add app and requirements

app.py

@@ -0,0 +1,123 @@
+import torch
+import numpy as np
+import cv2
+import json
+import os
+import gradio as gr
+from detectron2.engine import DefaultPredictor
+from detectron2.config import get_cfg
+from detectron2 import model_zoo
+
+# Create output directory if it doesn't exist
+output_dir = "key/"
+os.makedirs(output_dir, exist_ok=True)
+output_file = os.path.join(output_dir, "keypoints.json")
+
+# Load pre-trained Keypoint R-CNN model
+cfg = get_cfg()
+cfg.merge_from_file(model_zoo.get_config_file("COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml"))
+cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml")
+cfg.MODEL.DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Load the predictor
+predictor = DefaultPredictor(cfg)
+
+def process_image(image, user_height_cm):
+    # Convert Gradio image input to OpenCV format
+    image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
+
+    # Run keypoint detection
+    outputs = predictor(image)
+
+    # Extract keypoints
+    instances = outputs["instances"]
+    keypoints = instances.pred_keypoints.cpu().numpy().tolist() if instances.has("pred_keypoints") else None
+
+    if not keypoints:
+        return "No keypoints detected.", None
+
+    # Save keypoints to JSON
+    with open(output_file, "w") as f:
+        json.dump({"keypoints": keypoints}, f, indent=4)
+
+    keypoints = np.array(keypoints[0])[:, :2]  # Extract (x, y) coordinates
+
+    # COCO format indices
+    NOSE, L_SHOULDER, R_SHOULDER = 0, 5, 6
+    L_ELBOW, R_ELBOW = 7, 8
+    L_WRIST, R_WRIST = 9, 10
+    L_HIP, R_HIP = 11, 12
+    L_ANKLE, R_ANKLE = 15, 16
+
+    # Define Keypoint Pairs for Drawing Lines (COCO Format)
+    skeleton = [(5, 6), (5, 11), (6, 12), (11, 12)]
+
+    # Draw Keypoints
+    for x, y in keypoints:
+        cv2.circle(image, (int(x), int(y)), 5, (0, 255, 0), -1)
+
+    # Draw Skeleton
+    for pt1, pt2 in skeleton:
+        x1, y1 = map(int, keypoints[pt1])
+        x2, y2 = map(int, keypoints[pt2])
+        cv2.line(image, (x1, y1), (x2, y2), (255, 0, 0), 2)
+
+    # Function to calculate Euclidean distance
+    def get_distance(p1, p2):
+        return np.linalg.norm(np.array(p1) - np.array(p2))
+
+    # Calculate full height (consider head length)
+    ankle_mid = ((keypoints[L_ANKLE] + keypoints[R_ANKLE]) / 2).tolist()
+    pixel_height = get_distance(keypoints[NOSE], ankle_mid)
+
+    # Estimated full body height (add approx head length)
+    estimated_full_pixel_height = pixel_height / 0.87  # Since 87% = nose to ankle
+    pixels_per_cm = estimated_full_pixel_height / user_height_cm
+
+    # Waist and shoulder measurements
+    shoulder_width_px = get_distance(keypoints[L_SHOULDER], keypoints[R_SHOULDER])
+    waist_width_px = get_distance(keypoints[L_HIP], keypoints[R_HIP])
+
+    # Convert to cm
+    shoulder_width_cm = shoulder_width_px / pixels_per_cm
+    waist_width_cm = waist_width_px / pixels_per_cm
+
+    # Torso Length (Neck to Pelvis)
+    pelvis = ((keypoints[L_HIP] + keypoints[R_HIP]) / 2).tolist()
+    neck = ((keypoints[L_SHOULDER] + keypoints[R_SHOULDER]) / 2).tolist()
+    torso_length_px = get_distance(neck, pelvis)
+    torso_length_cm = torso_length_px / pixels_per_cm
+
+    # Arm Length (Shoulder to Wrist)
+    arm_length_px = get_distance(keypoints[L_SHOULDER], keypoints[L_WRIST])
+    arm_length_cm = arm_length_px / pixels_per_cm
+
+    # Calculate waist and hip circumference (Ellipse approximation)
+    # Waist circumference ≈ π × (waist_width / 2) × 2
+    waist_circumference = np.pi * waist_width_cm
+    hip_circumference = waist_circumference / 0.75  # Assuming hip is slightly bigger than waist
+
+    # Improved body measurement calculation
+    def calculate_body_measurements(waist_circumference, hip_circumference, shoulder_width_cm, torso_length_cm, arm_length_cm):
+        return {
+            "Waist Circumference (cm)": round(waist_circumference, 2),
+            "Hip Circumference (cm)": round(hip_circumference, 2),
+            "Shoulder Width (cm)": round(shoulder_width_cm, 2),
+            "Torso Length (Neck to Pelvis, cm)": round(torso_length_cm, 2),
+            "Full Arm Length (Shoulder to Wrist, cm)": round(arm_length_cm, 2),
+        }
+
+    measurements = calculate_body_measurements(waist_circumference, hip_circumference, shoulder_width_cm, torso_length_cm, arm_length_cm)
+
+    return measurements, cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+# Gradio Interface
+demo = gr.Interface(
+    fn=process_image,
+    inputs=[gr.Image(type="pil"), gr.Number(label="User Height (cm)")],
+    outputs=[gr.JSON(label="Measurements"), gr.Image(type="pil", label="Keypoint Overlay")],
+    title="Keypoint Measurement Extractor",
+    description="Upload an image, enter your height, and get body measurements based on keypoints.",
+)
+
+demo.launch()

requirements.txt

@@ -0,0 +1,14 @@
+torch>=1.10
+torchvision
+opencv-python
+numpy
+gradio
+pyyaml
+fvcore
+iopath
+termcolor
+matplotlib
+tqdm
+cloudpickle
+Pillow
+detectron2 @ git+https://github.com/facebookresearch/detectron2.git