267264785703,0.7250614892098834,-0.08987035878672849,0.558231717572826,-0.8397958260534285,-0.4256038637053921,data/volunteers_full_res/9. B front.jpg,data/volunteers_full_res/keypoints/9. B front_keypoints.json,data/volunteers_full_res/9. B side.jpg,data/volunteers_full_res/keypoints/9. B side_keypoints.json
-10A,189,179,-0.967607048096815,-0.8516730560853955,-0.2573670846406723,155,13346,21964,10953,11011,49817,-0.9068557521025037,-1.0223264573211466,-0.16457139639188673,-0.16457139639188673,-1.0474087761570212,-0.9941101434453615,-0.29004106968943183,-0.2900410696894318,-0.5840885829069261,-0.5332752158903319,0.19906183414420206,0.2317937959738227,0.1758982832290134,data/volunteers_full_res/10. A front.JPG,data/volunteers_full_res/keypoints/10. A front_keypoints.json,data/volunteers_full_res/10. A side.JPG,data/volunteers_full_res/keypoints/10. A side_keypoints.json
-11A,203,169,-0.9716968291393029,-0.9277894463065092,0.5540627406776014,147,16750,30912,15171,15741,63617,-0.871621034320157,-0.8781135022156633,-0.8560787634674033,-0.8560787634674033,-0.886623370048677,-0.866576433381505,0.04915099181033875,0.049150991810338746,1.0543983860547377,0.3036992727189106,0.24261567283875252,0.050276534651395643,1.1086845891110646,data/volunteers_full_res/11. A front.jpg,data/volunteers_full_res/keypoints/11. A front_keypoints.json,data/volunteers_full_res/11. A side.jpg,data/volunteers_full_res/keypoints/11. A side_keypoints.json
-12A,669,672,0.8596304382922542,0.7428807452800266,-0.5077018179835446,567,211624,335094,170606,164488,813075,0.7566719225447575,1.0198257653997465,0.08677258309590243,0.08677258309590243,0.9639151970662792,1.070528089537564,-0.7225470538308504,-0.7225470538308503,-1.2643249750290122,-0.7376568189389741,-0.04850067642283835,-0.06866794381056764,-0.48559890642479764,data/volunteers_full_res/12. A front.jpg,data/volunteers_full_res/keypoints/12. A front_keypoints.json,data/volunteers_full_res/12. A side.jpg,data/volunteers_full_res/keypoints/12. A side_keypoints.json

keypoints_extraction.py

@@ -6,21 +6,27 @@ from mmpose.apis import inference_topdown, init_model
 from mmpose.utils import register_all_modules
 register_all_modules()
 
+# coco keypoints: 
+# https://github.com/open-mmlab/mmpose/blob/master/mmpose/datasets/datasets/top_down/topdown_coco_dataset.py#L28
+model = init_model('mmpose/td-hm_hrnet-w48_8xb32-210e_coco-256x192.py', 'mmpose/td-hm_hrnet-w48_8xb32-210e_coco-256x192-0e67c616_20220913.pth', device=torch.cuda.current_device() if torch.cuda.is_available() else 'cpu')
+
 def save_image(img, img_path):
     # Convert PIL image to OpenCV image
     img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
     # Save OpenCV image
     cv2.imwrite(img_path, img)
 
-def predict_pose(img, img_path):
-    save_image(img, img_path)
+def predict_poses(front_img, front_img_path, side_img, side_img_path):
+    save_image(front_img, front_img_path)
+    save_image(side_img, side_img_path)
 
-    result = mmpose_coco(img_path)
-    keypoints = result[0].pred_instances['keypoints'][0]
+    mmpose_result = mmpose_coco(front_img_path, side_img_path)
+    front_result = mmpose_result[0]
+    side_result = mmpose_result[1]
 
