More WIP. Returns a sensible result now!

.gitignore

@@ -7,4 +7,4 @@ input_img.jpg
 app.py
 input_img.jpg
 requirements.txt
-__pycache__
+__pycache__

=1.12

@@ -1,14 +1,14 @@
 Requirement already satisfied: xtcocotools in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (1.14.3)
+Requirement already satisfied: numpy>=1.20.0 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from xtcocotools) (1.26.1)
 Requirement already satisfied: cython>=0.27.3 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from xtcocotools) (3.0.7)
+Requirement already satisfied: matplotlib>=2.1.0 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from xtcocotools) (3.8.1)
 Requirement already satisfied: setuptools>=18.0 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from xtcocotools) (60.2.0)
-Requirement already satisfied: numpy>=1.20.0 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from xtcocotools) (1.26.3)
-Requirement already satisfied: matplotlib>=2.1.0 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from xtcocotools) (3.8.2)
-Requirement already satisfied: pyparsing>=2.3.1 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from matplotlib>=2.1.0->xtcocotools) (3.1.1)
-Requirement already satisfied: fonttools>=4.22.0 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from matplotlib>=2.1.0->xtcocotools) (4.47.0)
-Requirement already satisfied: cycler>=0.10 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from matplotlib>=2.1.0->xtcocotools) (0.12.1)
+Requirement already satisfied: fonttools>=4.22.0 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from matplotlib>=2.1.0->xtcocotools) (4.43.1)
+Requirement already satisfied: pillow>=8 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from matplotlib>=2.1.0->xtcocotools) (10.1.0)
 Requirement already satisfied: packaging>=20.0 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from matplotlib>=2.1.0->xtcocotools) (23.2)
-Requirement already satisfied: python-dateutil>=2.7 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from matplotlib>=2.1.0->xtcocotools) (2.8.2)
-Requirement already satisfied: contourpy>=1.0.1 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from matplotlib>=2.1.0->xtcocotools) (1.2.0)
+Requirement already satisfied: contourpy>=1.0.1 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from matplotlib>=2.1.0->xtcocotools) (1.1.1)
 Requirement already satisfied: kiwisolver>=1.3.1 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from matplotlib>=2.1.0->xtcocotools) (1.4.5)
-Requirement already satisfied: pillow>=8 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from matplotlib>=2.1.0->xtcocotools) (10.2.0)
+Requirement already satisfied: pyparsing>=2.3.1 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from matplotlib>=2.1.0->xtcocotools) (3.1.1)
+Requirement already satisfied: python-dateutil>=2.7 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from matplotlib>=2.1.0->xtcocotools) (2.8.2)
+Requirement already satisfied: cycler>=0.10 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from matplotlib>=2.1.0->xtcocotools) (0.12.1)
 Requirement already satisfied: six>=1.5 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib>=2.1.0->xtcocotools) (1.16.0)

app.py

@@ -1,6 +1,7 @@
 from keypoints_extraction import predict_pose
 from calculate_measures import calculate_all_measures
 from calculate_masks import calculate_seg_mask
+from select_body_shape import select_body_shape
 
 import os
 os.system("pip install xtcocotools>=1.12")
@@ -24,19 +25,25 @@ 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)
-    side_rcnn_mask = calculate_seg_mask(side_img_path)
+    side_rcnn_mask = calculate_seg_mask(side_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.
 
-    measures = 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_rcnn_mask)
 
-    return (front_keypoint_result[0], front_keypoint_result[1], side_keypoint_result[0], side_keypoint_result[1])
+    # 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?
+    normalised_measures_data_frame = measures_data_frame
+
+    selected_body_shape = select_body_shape(normalised_measures_data_frame)
+
+    return (selected_body_shape)
 
 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")
-
-title = "MMPose detection for ShopByShape"
-iface = gr.Interface(fn=generate_output, inputs=[input_image_front, input_image_side], outputs=[output_image_front, output_text_front, output_image_side, output_text_side], title=title)
+# 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")
+
+title = "ShopByShape"
+iface = gr.Interface(fn=generate_output, inputs=[input_image_front, input_image_side], outputs=[output_body_shape], title=title)
 iface.launch()

body_shape_lookup.py

@@ -0,0 +1,19 @@
+# TODO: How do we know what the body shape is?
+
+def body_shape_lookup(index):
+    if index == 1:
+        return "Hourglass"
+    elif index == 2:
+        return "Triangle"
+    elif index == 3:
+        return "Inverted Triangle"
+    elif index == 4:
+        return "Rectangle"
+    elif index == 5:
+        return "Diamond"
+    elif index == 6:
+        return "Oval"
+    elif index == 7:
+        return "Round"
+    else:
+        return "Unknown"

body_shape_measures_normalised.csv

@@ -0,0 +1,11 @@
+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_updated.csv

@@ -0,0 +1,11 @@
+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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calculate_masks.py

@@ -1,4 +1,3 @@
-from PIL import Image
 from transformers import SegformerImageProcessor, AutoModelForSemanticSegmentation
 import torch.nn as nn
 
@@ -6,14 +5,6 @@ def calculate_seg_mask(image):
     processor = SegformerImageProcessor.from_pretrained("mattmdjaga/segformer_b2_clothes")
     model = AutoModelForSemanticSegmentation.from_pretrained("mattmdjaga/segformer_b2_clothes")
 
