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  1. app.py +169 -0
  2. detection1.pth +3 -0
  3. requirements.txt +8 -0
  4. sam2.1_b.pt +3 -0
  5. training-metrics.json +573 -0
app.py ADDED
@@ -0,0 +1,169 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import math
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+
3
+ import os
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+ os.environ["MPLBACKEND"] = "Agg" # backend sin Qt / sin ventanas
5
+
6
+ import gradio as gr
7
+
8
+ import torch
9
+ import torchvision
10
+ import cv2
11
+ import numpy as np
12
+ from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
13
+ from PIL import Image
14
+ from ultralytics import SAM
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+ #grad camara
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+ from pytorch_grad_cam import EigenGradCAM
17
+ from pytorch_grad_cam.utils.model_targets import FasterRCNNBoxScoreTarget
18
+ from pytorch_grad_cam.utils.image import show_cam_on_image
19
+ device = 'cuda' if torch.cuda.is_available() else 'cpu'
20
+
21
+
22
+ def load():
23
+ state = torch.load("detection1.pth", map_location=device)
24
+ model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
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+ in_feat = model.roi_heads.box_predictor.cls_score.in_features
26
+ model.roi_heads.box_predictor = FastRCNNPredictor(in_channels=in_feat, num_classes=2)
27
+ model.load_state_dict(state)
28
+ model.to(device)
29
+ model.eval()
30
+ return model
31
+
32
+
33
+ GR_MODEL = load()
34
+ SAM_MODEL = SAM('sam2.1_b.pt')
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+
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+ def predict(image, thr=0.5):
37
+ rgb_image = Image.fromarray(image).convert('RGB')
38
+ grad_img_input = np.asarray(rgb_image)
39
+
40
+ cv_image = cv2.cvtColor(np.array(rgb_image), cv2.COLOR_RGB2BGR)
41
+ cv_copy_image = cv_image.copy()
42
+ input = torchvision.transforms.ToTensor()(rgb_image).to(device=device)
43
+ grad_input = input.clone()
44
+ with torch.no_grad():
45
+ predictions = GR_MODEL([input])[0]
46
+
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+ boxes = predictions["boxes"]
48
+ scores = predictions["scores"]
49
+ labels = predictions["labels"]
50
+
51
+ print(scores)
52
+ bounding_boxes = []
53
+ labels_grad = []
54
+
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+ for (box, score, label) in zip(boxes, scores, labels):
56
+ if score < thr:
57
+ continue
58
+
59
+ x1, y1, x2, y2 = map(math.floor, box)
60
+ cv_image = cv2.rectangle(cv_image, (x1, y1),(x2, y2), thickness=3, color=[0.0, 255.0, 0.0, 255.0])
61
+ cv_image = cv2.putText(cv_image, f'Huina {score*100:.2f}% de probabilidad', (x1, y1 - 10),cv2.FONT_HERSHEY_SIMPLEX, 3.0, [0.0, 255.0, 0.0,],3)
62
+ bounding_boxes.append([x1, y1, x2, y2])
63
+ labels_grad.append(int(label))
64
+
65
+ if len(bounding_boxes) > 0:
66
+ with torch.no_grad():
67
+ results = SAM_MODEL(source=cv_copy_image,bboxes=bounding_boxes)
68
+ mask_data = np.zeros((cv_image.shape[0], cv_image.shape[1]))
69
+ for mask in results[0].masks.data:
70
+ mask_data = np.maximum(mask_data, mask.cpu().numpy())
71
+ cv_copy_image = (cv_copy_image * (mask_data)[:, :, np.newaxis]).astype(np.uint8)
72
+
73
+ if len(bounding_boxes) > 0:
74
+ GR_MODEL.train()
75
+
76
+ grad_cam = EigenGradCAM(
77
+ model=GR_MODEL,
78
+ target_layers=[GR_MODEL.backbone.body.layer4[-1]]
79
+ )
80
+ boxes_np = np.array(bounding_boxes, dtype=np.float32)
81
+ labels_np = np.array(labels_grad, dtype=np.int64)
