block22.python

# Plot faces extracted images
def plot_faces_boxes(df, max_cols = 2, max_rows = 6, fsize=(24, 5), max_items=12):    
    idx = 0    
    for item_idx, item in df.iterrows():
        img = cv2.cvtColor(cv2.imread(IMAGES_FOLDER_TRAIN + "/" + item["filename"][:-4] +"/" + item["image"], cv2.IMREAD_UNCHANGED), cv2.COLOR_BGR2RGB)    
        face_img = img #.copy()
        # grid subplots
        row = idx // max_cols
        col = idx % max_cols
        if col == 0: fig = plt.figure(figsize=fsize)
        ax = fig.add_subplot(1, max_cols, col + 1)
        ax.axis("off")
        # display image with boxes
        cols = [c for c in df.columns if "boxes" in c]
        for i, c in enumerate(cols, 0):
            face_locations = item[c]
            face_confidence = item[c]            
            if len(face_locations) > 0:
                for face_location in face_locations:        
                    ((x,y,w,h), confidence) = face_location
                    # face_img = face_img[y:y+h, x:x+w]
                    cv2.rectangle(face_img, (x, y), (x+w, y+h), (255,i*255,0), 8)
                    cv2.putText(face_img, '%.1f' % (confidence*100.0), (x+w, y+h), cv2.FONT_HERSHEY_SIMPLEX, 2.0, (255,i*255,0), 9, cv2.LINE_AA)
                ax.imshow(face_img)
            else:
                ax.imshow(img)
            ax.set_title("%s %s / %s - Faces: %d %s %s" % (item["label"] if "label" in df.columns else "", 
                                                           item["filename"], item["image"],
                                                           item["faces_cv2"] if "faces_cv2" in df.columns else len(face_locations),
                                                           item["faces_cv2_median"] if "faces_cv2_median" in df.columns else "",
                                                           item["faces"] if "faces" in df.columns else ""))
        if (col == max_cols -1): plt.show()
        idx = idx + 1
        if (max_items > 0 and idx >=max_items): break