File size: 4,006 Bytes
bbff197 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 | from transformers import pipeline
import matplotlib.pyplot as plt
from pathlib import Path
from PIL import Image
import argparse
import shutil
import json
import os
# Argument parser
parser = argparse.ArgumentParser(description="Compute image classification")
parser.add_argument("input_path", type=str, help="Path to the input directory")
args = parser.parse_args()
input_path = args.input_path
checkpoint = "openai/clip-vit-large-patch14"
detector = pipeline(model=checkpoint, task="zero-shot-image-classification", use_fast=True)
# Setting up candidate labels for CLIP
candidate_labels = ["Archaeological artifact", "Documentation sheet", "Documentation sheet with archaeological artifact", "Archaeological excavation site", "Outdoor", "Landscape", "Archaeological structure", "Floor"]
# Define output .json file and target path for filtered images
output_file = Path(f"results_pred_dataset_{os.path.basename(input_path)}.json")
target_path = f"filtered_images/{os.path.basename(input_path)}" # Create the dir and subdirs manually
if output_file.exists():
with output_file.open("r") as f:
all_results = json.load(f)
else:
all_results = []
files = sorted([f for f in os.listdir(input_path) if f.endswith(".jpg")])
"""
Classification
"""
fig = plt.figure(figsize=(9,13))
# Helper funtion to create plots
def add_image_subplot(rows, columns, subplot_idx, image, fname, prediction_score, prediction_label):
image = image.convert('RGB')
ax = fig.add_subplot(rows, columns, subplot_idx)
ax.imshow(image)
ax.set_title(f"{fname}\n{prediction_label}\n{prediction_score}", fontsize=9)
ax.axis("off")
columns = 4
rows = 5
subplot_idx = 1
plt_cont = 1
cont = 0
cont_arch = 0
for fname in files:
try:
image_path = os.path.join(input_path, fname)
# Load the image
image = Image.open(image_path)
predictions = detector(image, candidate_labels=candidate_labels)
if subplot_idx == 21:
plt.tight_layout()
plt.savefig(f"Result_Images/{os.path.basename(input_path)}_CLIP_predictions_{(subplot_idx-1)*plt_cont}.png", dpi=300, bbox_inches="tight") # Create the Result_Images folder manually
fig = plt.figure(figsize=(9,13))
plt_cont += 1
subplot_idx = 1
if subplot_idx < 21:
if predictions[0]['label'] == "Archaeological artifact":
add_image_subplot(rows, columns, subplot_idx, image, fname, predictions[0]['score'], predictions[0]['label'])
cont_arch += 1
subplot_idx += 1
results = {
"ground_truth:": str(fname),
"prediction_score:": float(predictions[0]['score']),
"prediction_label:": str(predictions[0]['label'])
}
all_results.append(results)
cont += 1
except:
pass
number_of_images = {
"Original number of images:": int(len(files)),
"Number of images after filtering:": int(cont_arch)
}
all_results.append(number_of_images)
with output_file.open("w") as f:
json.dump(all_results, f, indent=4)
# Save last 20 images
plt.tight_layout()
plt.savefig(f"Result_Images/{os.path.basename(input_path)}_CLIP_predictions_{(subplot_idx-1)*plt_cont}.png", dpi=300, bbox_inches="tight")
print(cont, "images appended to JSON file.")
"""
Move Images
"""
labeled_data = Path(f"results_pred_dataset_{os.path.basename(input_path)}.json")
lst_labeled_data = []
cont = 0
with labeled_data.open("r") as f:
lst_labeled_data = json.load(f)
source_files = sorted([f for f in os.listdir(input_path) if f.endswith(".jpg")])
for i in range(len(lst_labeled_data)):
if lst_labeled_data[i].get('prediction_label:') == "Archaeological artifact":
source_file_path = os.path.join(input_path, lst_labeled_data[i]['ground_truth:'])
cont += 1
shutil.copy2(source_file_path, target_path)
elif not lst_labeled_data[i].get('prediction_label:'):
continue
print("Done!", cont) |