82
+
83
+
84
+ targets = [FasterRCNNBoxScoreTarget(labels=labels_np, bounding_boxes=boxes_np, iou_threshold=0.4)]
85
+ gray_cam = grad_cam(grad_input.unsqueeze(0), targets=targets)[0]
86
+ grad_img = show_cam_on_image(grad_img_input.astype('float32')/255.0, gray_cam, use_rgb=True)
87
+
88
+ return cv2.cvtColor(cv_image, cv2.COLOR_BGR2RGB), cv2.cvtColor(cv_copy_image, cv2.COLOR_RGB2BGR), grad_img
89
+
90
+ import gradio as gr
91
+ import json
92
+
93
+ STATIC_JSON = json.load(open('training-metrics.json', mode='r+'))
94
+ for obj in STATIC_JSON['metrics']:
95
+ if obj.__contains__('AP100'):
96
+ ar100 = obj["AP100"]
97
+ del obj["AP100"]
98
+ obj["AR100"] = ar100
99
+ obj['AP'] = round(obj['AP'], 2)
100
+ obj['AP75'] = round(obj['AP75'], 2)
101
+ obj['AR100'] = round(obj['AR100'], 2)
102
+
103
+ STATIC_JSON['best_score'] = round(STATIC_JSON['best_score'], 2)
104
+
105
+ with gr.Blocks() as demo:
106
+ gr.Markdown("# Detector de Huiñas en cámaras trampa")
107
+ gr.Markdown("""
108
+ **Detección de Huiñas con FAST R-CNN y PyTorch**
109
+ - Épocas de entrenamiento: 80
110
+ - Número de clases: 1
111
+ - Label: Huiña
112
+ - Métrica: AP@0.5 * 0.5 + AP@0.75 * 0.25 + AR@100 * 0.25
113
+ - Train: 85% (398 imágenes)
114
+ - Val: 10% (46 imágenes)
115
+ - Test: 5% (24 imágenes)
116
+ - Detección de gradiente con EigenGradCam
117
+ - Segmentación de máscara con SAM (Segment Anything Model)
118
+ """)
119
+ with gr.Row():
120
+ with gr.Column(scale=1):
121
+ inp_img = gr.Image(type="numpy", label="Imagen")
122
+ thr = gr.Slider(0.1, 0.99, value=0.5, step=0.01, label="Threshold")
123
+
124
+ with gr.Row():
125
+ btn_submit = gr.Button("Submit", variant="primary")
126
+ btn_clear = gr.Button("Clear")
127
+
128
+ # JSON fijo debajo de los botones
129
+ gr.JSON(value=STATIC_JSON, label="Información de entrenamiento")
130
+
131
+ with gr.Column(scale=1):
132
+ out_bbox = gr.Image(type="numpy", label="Detección de bounding box")
133
+ out_sam = gr.Image(type="numpy", label="Segmentation Anything (SAM)")
134
+ out_cam = gr.Image(type="numpy", label="Eigen/Grad-CAM")
135
+
136
+ gr.Examples(
137
+ examples=[
138
+ ["https://i.postimg.cc/G8wjmq2N/ex1.jpg", 0.50],
139
+ ["https://i.postimg.cc/CdCczTbf/ex5.jpg", 0.70],
140
+ ["https://i.postimg.cc/k2Gs12zK/ex6.jpg", 0.70],
141
+ ["https://i.postimg.cc/p5PYDrKz/ex3.jpg", 0.70],
142
+ ["https://i.postimg.cc/bd02h6pP/ex4.jpg", 0.70],
143
+ ["https://i.postimg.cc/LYt3YLgZ/ex2.jpg", 0.70],
144
+
145
+ ],
146
+ inputs=[inp_img, thr],
147
+ fn=predict,
148
+ outputs=[out_bbox, out_sam, out_cam],
149
+ cache_examples=True,
150
+ label="Ejemplos",
151
+ )
152
+
153
+ btn_submit.click(
154
+ fn=predict,
155
+ inputs=[inp_img, thr],
156
+ outputs=[out_bbox, out_sam, out_cam],
157
+ )
158
+
159
+ def _clear():
160
+ return None, 0.5, None, None, None
161
+
162
+ btn_clear.click(
163
+ fn=_clear,
164
+ inputs=[],
165
+ outputs=[inp_img, thr, out_bbox, out_sam, out_cam],
166
+ )
167
+
168
+ demo.launch()
169
+
detection1.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:b11cf00cf8651fbf2c82156403c20cf7f57593f948832b9ffba0737c1237da86
3
+ size 165728267
requirements.txt ADDED
@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
 
1
+ gradio
2
+ numpy
3
+ opencv-python-headless
4
+ torch
5
+ torchvision
6
+ pillow
7
+ ultralytics
8
+ grad-cam
sam2.1_b.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:f1a9cf2dd69d84bb463b5ad98246d03e2d47a130a9295db0ec967e6cd95e2e47
3
+ size 161935802
training-metrics.json ADDED
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