!pip install -U aicrowd-cli==0.1 > /dev/null

!pip install numpy pandas

import pandas as pd
import numpy as np
import os

API_KEY = "6d9b67915c94a7b780aef947bb84bd93"
!aicrowd login --api-key $API_KEY

!aicrowd dataset download --challenge mabe-task-1-classical-classification

!rm -rf data
!mkdir data
 
!mv train.npy data/train.npy
!mv test-release.npy data/test.npy
!mv sample-submission.npy data/sample_submission.npy

train = np.load('data/train.npy',allow_pickle=True).item()
test = np.load('data/test.npy',allow_pickle=True).item()
sample_submission = np.load('data/sample_submission.npy',allow_pickle=True).item()

print("Dataset keys - ", train.keys())
print("Vocabulary - ", train['vocabulary'])
print("Number of train Sequences - ", len(train['sequences']))
print("Number of test Sequences - ", len(test['sequences']))

sequence_names = list(train["sequences"].keys())
sequence_key = sequence_names[0]
single_sequence = train["sequences"][sequence_key]
print("Sequence name - ", sequence_key)
print("Single Sequence keys ", single_sequence.keys())
print(f"Number of Frames in {sequence_key} - ", len(single_sequence['annotations']))
print(f"Keypoints data shape of {sequence_key} - ", single_sequence['keypoints'].shape)
print(f"annotator_id of {sequence_key} - ", single_sequence['annotator_id'])

import matplotlib.pyplot as plt
from matplotlib import animation
from matplotlib import colors
from matplotlib import rc
 
rc('animation', html='jshtml')
 
# Note: Image processing may be slow if too many frames are animated.                
 
#Plotting constants
FRAME_WIDTH_TOP = 1024
FRAME_HEIGHT_TOP = 570
 
RESIDENT_COLOR = 'lawngreen'
INTRUDER_COLOR = 'skyblue'
 
PLOT_MOUSE_START_END = [(0, 1), (0, 2), (1, 3), (2, 3), (3, 4),
                        (3, 5), (4, 6), (5, 6), (1, 2)]
 
class_to_color = {'other': 'white', 'attack' : 'red', 'mount' : 'green',
                  'investigation': 'orange'}
 
class_to_number = {s: i for i, s in enumerate(train['vocabulary'])}
 
number_to_class = {i: s for i, s in enumerate(train['vocabulary'])}
 
def num_to_text(anno_list):
  return np.vectorize(number_to_class.get)(anno_list)
 
def set_figax():
    fig = plt.figure(figsize=(6, 4))
 
    img = np.zeros((FRAME_HEIGHT_TOP, FRAME_WIDTH_TOP, 3))
 
    ax = fig.add_subplot(111)
    ax.imshow(img)
 
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)
 
    return fig, ax
 
def plot_mouse(ax, pose, color):
    # Draw each keypoint
    for j in range(7):
        ax.plot(pose[j, 0], pose[j, 1], 'o', color=color, markersize=5)
 
    # Draw a line for each point pair to form the shape of the mouse
 
    for pair in PLOT_MOUSE_START_END:
        line_to_plot = pose[pair, :]
        ax.plot(line_to_plot[:, 0], line_to_plot[
                :, 1], color=color, linewidth=1)
 
def animate_pose_sequence(video_name, keypoint_sequence, start_frame = 0, stop_frame = 100, 
                          annotation_sequence = None):
    # Returns the animation of the keypoint sequence between start frame
    # and stop frame. Optionally can display annotations.
    seq = keypoint_sequence.transpose((0,1,3,2))
 
    image_list = []
    
    counter = 0
    for j in range(start_frame, stop_frame):
        if counter%20 == 0:
          print("Processing frame ", j)
        fig, ax = set_figax()
        plot_mouse(ax, seq[j, 0, :, :], color=RESIDENT_COLOR)
        plot_mouse(ax, seq[j, 1, :, :], color=INTRUDER_COLOR)
        
        if annotation_sequence is not None:
          annot = annotation_sequence[j]
          annot = number_to_class[annot]
          plt.text(50, -20, annot, fontsize = 16, 
                   bbox=dict(facecolor=class_to_color[annot], alpha=0.5))
 
        ax.set_title(
            video_name + '\n frame {:03d}.png'.format(j))
 
        ax.axis('off')
        fig.tight_layout(pad=0)
        ax.margins(0)
 