-    # Create a dictionary to store keypoints and their names
-    keypoints_data = {
-        'keypoints': keypoints.tolist(),
+    front_keypoints = front_result[0].pred_instances['keypoints'][0]
+    front_keypoints_data = {
+        'keypoints': front_keypoints.tolist(),
         'keypoint_names': [
             'nose',
             'left_eye',
@@ -41,14 +47,37 @@ def predict_pose(img, img_path):
             'right_ankle'
         ]
     }
-    return (img, keypoints_data)
-
-def mmpose_coco(img_path, 
-                config_file = 'mmpose/td-hm_hrnet-w48_8xb32-210e_coco-256x192.py', 
-                checkpoint_file = 'mmpose/td-hm_hrnet-w48_8xb32-210e_coco-256x192-0e67c616_20220913.pth'):
-    device = torch.cuda.current_device() if torch.cuda.is_available() else 'cpu'
-    # coco keypoints: 
-    # https://github.com/open-mmlab/mmpose/blob/master/mmpose/datasets/datasets/top_down/topdown_coco_dataset.py#L28
-    model = init_model(config_file, checkpoint_file, device=device)
-    results = inference_topdown(model, img_path)
-    return results
+    
+    side_keypoints = side_result[0].pred_instances['keypoints'][0]
+    side_keypoints_data = {
+        'keypoints': side_keypoints.tolist(),
+        'keypoint_names': [
+            'nose',
+            'left_eye',
+            'right_eye',
+            'left_ear',
+            'right_ear',
+            'left_shoulder',
+            'right_shoulder',
+            'left_elbow',
+            'right_elbow',
+            'left_wrist',
+            'right_wrist',
+            'left_hip',
+            'right_hip',
+            'left_knee',
+            'right_knee',
+            'left_ankle',
+            'right_ankle'
+        ]
+    }
+
+    return ((front_img, front_keypoints_data), (side_img, side_keypoints_data))
+
+def mmpose_coco(front_img_path,
+                side_img_path):
+
+    front_results = inference_topdown(model, front_img_path)
+    side_results = inference_topdown(model, side_img_path)
+
+    return (front_results, side_results)

select_body_shape.py

@@ -4,7 +4,7 @@ from numpy import dot
 from numpy.linalg import norm
 from body_shape_lookup import body_shape_lookup
 
-BODY_SHAPE_MEASURES = "body_shape_measures_normalised_updated.csv"
+BODY_SHAPE_MEASURES = "body_shape_measures.csv"
 
 # selecting specific features
 RATIOS_TO_USE = ['shoulder_to_hip_distance',
@@ -39,6 +39,8 @@ def select_body_shape(normalised_body_shape_measures):
     # select only the columns corresponding to the ratios
     body_shape_ratios = body_shape_df[RATIOS_TO_USE]
 
+    calculation_information = ""
+
     # calculate euclidean distance for each volunteer
     for index, volunteer_row in volunteers_df.iterrows():
         print(f"\nProcessing volunteer {volunteer_row['id']}")
@@ -59,13 +61,15 @@ def select_body_shape(normalised_body_shape_measures):
                     top_scores = top_scores[:3]  
                     break
 
-            print(f"Volunteer {volunteer_row['id']} (body shape {body_index + 1}) Similarity:\t{similarity:.3f}")
+            print(f"(body shape {body_index + 1}) Similarity:\t{similarity:.3f}")
+            calculation_information += f"(body shape {body_index + 1}) Similarity:\t{similarity:.3f}\n"
 
         # Print the top 3 best body shapes and scores for the current volunteer
-        print(f"Volunteer {volunteer_row['id']} top 3 body shapes and scores are:")
+        print(f"Body shapes and scores are:")
         for i, (score, body_shape) in enumerate(top_scores):
             print(f"Rank {i + 1}: Body Shape {body_shape} with score {score:.3f}")
+            calculation_information += f"Rank {i + 1}: Body Shape {body_shape} with score {score:.3f}\n"
 
         body_shape_index = top_scores[0][1]
 
-        return body_shape_lookup(body_shape_index)
+        return (body_shape_lookup(body_shape_index), calculation_information)