-    class_names = {
-        0: "Background", 1: "Hat", 2: "Hair", 3: "Sunglasses", 
-        4: "Upper-clothes", 5: "Skirt", 6: "Pants", 7: "Dress", 
-        8: "Belt", 9: "Left-shoe", 10: "Right-shoe", 11: "Face", 
-        12: "Left-leg", 13: "Right-leg", 14: "Left-arm", 15: "Right-arm", 
-        16: "Bag", 17: "Scarf"
-    }
-
     inputs = processor(images=image, return_tensors="pt")
 
     outputs = model(**inputs)
@@ -27,4 +18,4 @@ def calculate_seg_mask(image):
     )
 
     pred_seg = upsampled_logits.argmax(dim=1)[0]
-    return pred_seg
+    return pred_seg

calculate_measures.py

@@ -33,6 +33,9 @@ def get_volume_result(mask_of_interest, original_image, max_x, min_x, max_y, min
     return None
 
 def calculate_all_measures(front_image, side_image, front_keypoint_data, side_keypoint_data, front_seg_mask, side_rcnn_mask):
+    # Initialize an empty DataFrame
+    results = []
+
     results_dict = {}
 
     front_keypoints = front_keypoint_data['keypoints']
@@ -128,6 +131,10 @@ def calculate_all_measures(front_image, side_image, front_keypoint_data, side_ke
     results_dict['upper_torso_to_body_ratio'] = upper_torso_area / full_side_body_area
     results_dict['upper_torso_to_body_ratio'] = upper_torso_area / full_side_body_area
 
-    results_df = pd.DataFrame(results_dict)
+    # TODO: Temporary to force thigns to work.
+    results_dict['id'] = "1"
 
-    return results_dict
+    results.append(results_dict)
+    
+    results_df = pd.DataFrame(results)
+    return results_df

select_body_shape.py

@@ -0,0 +1,80 @@
+import re
+import numpy as np
+import pandas as pd
+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"
+VOLUNTEERS_MEASURES = "volunteers_measures_normalised_updated.csv"
+
+# selecting specific features
+RATIOS_TO_USE = ['shoulder_to_hip_distance',
+                 'hip_to_ankle_distance',
+                'thigh_to_torso_ratio_normalised',
+                'upper_to_lower_torso_normalised_ratio',
+                'shoulder_to_hip_ratio',
+                'thigh_to_body_ratio',
+                'upper_torso_to_body_ratio']
+
+def extract_digits(input_string):
+    # find digits in the format '1A' or '12B'
+    match = re.search(r'\d+', input_string)
+    if match:
+        return int(match.group())
+    else:
+        return -1 # not found
+
+def is_match(row):
+    # check whether there was a match for this record
+    # extract the user class from id
+    ground_truth = extract_digits(row['Volunteer_ID'])
+    return ground_truth == row['Rank_1_Body_Shape'] or ground_truth == row['Rank_2_Body_Shape'] or ground_truth == row['Rank_3_Body_Shape']
+
+def select_body_shape(normalised_body_shape_measures):
+    # load the body shape measures
+    body_shape_df = pd.read_csv(BODY_SHAPE_MEASURES)
+    # body_shape_df = normalised_body_shape_measures
+
+    # load the volunteers measures
+    # volunteers_df = pd.read_csv(VOLUNTEERS_MEASURES)
+    volunteers_df = normalised_body_shape_measures
+
+    # select only the columns corresponding to the ratios
+    body_shape_ratios = body_shape_df[RATIOS_TO_USE]
+
+    # Create a DataFrame to store the results
+    results_df = pd.DataFrame(columns=["Volunteer_ID", "Rank_1_Body_Shape", "Score_1",
+                                       "Rank_2_Body_Shape", "Score_2",
+                                       "Rank_3_Body_Shape", "Score_3"])
+
+    # calculate euclidean distance for each volunteer
+    for index, volunteer_row in volunteers_df.iterrows():
+        print(f"\nProcessing volunteer {volunteer_row['id']}")
+        volunteer_ratios = volunteer_row[RATIOS_TO_USE]
+
+        top_scores = [(-1000, 'n/a')] * 3
+
+        for body_index, body_shape_row in body_shape_ratios.iterrows():
+            # euclidean distance
+            # similarity = np.linalg.norm(volunteer_ratios - body_shape_row)
+            # calculate cosine similarity
+            similarity = dot(volunteer_ratios, body_shape_row) / (norm(volunteer_ratios)*norm(body_shape_row))
+
+            # Check if the current score is among the top 3
+            for i, (score, _) in enumerate(top_scores):
+                if similarity > score:
+                    top_scores.insert(i, (similarity, body_index + 1))
+                    top_scores = top_scores[:3]  
+                    break
+
+            print(f"Volunteer {volunteer_row['id']} (body shape {body_index + 1}) Similarity:\t{similarity:.3f}")
+
+        # 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:")
+        for i, (score, body_shape) in enumerate(top_scores):
+            print(f"Rank {i + 1}: Body Shape {body_shape} with score {score:.3f}")
+
+        body_shape_index = top_scores[0][1]
+
+        return body_shape_lookup(body_shape_index)

volunteers_measures_normalised_updated.csv

@@ -0,0 +1,11 @@
+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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