add the PR curve plot

README.md

@@ -275,6 +275,19 @@ python yolov5/train.py --imgsz <IMG_SIZE> --batch <BATCH_SIZE> --epochs <NB_EPOC
 ```
 <br />
 
+# Validating the model
+
+```bash
+python yolov5/val.py  --task test --imgsz <IMG_SIZE> --data <custom_data.yaml> --weights yolov5/train/<your_train>/weights/best.pt
+```
+
+To vizualise the Precision / Recall data, use the following command :
+
+```bash
+python plot_PR_curve.py yolov5/runs/val/<your_val> --interactive
+```
+
+
 # Detection
 <br />
 

plot_PR_curve.py

+"""Collect the results and plot the graphics""" 
+
+import os 
+import argparse 
+import numpy as np
+import pandas as pd
+import glob
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+import utils
+
+def main(arguments):
+    """
+    Plot and save the Precision / Recall curve and compute area under the curve (mAP).
+    :param argument (argparse) : Parser containing Path and Directory
+    """
+
+    precision = np.load(glob.glob(os.path.join(arguments.path, 'class_precision.npy'))[0])
+    recall = np.load(glob.glob(os.path.join(arguments.path, 'class_recall.npy'))[0])
+
+    n_class = precision.shape[1]
+    # Load a colormap
+    cmap = mpl.colormaps['tab20']
+
+    # If more class than color in the colormap, switch with dashed and solid lines
+    if n_class > 20:
+        lines = ['dashed','solid'] * round(n_class / 2)
+        colors = cmap(np.linspace(0, 1, n_class))
+    else: 
+        lines = ['solid'] * n_class
+        colors = cmap(np.linspace(0, 1, n_class))
+
+    fig = plt.figure(figsize=([15,8]))
+
+    # Compute the area under the curve for each class and plot in the figure
+    for class_index in range(0, n_class):
+        area_under_curve = np.round(np.trapz(precision[:,class_index], recall), 2)
+
+        plt.plot(recall,precision[:,class_index],
+                 label=f'mAP {class_index} = {area_under_curve}',
+                 color=colors[class_index],
+                 linestyle=lines[class_index])
+    plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05),
+          fancybox=True, ncol=8)
+    plt.tight_layout()
+
+    if arguments.interactive:
+        plt.show()
+    else:
+        plt.savefig(os.path.join(arguments.path, 'PR_curve_all_class.jpg'))
+        print(f'Saved in {arguments.path} as PR_curve_all_class.jpg')
+    return
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter, description='Collect the results and plot the graphics')
+    parser.add_argument('path', type=utils.arg_directory,
+                        help='Path of the folder/file that contains the val / test results')
+    parser.add_argument('--interactive', action='store_const',
+                        help='If in arguments, will show the plot in interactive mode, else will save the plot in path', 
+                        const=1, default=None)
+    args = parser.parse_args()
+
+    main(args)