volunteers_measures_normalised_updated.csv

@@ -1,11 +0,0 @@
-id,shoulder_width,hip_width,shoulder_to_hip_distance,hip_to_ankle_distance,torso_to_leg_ratio,waist_width,thigh_area,torso_area,lower_torso_area,upper_torso_area,full_side_body_area,thigh_normalised,torso_normalised,thigh_to_torso_ratio_normalised,thigh_to_torso_ratio,upper_torso_normalised,lower_torso_normalised,upper_to_lower_torso_normalised_ratio,upper_to_lower_torso_ratio,shoulder_to_hip_ratio,shoulder_to_waist_ratio,waist_to_hip_ratio,thigh_to_body_ratio,upper_torso_to_body_ratio,front_img_jpg,front_img_json,side_img_jpg,side_img_json
-2A,309,269,-0.6451853254425501,-0.6682325556525112,0.3512052843480335,235,34229,50778,25191,25587,129184,-0.5136746421535362,-0.6393085847116429,0.5341966631577374,0.5341966631577363,-0.6530399046830264,-0.6235671698990638,-0.18048946872604607,-0.18048946872604604,0.46262354383444276,-0.038443926936658726,0.2848605088833572,0.11603850407389217,-0.6352860522185653,data/volunteers_full_res/2. A front.JPG,data/volunteers_full_res/keypoints/2. A front_keypoints.json,data/volunteers_full_res/2. A side.JPG,data/volunteers_full_res/keypoints/2. A side_keypoints.json
-5A,751,784,0.9144271139787139,1.2087976272001555,-1.393368840086257,820,359036,478409,244104,234305,1317742,1.7908025462101405,1.6581076973120041,1.3373436638658922,1.3373436638658922,1.5898707794468867,1.7184597534399404,-0.7675499961012099,-0.7675499961012098,-1.6885839094884345,-2.105222050862348,2.2088413671422513,0.4115935545489668,-1.3509388020203796,data/volunteers_full_res/5. A front.jpg,data/volunteers_full_res/keypoints/5. A front_keypoints.json,data/volunteers_full_res/5. A side.jpg,data/volunteers_full_res/keypoints/5. A side_keypoints.json
-7A,888,771,1.2266563354411533,1.4798058058940855,-1.3175843564008711,549,252555,338580,163668,174912,874524,0.9620258960628703,0.8055256743012406,1.2894518062332987,1.2894518062332998,0.8862023059241599,0.7239466229852285,0.3762791226520531,0.37627912265205304,0.49703650905427676,1.5287976601007285,-1.5188804555519377,1.0550592184153258,-0.5666856621104159,data/volunteers_full_res/7. A front.jpg,data/volunteers_full_res/keypoints/7. A front_keypoints.json,data/volunteers_full_res/7. A side.jpg,data/volunteers_full_res/keypoints/7. A side_keypoints.json
-7B,718,696,1.4860059660807992,1.0493591618169957,0.01163954223114654,537,252642,372623,181265,191358,979634,0.8963355830168163,0.9892525726734528,0.5753963012377995,0.5753963012377995,1.0609718924822236,0.9157212305526141,0.2394609133057007,0.23946091330570068,-0.8575885214005697,0.07635234591621287,-0.8546416455323987,-0.16253115903080398,-0.7317305165035971,data/volunteers_full_res/7. B front.jpg,data/volunteers_full_res/keypoints/7. B front_keypoints.json,data/volunteers_full_res/7. B side.jpg,data/volunteers_full_res/keypoints/7. B side_keypoints.json
-7C,233,198,-0.7858354516068623,-0.8660844259672597,1.3774253575446744,160,21697,39647,20911,18736,72801,-0.6955531203216642,-0.6341185908976849,-0.7993686079294058,-0.7993686079294058,-0.7209444855652456,-0.5471821012653706,-1.4387549608901573,-1.4387549608901582,0.7791053627807573,0.6936939800717078,-0.4467705325621465,1.4191973718580178,1.4593062364358502,data/volunteers_full_res/7. C front.JPG,data/volunteers_full_res/keypoints/7. C front_keypoints.json,data/volunteers_full_res/7. C side.JPG,data/volunteers_full_res/keypoints/7. C side_keypoints.json
-9A,211,178,-0.8536044015374634,-0.9242373480961905,1.5839544420431042,141,13005,29217,12972,16245,63173,-0.9885722682153351,-0.8866846225820019,-1.8732405871833364,-1.8732405871833364,-0.810033330890077,-0.9580863087680326,2.305864794991042,2.3058647949910416,0.8763606978908735,0.9019251126074833,-0.6248177905120642,-2.212964050626634,1.4519546942072203,data/volunteers_full_res/9. A front.JPG,data/volunteers_full_res/keypoints/9. A front_keypoints.json,data/volunteers_full_res/9. A side.JPG,data/volunteers_full_res/keypoints/9. A side_keypoints.json
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