        fig.canvas.draw()
        image_from_plot = np.frombuffer(fig.canvas.tostring_rgb(),
                                        dtype=np.uint8)
        image_from_plot = image_from_plot.reshape(
            fig.canvas.get_width_height()[::-1] + (3,)) 
 
        image_list.append(image_from_plot)
 
        plt.close()
        counter = counter + 1
 
    # Plot animation.
    fig = plt.figure()
    plt.axis('off')
    im = plt.imshow(image_list[0])
 
    def animate(k):
        im.set_array(image_list[k])
        return im,
    ani = animation.FuncAnimation(fig, animate, frames=len(image_list), blit=True)
    return ani
 
def plot_annotation_strip(annotation_sequence, start_frame = 0, stop_frame = 100, title="Behavior Labels"):
  # Plot annotations as a annotation strip.
 
  # Map annotations to a number.
  annotation_num = []
  for item in annotation_sequence[start_frame:stop_frame]:
    annotation_num.append(class_to_number[item])
 
  all_classes = list(set(annotation_sequence[start_frame:stop_frame]))
 
  cmap = colors.ListedColormap(['red', 'orange', 'green', 'white'])
  bounds=[-0.5,0.5,1.5, 2.5, 3.5]
  norm = colors.BoundaryNorm(bounds, cmap.N)
 
  height = 200
  arr_to_plot = np.repeat(np.array(annotation_num)[:,np.newaxis].transpose(),
                                                  height, axis = 0)
  
  fig, ax = plt.subplots(figsize = (16, 3))
  ax.imshow(arr_to_plot, interpolation = 'none',cmap=cmap, norm=norm)
 
  ax.set_yticks([])
  ax.set_xlabel('Frame Number')
  plt.title(title)
 
  import matplotlib.patches as mpatches
 
  legend_patches = []
  for item in all_classes:
    legend_patches.append(mpatches.Patch(color=class_to_color[item], label=item))
 
  plt.legend(handles=legend_patches,loc='center left', bbox_to_anchor=(1, 0.5))
 
  plt.tight_layout()

keypoint_sequence = single_sequence['keypoints']
annotation_sequence = single_sequence['annotations']

ani = animate_pose_sequence(sequence_key,
                            keypoint_sequence, 
                            start_frame = 3000,
                            stop_frame = 3100,
                            annotation_sequence = annotation_sequence)

# Display the animaion on colab
ani

annotation_sequence = single_sequence['annotations']
text_sequence = num_to_text(annotation_sequence)
 
plot_annotation_strip(
    text_sequence,
    start_frame=0,
    stop_frame=len(annotation_sequence) + 1000
)

vocabulary = train['vocabulary']
def get_percentage(sequence_key):
  anno_seq = num_to_text(train['sequences'][sequence_key]['annotations'])
  counts = {k: np.mean(np.array(anno_seq) == k) for k in vocabulary}
  return counts

anno_percentages = {k: get_percentage(k) for k in train['sequences']}

anno_perc_df = pd.DataFrame(anno_percentages).T
print("Percentage of frames in every sequence for every class")
anno_perc_df.head()

all_annotations = []
for sk in train['sequences']:
  anno = train['sequences'][sk]['annotations']
  all_annotations.extend(list(anno))
all_annotations = num_to_text(all_annotations)
classes, counts = np.unique(all_annotations, return_counts=True)
pd.DataFrame({"Behavior": classes,
              "Percentage Frames": counts/len(all_annotations)})

# Generating Random Predictions
submission = {}
test = np.load('data/test.npy',allow_pickle=True).item()
for sequence_id, sequence in test["sequences"].items():
  keypoint_sequence = sequence['keypoints']
  submission[sequence_id] = np.random.randint(4, size=len(sequence['keypoints']))

def validate_submission(submission, sample_submission):
    if not isinstance(submission, dict):
      print("Submission should be dict")
      return False

    if not submission.keys() == sample_submission.keys():
      print("Submission keys don't match")
      return False
    
    for key in submission:
      sv = submission[key]
      ssv = sample_submission[key]
      if not len(sv) == len(ssv):
        print(f"Submission lengths of {key} doesn't match")
        return False
    
    for key, sv in submission.items():
      if not all(isinstance(x, (np.int32, np.int64, int)) for x in list(sv)):
        print(f"Submission of {key} is not all integers")
        return False
    
    print("All tests passed")
    return True

validate_submission(submission, sample_submission)

np.save("submission.npy", submission)

!aicrowd submission create -c mabe-task-1-classical-classification -f submission.npy